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Earth Observing of Years 20 → ERS MISSIONS

SP-1326 → 20 YEARS OF ERS MISSIONS An ESA Communications Production Communications An ESA Agency Space © 2013 European Copyright ESA Member States Member ESA Austria Belgium Republic Czech Finland France Greece Ireland Italy Luxembourg Netherlands Poland Portugal Romania Sweden Switzerland Kingdom United ESA_ERS_0_COVER_DEF.indd 1

SP-1326 July 2013

→ ERS MISSIONS 20 Years of Observing Cover: Synthetic Aperture Radar multitemporal colour composite image showing Naples and its bay. Naples, the largest city in southern Italy and capital of the Campania Region, can be seen as the large bright area on the northern tip of the Bay of Naples. The bay lies within an important volcanic province, with the volcano Mount Vesuvius and the Phlegrean Fields (Campi Flegrei) area being its active and ancient primary testimonies. The different colours shown by the sea in the bay indicate sea surface roughness caused by the which occurred during the dates of acquisition. The colour patches to the north of Naples represent crop . The image is made from three ERS-2 SAR Precision Radar Image images acquired on different dates. A different RGB colour is assigned to each date of acquisition: Red: 11 February 2004, Green: 21 April 2004, Blue: 30 June 2004. (ESA)

An ESA Communications Production Publication ERS Missions: 20 Years of Observing Earth (ESA SP-1326, July 2013) Production Editor K. Fletcher Editing/Layout Contactivity bv, Leiden, the Netherlands Publisher ESA Communications ESTEC, PO Box 299, 2200 AG Noordwijk, the Netherlands Tel: +31 71 565 3408 Fax: +31 71 565 5433 www.esa.int

ISBN 978-92-9221-424-1 ISSN 0379-6566 Copyright © 2013 European Space Agency Foreword

Foreword

Scientific and commercial was still in its early stage when the first European Remote Sensing satellite, ERS‑1, was launched in 1991, leading to the outstanding achievements described in this book. ERS‑1 was the most advanced and complex satellite of its day, carrying three instruments (ERS‑2 had four instruments) and delivering what was at the time an enormous volume of data to Earth. Ensuring the timely delivery of products was a great challenge because of the low computing capacities and the weak performance of telephone lines featuring ‘modern ISDN’ technology. It should be considered that at the time, information technology for digital data processing was very expensive and the majority of products were disseminated using postal services. Consequently, the ERS programme was built with exploitation goals based on the available ‘ground’ technology of the late 1980s targeting specific user communities. Concomitant with the development and launch of ERS‑1 a revolution took place in computer and information technology. Computers and workstations doubled in speed and capacity every year, and the World Wide Web took off, boosting internet utilisation, supporting easy access to ERS data and bringing computing costs down to a mass market level. The mission operations benefited strongly from this technological revolution, removing processing limitations and bottlenecks in accessing data and making them freely available online. Earth observation applications based on space data also profited from these developments as they could suddenly be processed on the desks of users instead of only in computer centres. Throughout the 20 years of ERS, special emphasis has been given to the calibration of instruments, validation of products, cross-calibration of instrumentation, information retrieval evolution, and to the documentation of these processes. This has been a joint effort with specialised labs, user communities and partner space agencies, keeping the mission an innovative asset for research and services. By enabling the dissemination of high-quality data and building on continuous technological progress driven by the computer and internet revolution, the ‘free and open’ data policy has been the ultimate accelerator in ensuring the full exploitation of ERS data. Allowing an ever-growing community to have easy and free access to satellite data enormously facilitated the transformation of these unique data into valuable information, far beyond the original scope of the programme. The outstanding robustness of both satellites and instrumentation has facilitated long time series of data overlapping with successor instrumentation flown on ERS‑2, and MetOp, enabling the creation of ‘one mission’ across several platforms. The environment of high-quality data being available easily to all users has been the essence of an outstanding return on investment for ESA stakeholders, and has also led to new national satellites and the Global Monitoring for Environment and Security (GMES) programme, recently renamed Copernicus. ERS has served as a precursor mission in Europe for acquiring key industrial expertise in areas like radar technology and scientific application fields like interferometry. There are thousands of projects throughout the world relying on ERS data. This book describes the exceptional success of a pioneering satellite, starting in a different technological era and founding communities that have significantly advanced our knowledge of planet Earth, while bringing research to operational services and business. The Sentinels will carry on this heritage, making remote sensing a commodity for all citizens.

Volker Liebig Director of Earth Observation Programmes

iii

Contents

Contents

Introduction 1

History of the ERS Programme ...... 7 G. Duchossois, G. Kohlhammer & W. Lengert

The ATSR Instruments and What They Have Led To ...... 45 D.T. Llewellyn-Jones

ERS and Atmospheric Composition Measurements: GOME and the SCIAMACHY Project . . 63 J.P. Burrows

The ERS : Achievements and the Future 143 A. Stoffelen & W. Wagner

The ERS SAR Wave Mode: A Breakthrough in Global Ocean Wave Observations 165 K. Hasselmann, B. Chapron, L. Aouf, F. Ardhuin, F. Collard, G. Engen, S. Hasselmann, P. Heimbach, P. Janssen, H. Johnsen, H. Krogstad, S. Lehner, J.-G. Li, X.-M. Li, W. Rosenthal & J. Schulz-Stellenfleth

Satellite Oceanography from the ERS Synthetic Aperture Radar and Radar Altimeter: 199 A Brief Review J.A. Johannessen, B. Chapron, W. Alpers, F. Collard, P. Cipollini, A. Liu, J. Horstmann, J. da Silva, M. Portabella, I.S. Robinson, B. Holt, C. Wackerman & P. Vachon

New Views of Earth from ERS Satellite Altimetry ...... 225 O.B. Andersen, P. Knudsen, P. Berry, R. Smith, F. Rémy, T. Flament, S. Calmant, P. Cipollini, S. Laxon, A. Shephard, D. McAdoo, R. Scharroo, W. Smith, D. Sandwell, P. Schaeffer, J. Bamber, C.K. Shum, Y. Yi & J. Benveniste

20 Years of ERS Interferometry 255 F. Rocca

ERS SAR over Land 273 T. Le Toan

Acronyms and Abbreviations 291

v

→→Introduction

Introduction

Introduction

The first European Remote Sensing (ERS‑1) satellite was launched in July 1991 and was followed by ERS‑2 in April 1995. Carrying suites of sophisticated instruments to study the complexities of the atmosphere, land, oceans and polar caps, these two missions were the most advanced of their time. Over 20 years the two satellites delivered a continuous stream of data and information that changed our view of the world and placed Europe firmly at the forefront of Earth observation. In September 2011 ESA organised a scientific workshop to celebrate the outstanding achievements of the ERS mission after 20 years of successful operations. The main themes of the discussions are captured in each of the chapters of this publication. Comprising contributions from key scientists and their colleagues involved in the mission exploitation, this publication highlights the outstanding scientific achievements unique to the ERS mission. The authors also provide their personal perspectives on the mission, addressing the human angle of the mission preparation and of its scientific exploitation, as well as recalling successes and wider impacts resulting from the unique achievements of the ERS mission and its exploitation. The first chapter deals with the history of ERS from its conception in the 1970s, through the hectic days of the launches, the experiments that paved the way for new research, the adaptations of ESA data policy and the deorbiting phase. In particular it recalls the proactive role of ESA mission management for the provision of data to the science community, the deployment of a worldwide network of ground stations and early steps in international cooperation, as well as the support to campaigns like the first ever ERS‑1/2 tandem campaign and pilot projects for precursor applications demonstration. The second chapter focuses on the Along-Track Scanning Radiometer instruments, ATSR and ATSR-2, which were capable of measuring land and sea surface temperatures with unprecedented accuracy. The first measurements from ATSR proved to be better than actual buoy measurements. The scientific exploitation results extended to global land surface temperatures and or aerosol measurements. ESA initiated an innovative exploitation of the ATSR infrared channel properties to produce a regularly updated world fire atlas. Thanks to the ERS ATSR/ATSR-2 instrument data, we have a unique and continuous set of Sea Surface Temperature measurements going back to 1991, which is used today in research. The third chapter discusses the history of the development of the Global Ozone Monitoring Experiment (GOME), including exploitation results. GOME was the only new instrument on ERS‑2 as compared to the ERS‑1 payload, adding monitoring capabilities. Having a high spectral resolution and broad-wavelength coverage, GOME measured ozone, but also known ozone-depleting substances such as chlorine and bromine, and aerosols, and chemically reactive factors of air quality such as nitrogen and sulphur dioxide. Based on the success of GOME, follow-on instruments such as GOME-2 on MetOp and the Ozone Monitoring Instrument (OMI) on the satellite have been realised, which are extending the provision of long-term ozone data for the World Meteorological Organization’s Scientific Assessment of . In addition, GOME has enabled the development of air-quality applications based on satellite data. The fourth chapter illustrates the contribution to science and operations over land, sea and ice by observations made with the ERS Active Microwave Instrument (AMI) scatterometer mode. The ERS scatterometer measures the speed and direction of winds over the surface of the ocean. This information is used for operational weather and wave forecasting, as well as climate research. Other applications of ERS scatterometer backscatter measurements include sea ice age, extent and melt. Over land the scatterometer is being used

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operationally to retrieve soil moisture and soil water index globally. Follow-on instruments are now carried on the European operational MetOp satellites operated by Eumetsat. The impact of the information provided by the ERS AMI wave mode on oceanography, meteorology and climate research is discussed in the fifth chapter. This chapter describes the motivation for incorporating the Synthetic Aperture Radar (SAR) wave mode in the ERS‑1 instrument, in particular thanks to its capability to image globally ocean waves and to the development of numerical two-dimensional wave spectral models. The SAR wave mode has become an established component of operational wave prediction and has been continued on follow-on satellites such as Envisat and Sentinel-1. Finally, the recent cross-spectral analysis of SAR wave data has allowed major improvements in wave product inversion and subsequently a new application on swell tracking has been demonstrated for operational use. The sixth chapter reports some of the advances in studies of the upper ocean, its monitoring and understanding with the use of ERS AMI Image Mode and Radar Altimeter. SAR imaging over the ocean has enabled significant advances in physical oceanography such as the observation of mesoscale fronts and eddies, monitoring and understanding of internal waves, the detection of oil spills and the measuring of speed. This period saw the development of the first operational services in near-realtime, such as oil spill monitoring and ship detection in the Norwegian Sea. The ERS altimetric mission objective was to map global ocean wave heights, roughness, ocean circulation and associated transfers of energy including ocean–atmosphere interactions. With its 35-day repeat and therefore denser ground tracks, the ERS mission was an indispensable complement to the NASA–CNES Topex–Poseidon altimetric mission, bringing high-resolution mesoscale variability into the picture. The seventh chapter illustrates the contribution of the Radar Altimeter to geodetic research, ice and sea-ice studies, improvements in Digital Elevation Models (DEMs), coastal oceanography, and river and lake hydrology. While ERS‑2 was in the final stage of development, the scientific community requested that ERS‑1 be flown in a very dense orbit. The so-called geodetic phase, which lasted from April 1994 to March 1995, made it possible to map the mean sea surface and derive the marine geoid and seafloor bathymetry at unprecedented resolution and accuracy. The ERS‑1 geodetic phase data acquired over land were used to correct existing DEMs and led to the release of a new global and accurate DEM called Altimetry Corrected Elevation (ACE). A new application emerged, based on the capability to extract river and lake levels globally and in particular in regions that are difficult access and poorly monitored. Ice sheet topography was a key target of the ERS mission and was mapped with unprecedented coverage and accuracy. Finally, the level of the sea ice above the water level could be estimated and sea surface topography measurements extended in the coastal zone. The eighth chapter explains the steps that have led to the birth of modern SAR interferometry. Although not known at the time, and therefore not specified in the ERS‑1 imaging radar development, scientists discovered that it was possible to exploit the phase difference in radar pixels from consecutive overpasses, which could be directly related to terrain height. ESA teams developed a new processor to generate a new phase-preserved product, organised a fringe group of science users, distributed new products widely and set up proof-of-concept validation campaigns. Today, SAR interferometry is used in most imaging radar applications such as land cover classification, DEMs, surface deformation, earthquake and volcano monitoring and ice flow velocity measurements. In addition, the property of the radar over specific point targets to preserve the phase information allowed measurements of local subsidence of a few millimetres. Value-adding companies processed entire datasets from the ERS archive and generated subsidence maps at regional and national scales.

4 Introduction

In the ninth chapter the authors highlight the value of the ERS SAR compared with optical imaging. SAR can image in all weather conditions, irrespective of day or night, and is particularly useful for agriculture/forest monitoring and snow and ice studies in the polar regions. In addition, the ERS SAR demonstrated particular sensitivity to surface roughness, allowing geological or topography mapping. Also sensitive to dielectric properties, it allowed for soil moisture or biomass mapping. New applications such as rice crop mapping and tropical forest monitoring were also developed using multitemporal ERS imagery. Finally, combining radar interferometry and the measurement of radar reflectivity allowed for biomass retrieval at regional scales, such as the boreal forest Siberia project or the Chinese forest under the Dragon cooperation. In conclusion, we invite readers to evaluate whether ERS has fulfilled the objective set at the origin of the mission: ‘ERS‑1 will be the European Space Agency's first satellite devoted entirely to remote sensing from a . It will be a forerunner of a new generation of space missions planned for the 1990s which will make a substantial contribution to the scientific study of our environment.’ Our opinion is a clear YES! The contributions of the ERS missions to science, applications and operational services is well illustrated by the more than 2000 scientific papers exploiting the ERS data that have been published so far. The ERS archive is freely and openly available for further exploitation – from science to climate research – thus ensuring that the success story will continue. We wish you all pleasant reading and look forward to the opportunity to meet you at the next ESA Living Planet Symposium.

The organisers of the scientific workshop '20 years of ERS missions'

5

→→History of the ERS Programme

History of ERS

Historyf o the ERS Programme

G. Duchossois1, G. Kohlhammer2 & W. Lengert3

1 ESA, ERS Mission Manager (1984–1994) 2 ESA, ERS Mission Manager (1994–2001) 3 ESA, ERS Mission Manager (2001–present)

1.e Th Emergence of Earth Observation at ESRO/ESA in the 1970s In 1971 a group of European experts from eight ESRO Member States was invited by NASA to participate in an international colloquium on remote sensing (3−14 May 1971) and in the 7th ERIM symposium at the Environmental Research Institute of Michigan in Ann Arbor (17−24 May 1971). In their mission report to the ESRO Director General, the group defined the requirements for a European remote sensing programme and formulated a number of recommendations on the possible roles of ESRO, including:

—— coordinating national activities at various levels in the ESRO Member States; —— developing and implementing airborne remote sensing facilities in Europe based on existing national capacities; —— developing European reception facilities for the acquisition of data from the Earth Resources Technology Satellite (ERTS-A) being developed by NASA and planned for launch in 1972, and possible follow-on missions; —— preparing and delivering information to the still embryonic community of civilian users in Europe at that time (most existing expertise was in the domain of military applications); and —— defining and preparing a European space remote sensing programme focusing on resources management, environmental monitoring and the delivery of assistance to developing/emerging countries.

During the period 1972−79 ESRO/ESA carried out intensive exploratory activities, including consultations with experts/advisory groups in existing and potential areas of application, while European industry conducted studies focusing on:

—— candidate space-borne remote sensing instrumentation, focusing on passive microwave (Passive Microwave Radiometer Satellite, PAMIRASAT, in 1974) and active microwave techniques (Synthetic Aperture Radar Satellite, SARsat, in 1973; the Synthetic Aperture Radar on Spacelab, SARLab, in 1974; followed by two dedicated SAR Phase A studies performed by Thomson-CSF and Marconi Research Labs in 1977); —— an inventory of European airborne facilities with studies performed by the Société d’Études Techniques et d’Entreprises Générales (SODETEG) in association with the French Institut Géographique National and Bureau de Recherches Géologiques et Minières (IGN/BRGM), and EASAMS in association with Fairey Air Survey, UK; and —— the possible use of Spacelab as an intermediate step prior to the development of free-flying remote sensing satellites (references to the post-Apollo group of experts, the 1973 symposium on the use of Spacelab, the ESRO 1973 Summer School in Nottingham, UK, etc.).

On the basis of the results of these consultations and industry studies, bodies such as the Remote Sensing Ad Hoc Group (RSAHG) and its successor, the

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Remote Sensing Programme Advisory Group (RESPAG), identified three main priorities and possible lines of action:

—— In the short term (mid-1970s), the creation of a European network for the acquisition, preprocessing, archiving and distribution of data from US/NASA satellites such as Landsat, the Heat Capacity Mapping Mission (HCMM), Nimbus-G and , based on existing national capacities. To this end, the EarthNet programme was approved in 1977 and implemented based on the use and upgrading of several European receiving stations (Fucino, Kiruna, Oakhanger, Maspalomas, Lannion, etc.) and of the ESRIN centre for the management of the programme. The creation of the European Association of Remote Sensing Laboratories (EARSeL) in 1976 (chaired by Professor Preben Gudmandsen of the Technical University of Denmark at Lyngby), under the auspices of the European Commission, ESA and the Council of Europe, was also a major step in the development of the community of scientific users in Europe. Today, EARSeL members include more than 200 research institutes, laboratories and universities in Western and , the Middle East and North Africa.

—— In the medium term (early 1980s), the use of Spacelab for the testing and demonstration, as a priority, of high-resolution optical imaging sensors and of all-weather remote sensing techniques. Germany proposed two experiments for the First Spacelab Flight Payload (FSLP): the Metric Camera, a modified Zeiss RMK A30/23 airborne camera, for cartographic applications and the production of maps at scales down to 1/50 000, and the Microwave Remote Sensing Experiment (MRSE) combining several modes of operation (SAR, Wind Scatterometer and passive radiometer modes). The FSLP was flown on 28 November 1983; the Metric Camera was a full success but unfortunately the MRSE SAR and Scatterometer modes failed.

—— In the longer term (late 1980s/early 1990s), the preparation of a European space satellite programme focusing on all-weather techniques to account for cloud cover conditions over northern Europe as well as day and night operation capability. Particular attention was paid to the development of a complex Synthetic Aperture Radar (SAR) instrument for which, following numerous consultations and studies with European and American expert groups, the C‑band frequency was preferred to the X- or L-bands.

2.eG Th enesis of ERS‑1: Mission Objectives and Payload

Industry studies performed in the late 1970s concentrated on the definition of candidate satellite missions that would focus on land (Land Application Satellite System, LASS), the open ocean (Global Ocean Monitoring Satellite System, GOMSS) and coastal zones (Coastal Ocean Monitoring Satellite System, COMSS), all of which included SAR instrumentation. These studies highlighted the need for a Remote Sensing Preparatory Programme (RSPP) to develop the critical technologies and techniques required for such ambitious projects (optical and microwave technologies, very high data rate transmission, ground data processing algorithms, fast processors, etc.). ESA Member States (and Canada through a Cooperation Agreement) approved the RSPP in March 1979 for an initial period of two years, which was later extended to the end of 1981. From consultations with the advisory groups, it appeared that most of the requirements for GOMSS could be covered by the COMSS concept, so that, as a consequence, only the LASS and COMSS Phase A studies proceeded on the basis of instrument payloads comprising SAR and an Optical Imaging Instrument (OII) for LASS, and an Ocean Colour Monitor (OCM) and a passive Imaging Microwave Radiometer (IMR) for COMSS.

10 History of ERS

An important element in the decision process for the ESA Remote Sensing Satellite Programme was the launch of Seasat in July 1978, which provided, in spite of its short lifetime (106 days), large amounts of data of great interest to the global oceanographic community. The Seasat results encouraged consideration of alternative COMSS payloads, and follow-on trade-off studies were performed in 1980 to consider the inclusion of Radar Altimeter, Wind Scatterometer and Earth radiation budget instruments. A high-level advisory committee of European experts was set up in early 1981, chaired by John Houghton (Director of the Rutherford and Appleton Laboratories). It was charged (by Eric Quistgaard, Director General of ESA) with reviewing the mission objectives and proposing a sound payload configuration for the new mission concept, called ERS‑1, resulting from the merger of LASS and COMSS as it appeared that a single mission could cover most land, ice and ocean objectives (see Table 1). It is important to recall that the initial mission objectives defined in the 1970s had emphasised commercial and operational exploitation and applications of remote sensing. However, a gradual change occurred with the emergence of global climate and ocean monitoring programmes (such as the World Climate Research Programme, the World Ocean Circulation Experiment and the Tropical Ocean Global Atmosphere programme) to respond to the growing concerns of the public, decision makers and politicians about climate variability processes and possible human interactions and contributions, which required the development of an improved scientific understanding of the climate system. This rebalancing of requirements from commercially oriented applications to scientific research became effective in the early 1980s when the ERS‑1 mission configuration and objectives were finalised as follows:

—T— o increase scientific understanding of coastal zones, global ocean and polar region processes and to provide support for government policies related to the environment and .

—T— o develop and promote economic and commercial applications considering ERS‑1 as a demonstration mission and a ‘market opener’ for downstream/ value-added industries.

—T— o explore the potential of radar data for studies of land processes and applications.

Table 1. Comparison of ERS‑1 and Seasat payloads (ESA, 1997b).

Observation targets Seasat ERS‑1 All-weather high-resolution imagery over SAR L-band, HH polarisation SAR C-band, VV polarisation land and ice and global ocean wave spectra Global ocean surface wind speed and Microwave scatterometer Microwave scatterometer direction (Ku-band) (C-Band) Global sea surface temperatures Scanning Multichannel Microwave Radiometer Along-Track Scanning Radiometer (ATSR) (SMMR) Global observations of land and water Visible and infrared radiometer ATSR features and identification of clouds Global ocean wave heights and sea surface Radar altimeter Radar altimeter topography (Ku-band) (Ku-band) Global tropospheric water vapour content Combined use of SMMR and Visible Infrared Dual-frequency microwave sounder Radiometer Precise orbit determination Laser Retroreflector array Precise Range and Range Rate Equipment (PRARE) and Laser Retroreflector array

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Figure 1. Evolution of the ERS payload during Phase A. (ESA, 1997b; R. Francis)

In view of the technical payload accommodation constraints imposed by the use of the multi-mission Système pour l’Observation de la Terre (SPOT) platform (proposed by France as a ‘contribution in kind’), the financial implications and the scientific merits of the various payload options, the advisory committee proposed a payload configuration that no longer included an OCM and an IMR, but consisted of:

—— An Active Microwave Instrument (AMI) including SAR and Wind Scatterometer modes, both operating in the C-band (5.3 GHz) and using common timesharing electronic hardware. The SAR mode was also designed to operate as a Wave Scatterometer over the open ocean (SAR Wave mode) and as a high-resolution imaging radar (SAR imaging mode) over land, coastal zones and ice.

—— A Radar Altimeter (RA) operating in the Ku-band.

To take advantage of the onboard capacities made available with the elimination of the OCM and IMR, ESA released an Announcement of Opportunity (AO), from which three instruments were selected. These three nationally funded instruments included the Along-Track Scanning Radiometer (ATSR) provided by the UK and Australia, the Microwave Radiometer (MWR) provided by France, and the Precise Range and Range Rate and Experiment (PRARE) provided by Germany. This configuration was adopted by ESA Member States in 1981 as the basis for the forthcoming industrial development activities. Also in 1981, a major airborne campaign, using a Convair 580 provided by the Canada Centre for Remote Sensing (CCRS), equipped with multi-frequency and multi-polarisation SARs, was conducted in cooperation with the European Commission’s Joint Research Council (JRC) over some 52 test sites across Europe, from Sweden to Spain and Italy. The data collected were made available to the European scientific community to promote their understanding the potential of SAR data for various land, ice and ocean applications. Figure 1 shows the evolution of the ERS payload during Phase A, which began in 1978, during the operation of the Seasat mission.

3.S ER ‑1 Detailed Definition Phase B

In 1981 a major milestone in the development of ERS‑1 was the approval by 10 ESA Member States, plus Canada and Norway, of Phase B (total financial envelope: 25 million AU1). The Phase B industrial activities began in April 1982,

1 The ESA Accounting Unit (AU) was roughly equal to the European Currency Unit (ECU). In 1995, 1 AU = 6.58 FF, £0.78, 1.93 DM, 1885 Lira.

12 History of ERS

carried out by a very large consortium led by Dornier Systems GmbH, Germany, as the Prime Contractor. The scope of the contract included the establishment of the space segment specifications (platform and payload), the detailed definition of the ground segment (payload data management and mission operations), the continuation of critical technology development and breadboarding activities (see Table 2) and the definition and realisation of experimental campaigns to train users and demonstrate the soundness of the instrument concept and performances required. The objectives for the ground segment were ambitious, and it was sized for the delivery a number of selected products within 3 h of observation on the satellite. An additional condition was that maximum use must be made of existing national ground facilities for data acquisition, preprocessing and archiving. Phase B also included the preparation of technical and financial proposals for Phases C/D/E on a design-to-cost basis. A major difficulty encountered by the ESA project team at the beginning of Phase B was the reluctance of industry to make contractual commitments on the basis of geophysical performance requirements for the ERS‑1 instruments. This was considered too risky, as it would rely on semi-empirical conversion algorithms. As a consequence, the project team had to make a major effort to produce technical and engineering specifications based on the agreed

Table 2. Summary of critical issues at end of ERS‑1 Phase B (ESA, 1997b).

Components Critical issues Satellite —— Update of the coupled analysis in the light of work at the subsystem level —— Detailed definition of the AMI power amplifier radiator —— Power distribution subsystem optimisation —— Definition of the titanium elements —— Development of the payload harness mockup Active Microwave Instrument —— SAR antenna testing procedures and detailed definition facilities —— Scatterometer antenna: detailed design of hinges and release mechanism and analysis of mechanical/ thermal distortion —— Multipaction effect for antenna waveguide interfaces —— Harmonisation and verification of AMI internal interfaces for equipment and unit and cabling design (in particular waveguides) —— Procedure for selecting the power amplifier tube (choice between travelling wave tubes and a klystron) and participation in tests —— Influence of multipaction effect at the switching matrix level —— Detailed design of matched filters —— Optimisation of power requirements Radar Altimeter —— Continuation of the development of the pulse generator (chirp) —— Detailed design of the unit for locking the receiver onto the radar signal (tracker) and of the onboard signal processor and associated software —— Adaptation of the ultrastable oscillator specifications to meet the requirements of the Radar Altimeter —— Continuation of the development of the radar return signal simulator Payload Telemetry Equipment —— Detailed definition of the format structure to limit transmission of useless words (Instrument Data Handling and —— Mockup of the high-transmission rate interface in order to select appropriate components Transmission, IDHT) —— Evaluation of the results of predevelopment contracts for the X‑band antenna and the QPSK modulator Platform —— Detailed definition of modifications to be made to the SPOT platform in the onboard control software area —— Updating of a dynamic mathematical model of the satellite Ground Segment —— Continuation of work needed to keep the development of the SAR data processing equipment on schedule —— Monitoring the definition of the onboard software to assess its influence on the definition of the Ground Segment Assembly, integration and —— Detailed definition of the satellite/Electrical Ground Support Equipment (EGSE) interface specifications verification —— Definition of the operational requirements of the EGSE and standardisation of the equipment

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geophysical performance requirements before the industrial contract was signed. An extension of Phase B to end of March 1984 was agreed (total financial envelope: 8 million AU) to continue the definition of critical elements of the programme and for the procurement of the long-lead-time components (see Table 2). It should be noted that an early definition of the ERS data policy guidelines (see Annex C of the ERS‑1 Declaration) had to be elaborated during Phase B at the request of delegations as a sine qua non condition prior to the final approval of Phases C/D/E. This has to be seen in the international ‘commercial’ context of the 1980s, which saw the adoption of the Landsat Commercialisation Act in the , the definition of the French SPOT Data Policy and the creation of the company Spot Image in July 1982.

4.S ER ‑1 Development Phases C/D

In July 1984, Phases C/D/E were approved by 10 ESA Member States plus Canada and two associated states (Norway and Austria) with a total financial envelope of 606.63 million AU (502.88 million AU for Phases C/D and 105.75 million AU for Phase E). The industrial work started in December 1984 with Dornier Systems GmbH as the Prime Contractor, and involving about 80 firms, mostly European (and Canadian) aerospace companies (see Fig. 2).

Figure 2. Members of the ERS‑1 industrial consortium during Phases C/D/E (ESA, 1997b).

14 History of ERS

5.S ER ‑1 Challenges

During Phases B/C/D, from 1982 to the launch of ERS‑1 in 1991, in addition to the detailed definition of the ERS‑1 system, the management of industrial development, testing activities and technical reviews typical of such an ambitious project, the ESA project team faced a number of challenges, which are summarised in the following.

5.1 Critical Technologies

ERS ‑1 required the integration of key technologies, some of which were available at national level within ESA Member States, while others had to be developed within the programme. One should recall the high complexity of the instrumentation and the lack of experience/know how in Europe to cope with this extreme complexity. Some examples of critical technologies/issues are listed below for each key payload instrument (note that there were also stringent requirements for the platform subsystems such as the Instrument Data Handling and Transmission (IDHT) and Low-Bit-Rate (LBR) tape recorders for the satellite operations such as orbit determination and control, attitude control and pointing accuracy, etc.):

—— The SAR mode of AMI: SAR antenna manufacturing: a 10 × 1 m slotted waveguide array made of Carbon-Fibre-Reinforced Plastic (CFRP) with a metallised inner surface and subdivided into five mechanical panels; major issues with thermal stability behaviour, the metallisation process, the deployment sequence, and the requirement that the planarity be better than 1.5 mm; developing and testing the high-power amplifier/Travelling Wave Tube; the issue of arcing; overcoming the effect of multipaction (vacuum discharge) for the antenna waveguide interfaces and switching matrix; and developing a Surface Acoustic Wave (SAW) device for the chirp signal.

—— The Scatterometer mode of the AMI: three-beam scatterometer antenna design and realisation as aluminium planar slotted waveguide arrays, each one electrically subdivided into two panels; and stringent elevation requirements for the sideways-looking mid-beam.

—— Radar Altimeter: stringent requirements for the chirp pulse generator (SAW device).

—A— TSR: very high blackbody accuracy requirements; active Stirling cycle cooler to maintain detector stability to support very accurate emissivity; temperature uniformity; and temperature measurements with an error of less than 33mK.

The ERS‑1 technical specifications are summarised in Table 3. The geophysical validation of ERS‑1 derived products (e.g. surface wind and wave fields, sea surface temperature fields), involving retrieval algorithms and models (e.g. CMOD for the Wind Scatterometer), has required the implementation of major dedicated campaigns (in Europe, Canada and the United States) as well as statistical analysis of data, comparison of data with meteorological and oceanographic model outputs, and with in situ observations from ships, buoys and platforms.

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Table 3. ERS‑1 technical specifications Parameter Value (ESA, 1997b). Orbit Type Near-circular, polar, Sun-synchronous Altitude 782−785 km Period about 100 min per day 14.3 Repeat cycle 3-day, 35-day and 176-day Instruments Active Microwave Instrument comprising SAR (image and wave modes) and a Wind Scatterometer; Radar Altimeter; Along- Track Scanning Radiometer; Microwave Radiometer; Precise Range and Range-rate Equipment; and Laser Retroreflectors Mass Total mass 2157.4 kg Total payload 888.2 kg Total platform 1257.2 kg AMI 325.8 kg Radar Altimeter 96.0 kg IDHT 74.0 kg ATSR/M 55.3 kg PRARE 12.0 kg Laser Retroreflectors 2.5 kg Electrical power Peak power ≤2600 W Permanent power ≤550 W Power supply voltage 23−37 V Onboard energy 2650 Wh max Altitude and orbit control Type 3-axis stabilised Earth-pointed Data Handling Onboard computer Word length: 16 bits Payload memory capacity 20 K words max Communications Transponder Coherent S-band (2 kbit/s) Transmission power 50−200 mW max Telemetry rate 2048 bit/s Telecommand rate 200 bit/s Data downlink —— X-band (105 Mbit/s high rate link for SAR image mode) —— X-band (15 Mbit/s low rate link for realtime and playback of LBR data —— Onboard recorders provide 6.5 Gbit storage —— S-band telemetry links for housekeeping data

5.2 Calibration and Validation Approach

Particular attention was paid to the Calibration and Validation activities, since these would be critical for the development of science and applications. A dedicated delegate body, the ERS‑1 Operation Plan Advisory Group (EOPAG) was set up to deal with ERS‑1commissioning phase activities, the choice of the orbit scenario and the definition of the High-Level Operation Plan (HLOP). EOPAG operated from1986 to 1992. For each payload instrument, a calibration plan, including internal and external calibration activities and a geophysical (where applicable) validation

16 History of ERS

plan, was defined and implemented. This required the development of innovative calibration techniques, including the use of:

—— reference targets such as ground transponders or Active Radar Calibration (ARC), as well as the use of distributed, stable and homogeneous targets (e.g. the Amazon rainforest) for the SAR and Scatterometer, —— satellite laser ranging stations in Austria, Italy, Switzerland and the UK, —— an oceanographic research platform (near Venice, Italy) for the Radar Altimeter, and —— incorporated black bodies and ground targets with known albedo (such as White Sands, New Mexico) for the ATSR.

The geophysical validation, involving retrieval algorithms and models (e.g. CMOD for the Wind Scatterometer), the required dedicated campaigns (in Europe, Canada and the United States for the Scatterometer), statistical analysis of data, comparison of data with meteorological and oceanographic model outputs, and in situ observations from ships, buoys and platforms.

5.3 Selection of the Orbit Configuration Scenario

In order to meet the requirements of the various user communities (land, ice, ocean, coastal zones, geodesy) in terms of revisit frequency, as well as those of the commissioning phase activities (requiring a fast revisit frequency over calibration sites), several trade-off studies were performed in particular with the French Groupe de Recherches en Géodésie Spatiale (GRGS) in and the German Max- Institute for Meteorology (MPI) in Hamburg. The Programme Board for Remote Sensing (PB-RS) then evaluated the merits of different repeat cycles and provided recommendations. The orbit configuration selected included several repeat cycles, as shown in Table 4. The selected sequence of repeat cycles generated lively discussions, particularly in relation to the Geodetic Phase E, which would require a long repeat cycle to allow dense spatial sampling of the marine geoid with the Radar Altimeter. The concerns about the implications of such a repeat cycle for other key priority user communities (land and ice in particular.) were reinforced when a second Geodetic Phase (interleaved with the first) was proposed and adopted, doubling the spatial sampling density of the geoid (16 km at the equator). All this meant that the Mission Manager would have a hard time reconciling the demands of all the user communities, but his task was made easier when the existence of ‘pseudo-repeat cycles’ within this 168‑day cycle was demonstrated.

Table 4. ERS‑1 mission phases (ESA, 1997b).

Phase Description Repeat cycle Mean altitude Dates A Commissioning phase 3 days 785 km July to December 1991 B 1st Ice Phase 3 days 785 km December 1991 to March 1992 C 1st Multidisciplinary Phase 35 days 782 km April 1992 to December 1993 D 2nd Ice Phase 3 days 785 km January to April 1994 E 1st Geodetic Phase 168 days 770 km April to September 1994 F 2nd Geodetic Phase 168 days 770 km April to September 1994 G 2nd Multidisciplinary Phase 35 days 782 km April 1995 to end of mission

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5.4 Definition of the High-Level Operation Plan

The definition of the High-Level Operation Plan for ERS‑1 was also an important and difficult task for the definition of the instrument operation priorities, in particular for the AMI, for which the SAR and the Wind Scatterometer modes were mutually exclusive. Additional constraints that also had to be taken into account included the maximum and minimum operating times per orbit for the SAR mode, the maximum number of operations of the AMI switch matrix, high-priority geographical zones for the SAR mode as a function of the time of the year, the implications for coastal zone observations, etc.

5.5 Definition of the Overall Ground Segment Concept

The ERS‑1 ground segment included Telemetry, Tracking and Command (TT&C) operations performed by ESOC, and payload data management by ESRIN. The ground segment for the payload data handling was composed of two parts: the Near-Realtime (NRT) part with responsibility for generating and distributing an agreed set of products (geophysical parameters and SAR imagery) within 3 h of observation on the satellite, and an offline part for generating more elaborate products and for archiving and distributing them. Studies of the NRT component in relation to the downloading, processing and distribution of LBR tape-recorded data recommended a receiving station network including Kiruna (Sweden), Maspalomas (Spain), Gatineau (Canada) and Prince Albert (Canada). For the SAR data, the receiving stations at Kiruna, Fucino and Maspalomas were selected. For offline activities, four national Processing and Archiving Facilities (PAFs) were selected: the DFVLR (now the , DLR) at Oberpfaffenhofen, Germany, the Research Institute for Exploitation of the Sea (IFREMER) in Brest, France, the ASI facility at Matera, Italy and the Royal Aircraft Establishment (RAE), Farnborough, UK, each with well-defined responsibilities and mandates.

5.6 Definition and Implementation of Simulation Campaigns

Prior to the launch of ERS‑1, many airborne simulation campaigns related to the C‑band SAR, the C-band Wind Scatterometer and the Ku-band Radar Altimeter were carried out to provide samples of data to scientists for detailed analysis and interpretation, as well as to develop processing algorithms, test calibration techniques and generate sample products. Several of these campaigns were funded by and executed in cooperation with the EC’s Joint Research Centre, the Canada Centre for Remote Sensing and NASA/Jet Propulsion Laboratory (JPL), such as the Convair 580 campaign over Europe in 1981, the Agriscatt and Toscane-2 campaigns in 1988, and the airborne C-band campaigns over the Mediterranean Sea and French Guiana in 1989.

5.7 Development of User Communities

ESA devoted considerable effort and resources to the development of user communities, which in the early 1980s were still embryonic and not well structured. These efforts included:

—— The release in 1986 of an AO for scientific experiments and application pilot projects generated high interest and 140 replies, with 276 individual proposals covering all ERS research themes and applications. More than 100 Principal Investigators (PIs) were selected, each representing a team of scientists, some of them very large. The team of the Programme for International Polar

18 History of ERS

Ocean Research (PIPOR), for example, included several dozen polar ocean and ice scientists. The PIs held their first meeting at ESRIN in May 1988 and an organisation had to be in place to manage such a large number of scientists from all continents (Coordinating Investigators were introduced for each theme and/or geographical region). The PIs played a key role in the development of the user communities and in raising awareness of ESA’s Earth observation (EO) programmes and the ERS in particular. Thousands of peer-reviewed publications resulted from the AO process, which was repeated several times during the lifetimes of ERS‑1 and ERS‑2, with a variety of objectives, ranging from scientific research, application demonstration projects to the exploitation of ERS‑1/ERS‑2 tandem data collected following the launch of ERS‑2 in 1995. Regular ERS symposia were organised and attracted increasing numbers of participants, from 400 at the first meeting in Cannes, France, in 1992, to 1200 at the final symposium in Bergen, Norway, in June 2010.

—E— SA set up instruments and product advisory expert groups for each ESA- funded instrument (SAR, Wind Scatterometer, Radar Altimeter and a data teams to contribute to the definition and preparation of sensor calibration and product validation activities. These groups brought together the best European and Canadian experts, who contributed to the definition of processing algorithms to generate the agreed geophysical parameters derived from engineering data. They also assisted the project teams in the definition of airborne campaigns to support the calibration and validation activities.

—— EARSeL members (>200 laboratories and institutes) played an active role in expanding the user communities by organising specialised studies financed by ESA, as well as various workshops and symposia.

—— The creation of the European Association of Remote Sensing Companies (EARSC) in 1989, a network of value-added companies and small and medium enterprises (SMEs) provided an impetus for the development of operational/ commercial applications of ERS data. The EARSC now has 65 members.

—E— SA launched a range of activities to promote and raise awareness of the ERS programme, including presentations at important events, conferences, symposia, specialist workshops and thematic application events. ESA’s cooperation with foreign countries and/or international organisations provided opportunities to promote the ERS programme.

5.8 Definition of the ERS‑1 Data Policy

In 1998 the Programme Board for Remote Sensing set up a working group, chaired by Marianne von Glehn, the Swedish delegate, to define the ERS‑1 data policy. This was a major undertaking in view of the divergent views among the delegates on the approach (science versus commercial) to be adopted regarding the conditions of access to and distribution of ERS‑1 SAR and Low-Bit-Rate data to users, and direct access to SAR data by national (from participating states) and foreign ground stations. The policy that was finally adopted in 1990 (after two years of discussion!) was complex, based on an open and non-discriminatory approach and identified ‘user categories’ with specific conditions in terms of pricing policy. Fortunately, this approach was later simplified for Envisat, and was based on ‘usage categories’.

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Figure 3. The launch of ERS‑1 (V44) on 17 July 1991. (ESA/Sigma, Noges)

6. ERs→Launch and Early Exploitation Phase E

The ERS‑1 Launch Readiness Review took place in early 1990 but following the failure of flight V36, announced a delay and fixed a new launch date in May 1991. Further problems with the third stage of Ariane 4 forced a rescheduling of the launch for 16 July 1991. ERS‑1 was launched at the opening of the 10 min launch window, at 01:46 GMT on 17 July 1991 (Fig. 3) and was delivered with high accuracy to the planned orbit so that only 10 kg of hydrazine was required for final orbit acquisition, leaving a healthy fuel margin to support a long lifetime (recall that ERS‑1 was designed for a nominal two-year lifetime, with consumables sized for three years). This section highlights some early results obtained with ERS‑1 data a few weeks/months after launch when still in the commissioning phase. More detailed examples of results are presented elsewhere in this volume. The first SAR images of Spitsbergen and the Frisian islands, the Netherlands (Fig. 4) were acquired on 27 July 1991, 10 days after launch and the successful switch-on of all instruments (except PRARE, which failed after five days over the ). The first ERS‑1 image, after processing, was blurred and was not what was expected (Fig. 5). The engineers at ESRIN, with remote support of the Prime

20 History of ERS

Figure 4. One of the first SAR images acquired by ERS‑1, covering the calibration site of Flevoland, the Netherlands, on 27 July 1991. (ESA)

Figure 5. The first ERS‑1 image with inverted I/Q assignments, with original annotation. (ESA)

Contractor of the ERS‑1 Ground Segment and the Canadian company MDA that developed the processor, quickly identified that the data from ERS‑1 were perfect, but that the I/Q assignments of the processor needed to be inverted. MDA (Paul Lim) performed this remotely and less than 48 h after acquisition of the first data, an image with perfect quality was processed. Although the original mission requirements for ERS‑1 did not include the provision of an interferometry capability, the high performance and engineering quality of the ERS‑1 system allowed the development of this

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Figure 6. The first interferograms and unwrapped phase of the Gennargentu mountains, Sardinia, from ERS‑1 (left) and ERS‑2 (right). (ESA/POLIMI)

Figure 7. Topographical map (top) and 3D view (bottom) of Alaska obtained from ERS‑1 SAR data with SAR interferometric methods. These images show the extraordinary power of ERS‑1 to extract topographical information from SAR data. (C. Werner and R. Goldstein, ETH Zurich, and JPL, Pasadena, CA, USA)

22 History of ERS

technique, as reported by Fabio Rocca elsewhere in this volume. Philipp Hartl and his team at the Italian Institute for Navigation (INS) and the Polytechnic University of Milan (POLIMI) performed a ‘blind’ experiment using a set of corner reflectors, some having been moved up between successive ERS‑1 passes. The interferometric technique allowed the detection of small, centimetre-scale changes in the position of a few selected corner reflectors. The following pages show a number of interesting results obtained by ERS. Figure 8 shows a 10-km long oil slick detected off the coast of the South of France near St Tropez, on 19 September 1991. Figure 9 shows high-wave storm events detected by the ERS‑1 Radar Altimeter in August 1991. The storm off the coast of South Africa caused serious damage to giant oil tankers, which were forced to stop in Durban harbour for repair.

Figure 8. Image of an oil slick off the coast of St Tropez, France, 19 December 1991. (ESA)

Figure 9. Smoothed Significant Wave Heights (SWH) from ERS‑1 orbits 208−251, with ice edges added from a discriminator using RA data, 1−3 August 1991. (ESA)

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An improved marine geoid derived from ERS‑1 data (2 × 168-day cycle) is shown in Fig. 10. This is not an early result but the generation of an improved gravity field resulting from the exploitation of the two geodetic phase data which led to the declassification of the US Navy (classified) marine geoid. Figure 11 presents one of the first results provided by the ATSR, showing oil well fires in Kuwait during the first Gulf War in 1990. Wind scatterometer data are now used for weather forecasts, in cooperation with the European Centre for Medium-Range Weather Forecasts (ECMWF). Figure 12 shows the improvements in these forecasts, particularly for the southern hemisphere. Figure 13 shows an image of ERS‑1 taken from the French SPOT-4 satellite over Ténéré in the Sahara. Figure 14 shows a 3D image of the 1997−98 El Niño, created from simultaneous ATSR and Radar Altimeter data acquired by ERS‑2 in November 1997. This El Niño Figure 10. Geoid derived from ERS‑1 data. had very strong impacts worldwide, with increased rainfall causing flooding in (ESA/GFZ) the United States and Peru, and drought in the western Pacific. This 3D image of the Pacific shows the deviations from the ‘normal’ ocean state, with measured changes in Sea Surface Height of between −40 cm and +40 cm and temperature changes of −6°C (blue) to +8°C (red). In September 2002, measurements of total ozone by the Global Ozone Monitoring Experiment (GOME) on ERS‑2 showed an atypical split in the ozone hole over the Antarctic due to the unusual meteorological conditions in that year (see Fig. 15). GOME monitored the ozone layer and other atmospheric trace gases for 16 years, and led the way in the use of space instrumentation for air quality monitoring.

Figure 11. ATSR image of oil well fires in Kuwait, 1990. (ESA/RAL)

Figure 12. Improvements in ECMWF weather forecasts since 1981. (ECMWF)

Figure 13. Pictures of ERS‑1 taken from SPOT-4. (CNES/SpotImage)

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Figure 14. 3D image of sea level and temperature anomalies in the Pacific during the 1997−98 El Niño event, based on simultaneous ATSR and Radar Altimeter data acquired by ERS‑2 in November 1997. (ESA/CEOS)

Figure 15. An atypical split in the ozone hole over the Antarctic, measured by the GOME instrument on ERS‑2 in September 2002. (ESA/DLR)

7.S ER ‑1 Routine Exploitation

The ERS‑1 exploitation phase continued until the failure of the satellite on 10 March 2000 after nine years in orbit (45 000 orbits and 1.5 million individual SAR scenes collected). In 1992 ESA and the ERS Consortium (which included Spot Image, Eurimage and Radarsat International) reached agreement on ways to promote and distribute ERS‑1 products, in which ESA retained responsibility for the delivery of products to selected PIs free of charge. During that period ESA released a number of AOs; the second AO in 1993 for application pilot projects, for example, led to the selection of 80 projects. ERS symposia were

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Figure 16. The global network of ERS receiving stations. (ESA)

held in Hamburg in 1993 (600 participants), in Toledo in 1994 (for application projects only – 250 participants), in London in 1995 (again for application projects only – 250 participants), and Florence in 1997 (700 participants). In parallel, ESA negotiated numerous agreements for the acquisition of ERS‑1 SAR data with Asian countries (Japan, China, Taiwan, Singapore, Indonesia, Thailand), Africa (South Africa, Kenya), South America (Argentina, Ecuador), (Syowa, McMurdo, O’Higgins), the Unites States and Israel. It also entered into international agreements regarding the deployment of mobile/transportable stations (in Gabon, Mongolia and Bangladesh), and for upgrading some 20 receiving stations (see Fig. 16). ESA also organised major events, such as the Euro‑Asian Space week in Singapore and the Euro- Latin America Space week in Mexico, to extend the use of ERS data and raise awareness of ESA’s EO programmes. ESA, either alone or in association with UN agencies, has organised numerous education and training courses in developing countries and at ESRIN, and has collaborated with international organisations, in particular the UN Educational, Scientific and Cultural Organization (UNESCO), the UN Environment Programme (UNEP), the UN International Drug Control Programme (UNDCP), the UN Office for Affairs (UNOOSA), the World Meteorological Organization (WMO), the UN Food and Agriculture Organization (FAO), the Intergovernmental Oceanographic Commission (IOC), as well as the World Bank and the Asian and African Development Banks (the Asian Development Bank, for example, provided funding for the upgrading of the Bangkok receiving station). Such cooperation has greatly helped to promote and extend the use of ERS data and products.

8.S ER ‑2 Approval and Launch

In 1988 ESA felt the need for a second flight unit, ERS‑2, that would ensure the continuity of ERS‑1 data, both for scientific research (e.g. for detecting small climate changes) and for the development of applications. Past experience had shown that it takes 5−10 years to develop operational application services. At that

26 History of ERS

time, there were also increasing concerns within the scientific community about ozone depletion and global warming. In November 1988, ESA consulted European scientists and invited them to propose an atmospheric chemistry instrument which would be embarked on ERS‑2. The selected proposal, by John Burrows and Paul Crutzen (who was awarded the Nobel Prize for Chemistry in 1995), was called ‘SCIA-mini’, derived from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instrument selected for Envisat. The instrument was further simplified to meet the ERS‑2 platform resource constraints and was renamed the Global Ozone Monitoring Experiment (GOME). The GOME Phase B started in July 1989 and the development phase in April 1992, under the stringent condition that the addition of GOME should not delay the development of the main system. Also, GOME should be accommodated within the limited ERS‑2 system margins, and the project management and system engineering should be provided by ESTEC staff. All of these conditions were met and GOME was ready for launch three years later. This extremely short development time represents outstanding performance for such sophisticated space instrumentation. The GOME ground segment, including a GOME data processing unit, was developed, implemented and operated by the German Processing and Archiving Facility (PAF) with national funding. In spring 1990 the Programme Board for Earth Observation (PB-EO) approved the ERS‑2 programme budget, which totalled 440.33 million AU,

Figure 17. The launch of ERS‑2 on 21 April 1995 (Ariane 4/flight V72). (ESA/Service Optique CSG, M. Hucteau)

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including 370.98 million AU for Phases C/D and 69.35 million AU for Phase E. With the addition of GOME for stratospheric ozone measurements, the ERS‑2 payload was further improved with the ATSR-2, with three additional VIS channels, and PRARE-2, which had been completely redesigned and rebuilt using Hi-Rel or military standard rather than commercial components. ERS‑2 was launched on 21 April 1995 at 01:44 GMT (Fig. 17).

9. ers‑1/ERS‑2 Tandem Operations

Towards the end of 1993, it became clear that ERS‑1 would still be technically operational when ERS‑2 was launched. The concept of ERS‑1/ERS‑2 tandem operations started to take shape, considering that two identical SAR instruments would be flying at the same time. The participants in several ERS‑1 user meetings and symposia expressed their strong interest in the simultaneous operation of ERS‑1 and ERS‑2 over a period of several months, possibly up to one year. To explore the potential benefits of such ERS‑1/ERS‑2 tandem operations in more detail, ESA organised several consultation meetings with both scientific and application-oriented users at ESRIN. They highlighted the many advantages and benefits for both research and applications of flying two identical sets of instruments in an appropriate orbital configuration; in particular, a ‘unique SAR dataset’ could be collected for interferometry applications (Fig. 18). Following difficult negotiations with delegations (the extra operations would cost some 5 million AU), on 23 March 1995, the ESA Council adopted a resolution on ERS‑1/ERS‑2 tandem operations that would ‘keep ERS‑1 alive’ until April 1997 and allow such operations for a nominal duration of nine months, beginning at the end of the ERS‑2 Commissioning Phase in autumn 1995 to April 1996 (this initial period was further extended in view of the outstanding results obtained). A primary objective of the tandem operation phase was to acquire, as quickly as possible, complete Earth coverage with ERS‑1 and ERS‑2 SAR data within the visibility of the existing ERS‑1 ground receiving stations that had responded positively to this request. The collection of a unique tandem dataset was considered to be an asset for the development of interferometry applications such as Digital Elevation Models (DEMs) and differential

Figure 18. ERS‑1/ERS‑2 tandem orbital configuration. (ESA)

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interferometry applications, allowing the detection of subcentimetre displacements/ground motion. Just a few days after the launch of ERS‑2, a number of SAR images were acquired using ERS‑1 and ERS‑2 in tandem mode, observing the same region with a one-day interval. These data were quickly processed and analysed by European experts to demonstrate some of the possibilities offered by SAR interferometry for the generation of DEMs (see Rocca, section 2.4, this volume). An AO for the exploitation of the tandem dataset led to the selection of 61 projects. The first Fringe workshop on ASAR interferometry was held at ESRIN in October 1996 and a second in Liège, Belgium, in November 1999.

10.e Th Failure of ERS-1

The failure of ERS‑1 in January 2000 was caused by a series of failures during a blind orbit. First, the Central Control Unit (CCU) encountered a memory block failure, and the satellite automatically took protective measures and placed itself in fine acquisition mode. Shortly thereafter, one of the piloting triplet gyros failed. Since this was the second major failure, the CCU took the strongest protective measure and triggered the transition of the satellite to Safe Mode. In this configuration, however, the switch-off of the failing piloting gyro also introduced a switch-off of the electronic unit controlling a pair of gyros, the failed gyro and the gyro that was in charge of the Attitude and Orbit Control Subsystem (AOCS) in Safe Mode. In this situation the satellite had to cope with two recovery measures within a very short time, where the first measure has not been concluded when the second measure to go to Safe Mode had already started. This combination of acting on an uncompleted AOCS change prevented the correct entry into Safe Mode. In the absence of piloting gyros, the satellite was no longer pointing in an auto-controlled fashion. Unfortunately, the gyro failures and the attempt to enter Safe Mode took place during the first blind orbit of the night, and the situation of the satellite was only discovered at first visibility three orbits later. At this point the satellite was already rotating around the pitch and roll axis and the batteries were getting very low. In this configuration the satellite was no longer controllable. Ground commands to force the satellite to enter Safe Mode by changing the redundant Safe Mode gyroscope failed several times because the S-band antenna was moving and could not receive the commands. When one of the commands to enter Safe Mode was finally recognised, the panel was facing away from the Sun, pointing towards deep space, preventing the batteries recharging and power was decreasing very quickly. Despite continued efforts to command the satellite from the ground, ERS‑1 was unrecoverable. ERS‑1 generated a wealth of observational data, including about 800 000 radar images acquired in its first three years of operation. The last SAR image was taken on 7 March 2000, when ERS‑1 had been in service for about 8.5 years, more than three times its nominal mission lifetime.

11.S ER ‑2 Operations without Gyros

In mid-November 1999 the piloting gyroscopes, which ensure stable control of the ERS‑2 satellite, showed signs of severe degradation. By the end of 2000, three of the six gyros had stopped functioning and another two were showing the same telemetry pattern of degradation as those that had failed. A team led by the Post-Launch Support Office (PLSO/ESTEC), composed of ESOC, ESRIN and Astrium, the company that provided the platform, elaborated work-around solutions (‘zero gyro mode’) for piloting the satellite. They allowed the mission to continue without gyros, while meeting most of the pointing accuracy

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specifications. In this mode, precise roll and tilt piloting were ensured by the Digital Earth Sensor and Sun Sensor, while the yaw variation was measured based on the Doppler extracted from the Active Microwave Instrument Wave Mode science data. These processed data, giving the yaw deviation over the orbit, were provided by the ground stations to the Flight Operations Team at ESOC for corrective command uplinks. The corrections had to be made during several consecutive passes over Kiruna before they had an effect on the pointing accuracy. Thanks to the ERS ground station network, which is still based on agreements and contracts established at the start of the ERS‑1 mission, a global Low-Bit-Rate station network2 was providing Wave Mode data to ESOC within less than 40 min to be considered for attitude correction. A further improvement in the attitude accuracy was made in mid-2003, when a new Scatterometer Near-Realtime processor became operational that was able to handle a relaxed attitude precision of up to ±2°. The Scatterometer, with its three beams providing a significantly better yaw angle determination, replaced the Wave Mode data for attitude control in 2004. The outstanding success in keeping the satellite operational without gyros was due to the reliable and timely support of the LBR stations that were purposely integrated into the overall flight operations setup. However, an important factor in this success was the availability of the internet, as a robust infrastructure for global data flow, to support the ‘open-loop’ attitude control. With this approach, the initial yaw uncertainty caused by the gyro failures was reduced from ±10° to ±2°, ensuring the full functioning of all instrument missions. At the end of this recovery exercise, only the interferometry mission (which required very high pointing accuracy) was affected by the slight attitude degradation, although it was still possible to use some 50% of the data.

11.1 f Loss o the Tape Recorders

The ERS satellites were equipped with two conventional technology recorders using magnetic tape. The mechanical recording and playback operations of these recorders led to deterioration of the tape material. In December 2002 the tape of the first recorder snapped, and six months later, so also did the second. As a consequence, no onboard recordings could be made and observations could only be performed within direct station coverage. Therefore, from July 2003, the ERS‑2 LBR mission continued as a regional mission with an initial global coverage of just 8%. Within a few weeks the LBR mission scenario was modified to take maximum advantage of the existing ERS LBR station network, which was gradually extended over the years to cover 45% of Earth’s surface (see Fig. 19). Despite this reduced observation coverage, the ERS‑2 LBR data remained useful, in particular because of the special efforts that were made to ensure full coverage of the North Atlantic, since this has strong influence on European weather. Because of the long-term stability of the data and the measurement accuracy, they have been used by the ECMWF in models.

2 The LBR network includes ESA stations at Kiruna (Sweden), Maspalomas (Spain), Matera (Italy), Gatineau (Canada) and Prince Albert (Canada); the national stations O’Higgins (Antarctica), Chetulmal (Mexico) and West Freugh (UK); and foreign stations in Miami (USA), Beijing (China), Hobart (Australia), Johannesburg (South Africa) and Cuiaba (Brazil).

30 History of ERS

Figure 19. The ERS‑2 LBR station network used to compensate for the loss of the two tape recorders. (ESA)

11.2 Atsr‑2 Failure

On 5 February 2008, after some 4 billion revolutions, the ATSR‑2 scan mirror stopped turning following the mechanical failure of the mirror bearings. For 13 years the instrument had provided high-precision data that met the specifications with an outstanding stability. For Sea Surface Temperatures, a retrieval accuracy of 0.1K was achieved thanks to an onboard calibration system and the instrument’s excellent engineering that allowed the mission to exceed by four times its designed lifetime. The Advanced ATSR flown on Envisat was the successor to the ATSR-2 instrument on ERS‑2. The two instruments, observing the same target 28 min apart, were intensively cross-calibrated, met the Global Climate Observing System (GCOS) monitoring principles and represented significant assets for understanding Earth and its evolution in the context of climate change.

11.3 One Mission across Three Platforms

The ERS instruments were the predecessors of the Envisat ASAR, Radar Altimeter-2, AATSR and SCIAMACHY. The ERS Scatterometer and the successor to GOME are now flying on MetOp-A. Ensuring long-term data continuity over more than two decades, the cross-calibration requirements of the GCOS Climate Monitoring Principle no. 12 (GCOS, 2003) were fulfilled. Throughout these missions special emphasis has been given to determining and documenting inter-satellite/instrument biases. Furthermore, the ERS and equivalent Envisat instrument data were harmonised with respect to format and the same retrieval algorithm was used. This was particularly important for reprocessing the entire cross-satellite datasets. Data quality and homogeneity were operationally assessed with the support of the ECMWF for the Scatterometer and Wave Mode missions. For the ATSR missions, the data quality was jointly monitored by the Rutherford Appleton Laboratory (RAL), the instrument provider, the PI team, and the UK Natural Environment Research Council (NERC), which funded the ATSR mission. The high quality of the GOME mission data was ensured by DLR, which was responsible for operational processing and for improving data retrieval. This led to five reprocessing to derive new trace gases with progressively smaller error bars. Long-term quality assurance and documentation were essential elements of the ERS mission over two decades. For routine operational monitoring, a consortium of specialists monitored instrument performance on a daily

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basis. Quality Working Groups (QWGs), composed of European and Canadian specialists, evaluated the evolution of the instruments, analysed the retrieval algorithms and defined actions for further improvements. This setup ensured continuous product evolution beyond the lifetime of the ERS missions. The products also improved thanks to ESA’s tight working relationships with the global user community whose members provided inputs at workshops and symposia. For instruments that were jointly exploited by Eumetsat and the UK’s Department of Energy and Climate Change, the mechanism of Science Advisory Groups (SAGs) was used to advance the science and improve and develop the instrument products. In the case of the Advanced Scatterometer (ASCAT) on MetOp, for example, the user community, the QWG, the ASCAT SAG and Eumetsat collaborated in the creation of a Scatterometer Soil Moisture product, which after 20 years allowed ‘ocean data’ also to be used for land applications. ERS, one of the longest multi-instrument missions ever flown, has in its operations scenario also considered the GCOS Climate Monitoring Principles. With the continuous mission extension approvals by Member States, ERS‑2 had a five-year overlap with ERS‑1, a nine-year overlap with Envisat and a five-year overlap with MetOp-A for the Scatterometer and GOME‑2 mission. Before deorbitation, an LBR station was installed in Brazil to allow concurrent observations by the ERS Scatterometer and the MetOp ASCAT over the large homogeneous target of the Amazon rainforest covering all three beams, which contributed to the best possible cross-calibration of these twin instruments. A further example of the evolution of the mission is described in section 2 of the WMO’s Scientific Assessment of Ozone Depletion, on the analysis of measurements of the total ozone column retrieved from nadir sounding instruments (WMO, 2010). The report described the GOME‑1 dataset as the most reliable and accurate for the analysis of ozone trends for the morning orbit. The dataset has therefore become the standard in terms of reliability and accuracy. The SCIAMACHY and GOME‑2 data records are currently being combined with those of GOME‑1 to provide full global coverage for trend analysis adapted to GOME‑1.

11.4S ER ‑2–Envisat Tandem Operations

The inspiration to exploit cross-satellite interferometry capabilities came from the Swiss company Gamma Remote Sensing AG. Envisat was flying 28 min ahead of ERS‑2, observing the same ground targets. However, the two satellites had a slightly different centre frequency of 31 MHz. In order to perform interferograms taking advantage of this short observation interval, a perpendicular baseline of about 2 km (see Fig. 20) was needed. Since ERS‑2 had ample fuel, and Envisat

Figure 20. ERS‑2−Envisat tandem orbital configuration. (Gamma Remote Sensing)

32 History of ERS

had stringent Global Monitoring for Environment and Security (GMES) service commitments to fulfil, it was obvious that ERS‑2 needed to be manoeuvred to implement the required baseline at specific observation targets. Because the interferometry baselines could only be reached via slight inclination changes, and thus only a range of observation targets within a range of latitudes could be addressed, the ERS‑2–Envisat tandem mission was divided into four campaigns. The second and the fourth campaigns were to address change detection over the intermediate northern latitudes. The campaigns took place over the following periods:

—— first campaign, September 2007 to February 2008, northern latitudes; —— second campaign, November 2008 to April 2009, intermediate northern latitudes; —— third campaign, February 2010 to April 2010, Antarctica; —— fourth campaign, July 2010 to October 2010, intermediate northern latitudes.

The exploitation of the unique dataset was advanced and led by two Swiss remote sensing companies, Gamma Remote Sensing AG and Sarmap SA. The ERS‑2–Envisat tandem mission addressed numerous applications:

—F— or cryospheric applications, changes in the location of ice shelf grounding lines in 1992, 1994 and 2011 were identified and the dynamics of fast-moving glaciers was studied.

—— Outstanding results were achieved in the derivation of high-resolution DEMs of low-relief areas (see Fig. 21). The quality of these DEMs is equivalent to those derived from of the Shuttle Radar Topography Mission (SRTM) flown in February 2000, although the SRTM data were restricted to latitudes ±60°.

—— ERS‑2, a polar-orbiting satellite, acquired unique datasets that complemented the SRTM data with respect to coverage and to change detection over certain areas.

—— Outstanding results were achieved for forest mapping in Siberia, on the largest scale so far observed.

—— The ERS‑2–Envisat satellite constellation supported new applications for estimating biomass over low-relief areas.

Figure 21. Low-relief Digital Elevation Model of the Ob river estuary, Siberia (70.2N, 75.5E), derived from ERS-2/Envisat data, 3 October 2007. (U. Wegmueller and M. Santoro, Gamma Remote Sensing AG; ESA)

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Figure 22. Synergistic exploitation of ERS‑2 and Envisat. (ESA)

For day-to-day operations, the centralised user service and mission planning at ESRIN ensured that the synergy between ERS‑2 and Envisat was exploited to the full. ERS‑2, carrying the predecessor instrumentation, acquired data where Envisat conflicts could not be resolved. For example, ASAR on Envisat with its modes and different polarisations created conflicting demands, so that whenever Envisat Image Mode Swath 2 VV polarisation was requested within the ERS station coverage, then that task was assigned to ERS‑2 while Envisat addressed different requirements (see Fig. 22).

11.5S ER ‑2 Ice Phase

The ERS‑2 Ice Phase was intended to repeat the 1992 and 1994 ERS‑1 Ice Phases (see Fig. 23), even though the satellite had already been in space for 16 years with its known and unknown problems. Therefore ERS‑2 could ensure nominally only reduced duty cycles of some 4−5 min per orbit in order to ensure the safety of the satellite in view of its degraded power system. During the Ice Phase, however, the ESOC satellite operations team, in cooperation with the ESRIN mission planners, optimised and closely monitored the power utilisation of the satellite, allowing a maximum duty cycle of 10 min during eclipse periods to avoid depleting the batteries. The Ice Phase or ‘3-day repeat’ mission started on 10 March 2011, one day before the disastrous earthquake in Japan. Since the main mission objective was to observe the cryosphere, no pre-seismic acquisition was performed over this region. However, ERS‑2, with its fast mission planning capacity, was rescheduled to make observations of the Sendai area three days after the earthquake until the end of the mission. The ESOC Flight Dynamics team initiated special manoeuvres to ensure a minimal baseline of maximum 70 m with respect to the reference scene. Thanks to the low level of solar activity, the yaw accuracy was very stable, resulting in good data quality for interferometry for nearly all passes during the last mission phase. The Ice Phase could have started earlier (November 2010), but the orbit change of Envisat in October 2010 bound all the necessary resources and did not allow an ERS‑2 mission phase change at the time to ensure a better seasonal overlap with ERS‑1 data. There was, however, a seasonal overlap between ERS‑2 and ERS‑1 1994 data, which showed excellent results in terms of

34 History of ERS

Figure 23. ERS‑2–Envisat tandem mission observation targets. (ESA)

Figure 24. Retreat of the grounding line of the Petermann glacier in Greenland between 1992 and 2011. (ESA/University of Leeds/University of Strasbourg)

changes in the grounding lines and in the ice mass balances of major glaciers over a period of 16 years. Figure 24, derived by A. Shepherd (University of Leeds) and N. Gourmelen (University of Strasbourg), shows that the grounding line of the Petermann glacier in Greenland retreated by 0.7–3.4 km between 1992 and 2011.

11.6S ER ‑2 Operations Supporting Data Exploitation

Throughout the two decades of the ERS missions, the Earth observation environment has changed dramatically. This happened thanks to the availability of high-quality ERS data, the involvement and integration of the science community in the mission, as described elsewhere in this volume, and also to the dramatic improvements in Information Technology (IT). Since the early 1990s the capacity, cost and size of computers have changed significantly. At the launch of ERS‑1 the single-look complex image was cut

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into quarter scenes, since the VAX computer with its external array processors could not handle the processing of an entire 100 × 100 km frame, and data were disseminated via computer-compatible tape and later via Exabyte tape shipment. The small mass-market laptops in use today have far greater processing power than the SAR processing systems used at the start of the ERS programme. Likewise, at the beginning of the ERS‑1 mission in the early 1990s the requirement to disseminate data 3 h after sensing was an enormous challenge, for which a dedicated satellite-based data dissemination system, the Broadband Data Dissemination Network (BDDN), was deployed. Now, in the time of broadband internet, where mobile phones (LTE3 standard) have the same data source capacity as EO satellites, the demands and expectations of users have changed dramatically since the mission was defined. Since 1995 the ERS‑2 operations scenario gradually evolved and adapted to the changing environment. The Ground Segment, beside the data acquisition systems located at the stations, was equipped with off-the-shelf computing hardware. The ERS‑2 mission operations concept relied strongly on services available on the internet, as the following two examples illustrate. First, in 2007, ERS‑2 became a major contributor to the GMES Service Element Maritime Security Service (MARISS) project. To support this project, the mission planning procedure was tuned to allow satellite tasking within less than 8 h after the request was communicated. This service started on a trial basis thanks to the collaboration with DLR, which had the capacity to manage the entire NRT service. For this operational service, the Neustrelitz station (north of Berlin) ensured the provision of highly reliable ERS SAR data by offering observations up to the Portuguese Atlantic coast and the Mediterranean Sea, the timely processing of these data (initially at Oberpfaffenhofen until a local processing system was set up at the Neustrelitz station), and service support for the national authorities in their role as MARISS consortium members. Based on the success of this trial the service was quickly adopted by the entire ESA SAR station network covering Europe and selected world regions. In many cases the data were made available to the national authorities via the internet within less than 15 min after sensing. Despite the fact that systematic observations of the European coast were foreseen, in some cases fast tasking and operations were still required. The best performance from a satellite tasking request to the distribution of data, via the internet, to users was 6 h (mission planning, satellite tasking, acquisition, data processing and dissemination). Second, following the earthquake in Sendai, Japan, March 2011, ESA’s cloud computing infrastructure, which became operational in early 2008, was extended to Taiwan within 24 h, so that every three days ERS‑2 data over the Sendai area were provided to the geohazard science community in less than 3 h. In this ad hoc service setup, the Taiwan station (CSRSR) performed the acquisition and quickly put the data on ESA’s cloud computing assets, for which a Content Delivery Network storage facility was set up within a day on the US West coast. The CSTARS station in Miami performed data processing, putting the results back on ESA’s ‘virtual archive’, from which the National Research Council (CNR/IREA) in Italy picked up the lower-level product to create interferograms. These high-level products provided information on ground deformation (Fig. 25) triggered by the almost daily aftershocks. A further example of how the use of the internet allowed global collaboration in an operational manner is the Scatterometer Near-Realtime mission that followed the failure of the tape recorders on ERS‑2 in summer 2003. As described elsewhere in this volume, many X-band stations around the world

3 Long Term Evolution (LTE) standard for wireless communication of high-speed data for mobile phones and data terminals.

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Figure 25. Monitoring of the Japanese earthquake with ERS‑2 operated in a 3-day repeat cycle using the Small Baseline Subset− Interferometric SAR (SBAS−InSAR) approach. (ESA, SAR data; CNR/IREA, SBAS–InSAR processing; GSI, GPS RINEX data; ARIA team at JPL and Caltech, GPS data processing). have acquired, processed and disseminated Scatterometer NRT data for attitude control, as well as for science exploitation and operational meteorological support. The priorities of the mission were to provide full observation coverage of the North Atlantic, and to ensure that meteorological offices received timely, high-quality data. The available station network with Gatineau, Maspalomas, Kiruna, West Freugh and Matera stations, acquired nearly all possible passes, going beyond their contractual commitments and providing large volumes of overlapping data. The Royal Netherlands Meteorological Institute (KNMI) used the easy availability of the Scatterometer data via the internet to compile the products from the station network to form a homogeneous Scatterometer product. These products were then made available to the science community and to meteorological offices via the internet in NRT, allowing ERS data to be considered for assimilation and contributing to improved weather forecasts. A similar NRT service was set up for the GOME mission by the University of Bremen, the Belgian Institute for Space Aeronomy (BIRA) and KNMI. ERS‑1 (ESA) and the world wide web (CERN) were launched in 1991. Since then the exploitation of mission data has been closely linked to the internet and the evolution of mass-market IT assets. Today, the science community is interconnected via the internet, and online storage of large datasets (petabyte scale) is affordable allowing large-scale data sharing and supporting interdisciplinary research. One example of this new way of working is the Group on Earth Observation (GEO) Supersite infrastructure concept illustrated in Fig. 26. This online infrastructure will contain 20 years of ERS SAR data, as well as large-scale datasets derived from other space and in situ sources,

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Figure 26. ERS data within the GEOSS Supersite infrastructure. (ESA/EPOS/CEOS)

leading to new and outstanding science. These data, which currently represent the bulk of data assets in this infrastructure, provide the basis for the Global Earth Observation System of Systems (GEOSS) for monitoring geohazards. In 2011, scientists’ requests for hundreds of thousands of SAR images brought about significant changes in the operations concept since the start of the ERS mission. With respect to the future, the availability of cloud computing services at mass market prices (a few cents per hour for processing), in combination with the ‘open and free’ data policy of the ERS mission will drastically increase the demand for ERS data, ensuring the continuation of the mission beyond its active lifetime. Several areas of research require large-scale SAR data (hundreds of thousands of images) and processing capabilities, including:

—— the quantification of the impacts of climate change and the efficacy of solutions; —— studies of global earthquake cycles using the ERS‑1/ERS‑2 and Envisat SAR data archives; —— global volcano studies using ERS‑1/ERS‑2 and Envisat SAR data archives; and —— orbital and atmospheric noise in InSAR data inferred from the global ERS‑1/ERS‑2 and Envisat SAR data archives.

11.7e Th EO Information Services Sector

At the time of the launch of ERS‑1 in 1991, the commercial use of remote sensing satellites was focused on the sale of data and basic image products. Since then, the emphasis has changed from basic data to higher-level EO-based information services as the main types of remote sensing products provided. This trend has been accompanied by the emergence of many small, leading- edge companies across Europe that provide such services. These companies

38 History of ERS

often arise as start-ups from the university research and development sector, and are a constant source of innovation for new techniques and methods that find their way into commercial applications. Today, this industry has an estimated annual turnover in revenues of €650 million to €700 million, with the public sector still forming the largest source of demand (approximately 70% of revenues). The growth in revenues has been constant over the last two decades at around 8% per year. There has been no single breakthrough (or ‘killer app’), but continual improvements in the technical capabilities of these small companies, and an increasing awareness and acceptance of user communities of the benefits that EO services can deliver. The ERS has played a significant role in that development. It has led the way worldwide in the development of services based on radar, and it is estimated that 60% of all EO information services produced today are exploiting radar either entirely or in part. Some examples include oil- spill monitoring (used on an operational basis by the European Maritime Safety Agency, EMSA), ice mapping (used by national ice mapping agencies in Europe and Canada), and forest monitoring (where radar is an essential supplement to optical data for the cloud-covered regions of the tropics). But perhaps the most striking example is that of precision land motion monitoring using the technique of SAR interferometry. ERS has pioneered the development of this sector and led to a small but growing base of specialist companies in Europe that are continually expanding commercial activity with clients in both the public sector (e.g. local and national governments) and the private sector (e.g. oil & gas and civil engineering companies). Free access to ERS data has also been pivotal in developing the prospects for higher-level EO- based information services. It is planned to continue this policy with the next- generation GMES Sentinels, where the heritage and capabilities developed through ERS will come to full fruition.

11.8 ERs→Data Policy: Adapting to a Changing Environment

The ERS data policy, adopted in 1990, was based on an open and non- discriminatory approach and identifying ‘user categories’ with specific conditions in terms of pricing policy. This approach has gradually evolved to a new paradigm of an ‘open and free’ data policy, which was adopted in 2010. This evolution of the data policy was possible because of the IT mass market, which has allowed significant reductions in satellite data production costs; the internet, which has enabled data dissemination at very low cost; modern means of communication and collaboration (no fax any more); and the changing political environment for data sharing and global collaboration. An example of the evolution of the data policy leading to a paradigm change can be seen in the context of the GEO collaboration. Already in 2008, for the GEO Geohazard Supersites initiative, large-scale ERS and Envisat SAR data covering specific areas prone to severe earthquakes or volcanic eruptions were made ‘open and free’ to the Geohazard community applying the rules of ‘old’ data policy. The GEO Secretariat director, José Achache, was the Cat-1 PI and members of the global science community were GEO’s Co-PIs. Online access to ERS and Envisat line data was granted to each Co-PI, who had simply to sign the terms and conditions. Since 2008, tens of thousands of SAR products have been made available using ESA’s ‘virtual archive’, allowing the global community to download high-volume datasets at mass-market prices. These large datasets were compiled by UNAVCO4, which coordinated the demand for data from hundreds of co-PIs. Organising the demands of such

4 UNAVCO is a non-profit university-governed consortium that facilitates geoscience research and education using geodesy: www.unavco.org

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a very large group via one single interface greatly increased the efficient use of ESA resources. This setup meant that the production performed for one single user was fully accessible to several hundred others, thus significantly reducing costs while improving the service. The availability of time series data over certain observation areas over 20 years has also encouraged other data providers to contribute their data and to allow open & free access, but in line with their individual data policies. ESA, with its medium-resolution satellites and its open & free data policy, ensures an optimal outreach of remote sensing fostering further new research, applications and services that will benefit commercial and dual-use satellite missions.

11.9 Deorbiting Phase

After 16 years of successful operations, and a highly satisfactory output of scientific data and applications, the ERS‑2 satellite was deorbited in summer 2011 with the final passivation on 5 September 2011. The power system was the weakest point of the satellite, and already back in 2007 special measures were implemented to ensure that the satellite had enough power to recover from possible error conditions during eclipse periods. Even though ERS‑2 had the same platform as SPOT-1 and SPOT-2, which operated for more than 16 years, the problems with the ERS gyroscopes and the ageing power system supporting the SAR, an instrument with a very high power demand, were the main arguments for a controlled deorbitation in 2011. For this, ESA had to:

—— minimise the satellite reentry time (limit 25 years), in accordance with the Inter-Agency Mitigation Guidelines; —— free up the ‘busy’ orbits in the low-Earth orbit region (900–700 km); and —— deactivate the satellite once it reached its disposal orbit.

The ERS‑2 deorbitation strategy, considering hardware constraints and performance uncertainties, was developed under the leadership of ESTEC’s Post-Launch Support Office with the ‘historical’ development team and the ERS operations team. The zero-gyro operations strategy, which had been operated since 2001, could not be used for the deorbitation since the open-loop attitude control, relying on Near-Realtime data from the LRB station network, did not work with operational processing systems requiring a defined speed above ground. The strategy identified, minimising the risk of losing the satellite, was to maintain a Sun-synchronous orbit and to divide the deorbitation activities into three phases:

—P— hase 1: reducing altitude in a circular orbit using only one gyro having an optimal position for measuring the yaw angle. This gyro had already shown signs of degradation in the past, so the other gyro, which was still in perfect shape, was selected in case of problems for Safe Mode operations. This phase lasted three weeks.

—P— hase 2: further reducing altitude in a circular orbit, ensuring reentry well within 25 years. This phase lasted also three weeks, with the satellite reaching an altitude of 570 km.

—P— hase 3: exhausting the tanks, minimising the reentry time using a circular orbit and deactivating the satellite. For this phase a one-day repeat orbit at 570 km altitude was selected to allow a precise scheduling of the station network using ESA’s Kiruna and Maspalomas stations, and also the stations at Kourou, Troll in Antarctica, Hobart and Kumamoto. This phase lasted one week.

40 History of ERS

Figure 27. The last image acquired by ERS‑2, of the Rome area, 4 July 2011. (ESA)

The strategy was implemented via a detailed plan of activities, including manoeuvre performance calibration and new trajectory calculations, global station network activation, platform monitoring, etc., spanning a period of seven weeks. This plan was followed precisely, without any deviation, until the end. ERS‑2 is now flying in a circular orbit at about 570 km altitude; its reentry into the atmosphere, where it will burn up, will take 15–20 years depending on solar activity, well within the 25-year limit stated in the Inter-Agency Space Debris Mitigation Guidelines. The ERS‑2 satellite acquired data until 4 July 2011 (Fig. 27), with its last months of activity dedicated to a specific Ice Phase (3-day repeat orbit).

11.10E xchange with Users − ESA Earth Observation Symposia

Even before the launch of ERS‑1 many scientists and operational data users supported the ERS missions by participating in the definition phase, preparing the Calibration and Validation activities and developing retrieval algorithms. Many PhDs were earned in preparation for the ERS missions and during 20 years of operation. Since the beginning of the mission this community has gathered at symposia organised by ESA every three or four years to exchange the latest research findings but also to communicate their requirements so that ESA could tune its operations and services as the mission advanced, and address new challenges. The contributions to the symposia, in terms of the numbers of participants and presentations, increased steadily (see Fig. 28). The last ‘Living Planet’ symposium, held in Bergen in 2010, was the biggest since the launch of ERS‑1, with some 1200 participants from 44 countries presenting more than 1000 papers and posters.

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Figure 28. Participants and presentations at ESA user meetings, 2003–11. (ESA)

12. Conclusions: Lessons Learnt and Follow-on Steps

Over the 20 years of successful operations, the ERS programme made major advances and produced outstanding results, including:

—— The creation of archives of well calibrated, regularly reprocessed datasets that are invaluable for climate studies and other applications. They represent a major asset that is still to be fully exploited.

—— Demonstration of the operational capabilities and potential of ERS instruments such as the Wind Scatterometer and GOME, which has enabled their transfer to the operational Eumetsat MetOp series. It is important to recall the major role played by the ECMWF in this demonstration with the assimilation of ERS data into their global forecasting models, and in particular the contribution of Tony Hollingsworth, Director of Research at ECMWF.

—— The confirmation of Europe’s leadership in the domain of Earth observation from space with the development of well-calibrated active microwave sensors requiring the definition of innovative calibration techniques, the development of advanced technologies, the definition and implementation of scientific algorithms for data processing and interpretation, the development of industrial competence and expertise and of operational applications.

—— The reinforcement of the cooperation with the EU/European Commission and in particular the Joint Research Centre (JRC) at the Institute for Environmental Protection and Research (Ispra). The cooperation began in the early 1980s with joint airborne campaigns and the exploitation of ERS data for various projects. Within the Tropical Ecosystem Environment Observation by Satellites (TREES) project, for example, the first SAR map of equatorial forests of Central Africa was produced by Jean-Paul Malingreau (head of the MTV unit) and his team, which may be regarded as a first or early step towards GMES (the Baveno Manifesto establishing GMES was signed in May 1998).

—— The ERS mission can be regarded as an international cooperation tool that led to the establishment and negotiation of agreements for a global network of ground receiving stations. It encouraged regular exchanges of information on issues such as data formats, and on standards within the Committee on

42 History of ERS

Earth Observation Satellites (CEOS), a forum set up in 1984, through the exchange of data with third-party missions JERS‑1), etc.

—— Concrete collaboration in the development of applications and scientific research with the United States, China, Japan, India, , Australia, South Africa, etc., with international and European organisations (ECMWF, FAO, UNESCO, UNDCP, UNOOSA, WMO, etc.), with the worldwide scientific community, and with innovative SMEs.

—W— ith ERS, ESA (together with CNES and SPOT) played a major role in the creation of the International Charter for Major Disaster Management initiated in the aftermath of Hurricane Mitch, the most powerful of the 1998 hurricane season with ~300 km h−1 winds that caused ~20 000 fatalities.

—— Innovations such as the internet and the dramatic evolution of information technology have enabled an outstanding usage of the mission data, and have catalysed global collaboration in operations and advancement of science and services. Without this parallel evolution, fostered by the invention of the world wide web (another European success story), Earth observation would not have reached this level of maturity and would not now be part of the daily lives of citizens and businesses.

—— ERS, in particular as a non-active mission, will continue to be a pioneer for future satellite mission operations, collaborations and data exploitation schemes that will benefit from its data archive covering all areas of Earth science. Over two decades, it proved able to adapt efficiently to the changing environment, building on technical and structural heritage and partnerships.

—— ERS, supported by the open and free data policy, made significant contributions to innovative internet-based exploitation platforms with the potential to create new business opportunities for commercial companies and EO data providers, as well as for telecoms and IT industries.

In conclusion, one may say that the ERS programme has been a complete European success that paved the way for the Envisat, MetOp and Sentinel missions.

A cknowledgements

The authors wish to thank the thousands of engineers, technicians, scientists from ESA establishments and from European and Canadian industry who made ERS a reality and an outstanding worldwide success. Particular acknowledgements are addressed to Jan Burger, the first ERS‑1 Project Manager from 1984 to 1991 (when he unexpectedly passed away), and his successor, Reinhold Zobl, who successfully continued his work. The authors are also grateful to the Announcement of Opportunity Instrument Principal Investigators and their teams who enabled their instruments to fly on the ERS platform, namely David Llewellyn Jones and Chris Mutlow for the ATSR, John Burrows for GOME, Philipp Hartl for PRARE, and René Bernard and his successor, Laurence Eymard, for the Microwave Radiometer. Special acknowledgements go to the worldwide user community, whose members exploited the mission data with outstanding competence and dedication throughout the exploitation life cycle (research, applications, operational services), as well as to the ESA Member States, who have recognised the excellent teamwork and the good return on the investment in ERS, allowing the long-term continuation of the mission with Envisat and the Sentinels, and of the Scatterometer and GOME instruments on MetOp.

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R eferences

GCOS (2003). GCOS Climate Monitoring Principles. World Meteorological Organization, Geneva, Switzerland. www.wmo.int/pages/prog/gcos/ WMO (2010). Scientific Assessment of Ozone Depletion: 2010. Global Ozone Research and Monitoring Project, Report No. 52. World Meteorological Organization, Geneva, Switzerland. http://ozone.unep.org/Assessment_Panels/SAP/

In view of the thousands of publications and hundreds of services derived from the ERS mission, the following list includes only ESA publications that provide references to specific achievements:

ESA (1992). Space at the Service of our Environment. 1st ERS‑1 Symposium, Cannes, France. ESA SP-359, vols 1 & 2. European Space Agency, Noordwijk, the Netherlands. ESA (1993). Space at the Service of our Environment. 2nd ERS‑1 Symposium, Hamburg, Germany, vols 1 & 2. ESA SP-361. ESA (1994). First Workshop on ERS‑1 Pilot Projects, Toledo, Spain. ESA SP-365. ESA (1995). New Views of the Earth: Scientific Achievements of ERS‑1. ESA SP‑1176/I. ESA (1996a). New Views of the Earth: Applications Achievements of ERS‑1. ESA SP‑1176/II. ESA (1996b). Second ERS Applications Workshop, London, UK. ESA SP-383. ESA (1997a). Third ERS Scientific Symposium, Florence, Italy. ESA SP-414. ESA (1997b). New Views of the Earth: Engineering Achievements of ERS‑1. ESA SP 1176/III. ESA (1998). Further Achievements of the ERS Missions. ESA SP‑1228. ESA (2000). ERS–Envisat Symposium, Göteborg, Sweden. ESA SP-461. ESA (2004). Envisat and ERS Symposium, Salzburg, Austria. ESA SP-572. ESA (2007). Envisat Symposium, Montreux, Switzerland. ESA SP-636. ESA (2010). ESA Living Planet Symposium, Bergen, Norway. ESA SP-686.

44 →→TheTSR A Instruments → and What They Have Led To

ATSR

The ATSR Instruments and What They Have Led To

D.T. Llewellyn-Jones

AATSR Principal Investigator, Space Research Centre, Department of Physics & Astronomy, University of Leicester, Leicester LE1 7QR, UK

1. Introduction

The Along-Track Scanning Radiometer (ATSR) instrument was carried on the ERS‑1 satellite with the principal objective of providing global Sea- Surface Temperature (SST) measurements at the levels of accuracy, coverage, continuity and quality required by the climate research community. ATSR was an Announcement of Opportunity instrument, nationally funded by the UK, with additional contributions from Australia and France. It was followed by its two successor instruments, ATSR‑2 and AATSR, flown on the ERS-2 and Envisat satellites, respectively. The three ATSR instruments provided a near- continuous dataset of global SSTs at the level of accuracy needed by the climate research community, as well as high-quality data on land surfaces and, in the atmosphere, on aerosols and clouds. As an example of the completeness and the quality of the SST dataset from the ATSR sensors, Fig. 1 shows a Global SST Field, averaged for the month of January, from a new SST climatology derived exclusively from this 20-year dataset. It can be seen that there are still some areas of the global oceans where the quantity or quality of data were insufficient to enable a physically meaningful climatological average SST value to be derived. Nevertheless, all the main oceanographic features and currents can be seen with great clarity. Work is continuing to refine and complete this climatology (Corlett, 2011, private communication).

Figure 1. SST climatology for the month of January, derived from ATSR‑1, ATSR‑2 and AATSR SST data

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2. The Origin of the ATSR

In the late 1970s, it was recognised by the climate research community that there was an urgent need for accurate observations of global sea surface temperatures. The requirements were demanding, including the need for accuracy of better than 0.3°C, together with global coverage and continuous observations. Although it was recognised at the time that space observations could probably provide coverage and the continuity it was not widely believed that the required level of accuracy could be achieved. However, a consortium of scientists from four institutes – the Department of Atmospheric, Oceanic and Planetary Physics, University of Oxford; the recently formed Space Science Department, Rutherford Appleton Laboratory; the Mullard Space Science Laboratory (MSSL), University College London; and the UK Met Office − devised a scheme for achieving this level of accuracy by combining rigorous calibration procedures, advanced technology and a novel two-angle view of Earth’s surface in a single radiometric instrument. At about this time, ESA scientists were finalising their plans for the ERS-1 mission, the first polar-orbiting Earth-observing satellite. The envisaged payload was similar to that of the highly successful but unfortunately short- lived Seasat satellite that NASA had flown for just 100 days in 1978. The proposed ERS‑1 payload was an advanced version of its NASA predecessor and it included an ambitious Ocean Colour Monitor (OCM), which unfortunately had to be dropped from the planned payload on account of resource issues. In its place, ESA issued an Announcement of Opportunity (AO) for nationally provided instrumentation to fill this gap, but weighing less than 50 kg, consuming no more than 50 W of power and with a data rate limited to 200 kbit/s. In response to this AO, the ATSR instrument was proposed to ESA and, following a competitive evaluation, was accepted for flight on ERS‑1. It was duly built (Edwards et al., 1990) and launched on 17 July 1991 from the European launch facility at Kourou, French Guiana, on an Ariane 4 rocket. Figure 2 shows the cover of the ERS‑1 programme proposal that was presented to ESA’s Programme Board for Earth Observation for approval, together with

Figure 2. Top panels: Some stickers from the ERS era. Left: The first ERS-1 sticker designed for the ERS team at ESTEC. Centre: ATSR-1 sticker designed for the ATSR team at RAL, and the AATSR sticker designed by Sarah Llewellyn-Jones. Right: Another ESA sticker for the ERS-1 mission. Lower panels: The cover of the ERS‑1 programme proposal, which included sketches of the satellite fully deployed and inside the Ariane fairing (right).

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Figure 3. The launch of ERS‑1 on 17 July 1991 (left), and a close-up of the fairing from the same image (right) in which the umbilical cord to the satellite can be seen, still connected but about to part, signifying the birth of the ERS‑1 mission. The giant ERS‑1 sticker that decorated the fairing can also be seen.

sketches of the proposed satellite included the proposal. Figure 3 shows the launch of ERS-1, with a close-up of the fairing and of the sticker that the ERS-1 project team had designed for the occasion. Soon plans were afoot for the successor instruments, ATSR‑2 and AATSR. ATSR‑2, which was similar to ATSR‑1 but with the addition of visible and near- infrared channels (Read et al., 1991), was launched on the ERS-2 satellite in 1995, while AATSR (Llewellyn-Jones et al., 2001), with a higher data rate for more detailed data over land, was launched on Envisat in 2002.

3. Some Initial Results

Following its launch and commissioning, ATSR was soon producing very high- quality thermal and reflected infrared images of Earth’s surface. It was in some ways unfortunate that the launch of ERS‑1 coincided almost exactly with the massive eruption of the Mount Pinatubo volcano in the Philippines, during which considerable amounts of substantial particulate matter were injected into the upper atmosphere, where the prevailing stratospheric winds quickly distributed the dust, causing it to encircle the equatorial regions. Although this made validation of ATSR very difficult, it provided an immediate opportunity to demonstrate the power of the dual view, which was the most innovative hallmark of the ATSR observing system. Figure 4 illustrates the difference between AATSR’s two types of retrieved sea surface temperature: the SSTs retrieved from the generally used single view (nadir only) brightness temperatures, which was known to be seriously degraded by excessive atmospheric aerosol loading; and the other from ATSR’s novel dual view (forward and nadir view brightness temperatures combined, which prelaunch modelling studies had shown to be much less sensitive to contamination by excessive atmospheric aerosols than the nadir-only view. The difference between these two retrieved SST values was immediately observed on account of the Mount Pinatubo aerosols. The global plot in Fig. 4 shows the differences between these two SST values, averaged over a period of one month early in the ERS‑1 mission. Although the ATSR SST data were not fully developed or validated at this stage, it was clear that the innovative dual-angle viewing geometry would enable ATSR to generate SST values in the presence of very adverse atmospheric conditions. It can be seen from Fig. 4 that, throughout most of the mid-latitude regions, the difference between the two SST values is of the order of one- or two-tenths of a degree, but the difference is closer to 1°C throughout the tropics, where

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Figure 4. A plot from the early days of the ERS‑1 mission in 1991, showing the difference between SSTs calculated from dual-view observations and those from the nadir view only. The differences are due to the significant effects of the Mt Pinatubo aerosols on the nadir-only (AVHRR-style) estimates and the much less affected dual- view SST estimates, and provide a clear indication of the spatial distribution of the dust cloud which, at this early stage, was confined to tropical regions. The effects of an outburst of Saharan dust can also be discerned. (From Dundas, 1997)

the volcanic ash was known to be distributed during the months immediately following the eruption. This result immediately suggested that the difference between the dual- view and nadir-only view SST values might be used as a proxy for the level of atmospheric contamination. It can be seen from Fig. 4 that contamination, presumed to be from Mount Pinatubo’s aerosols, encircled the equator, as was already well known from other observations at the time. Figure 4 also shows, slightly to the north of the Atlantic tropics, what is possibly aerosol contamination due to a Saharan dust outburst, which is a regular occurrence. Although it took several years to achieve the levels of accuracy required by the climate research community, it was immediately clear from the Mt Pinatubo aerosol contamination that ATSR’s unique dual-angle viewing geometry had the potential to make consistent corrections for the effects of atmospheric aerosols.

4. Scientific Achievements of the ATSR Series 4.1 The Accuracy of AATSR

A comprehensive summary of ATSR’s scientific achievements over the oceans, land surfaces and in the atmosphere, and meteorological applications, can be found in the special issue of Remote Sensing of Environment (RSE), published in January 2012 (Llewellyn-Jones & Remedios, 2012). This special issue covered a wide range of scientific applications and demonstrated the high quality and versatility of ATSR data. This chapter highlights only a few of ATSR’s scientific achievements and the reader is referred to the RSE special issue and the rest of the scientific literature for further details and a wider review of ATSR’s achievements. Following many years of algorithm refinement, combined with rigorous validation programmes, the ATSR sensors met their objectives for the measurement of Sea-Surface Temperatures. A recent assessment of the levels of accuracy achieved, carried out by Corlett (2011, private communication), involved a careful comparison of AATSR SST values and in situ measurements from the global drifting buoy network. The result of an initial comparison, employing around 10 000 matchups, is shown in Fig. 5. The first aspect of this plot to note is that the agreement between the SST values and the coincident in situ observations appears to fit a Gaussian curve very well indeed; in fact it can be regarded as an exceptionally good fit for geophysical observations. Table 1 shows the level of agreement between AATSR SSTs and those from drifting boys when different statistical approaches are applied. The least mean deviation is obtained using so-called robust statistics, whereby exceptional outliers are disregarded from the daily analysis. This analysis leads to an estimated accuracy of less than one-tenth of a degree with

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Figure 5. The difference between the best AATSR SST retrievals, which were those using the dual-view 3-channel retrieval algorithm, plotted against quality- screened drifting buoy measurements for 10 000 matchups. (Corlett, 2011, private communication)

Table 1. Mean values and standard Method Mean value Standard deviation deviations characterising the distribution Simple arithmetical –0.09K 0.49K curve shown in Fig. 5, calculated by several 3-sigma –0.09K 0.35K generally accepted methods. Dual-nadir difference (GHRSST L2P1) –0.08K 0.37K Robust –0.08K (median) 0.33K Gaussian fit –0.07K 0.2K 1 Group for High-Resolution SST, Level 2P dataset.

a standard deviation of just over one-quarter of a degree. It is probable that a significant fraction of the standard deviation is due to variations in drifting boy observations because, for example, of varying water depths of the temperature sensors on account of the sea surface motion. With this level of accuracy clearly demonstrated, and with over 20 years of data collected, it is clear that the ATSR dataset has the potential to contribute to the climate record.

4.2 Time Series of Global SST

Having established a high level of accuracy and in order to use SST observations for investigating the behaviour of the global climate, it is an important starting point to monitor the average global temperature of the sea surface. Following a complex and detailed analysis of the dataset from the three ATSR sensors, taking into account all known effects and processes that might affect the accuracy of the

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Figure 6. Monthly global averages of the dual-view 3-channel night-time SST anomaly plotted over the time period between the launch of ERS-2 in 1995 and mid-2011. Note that there is a six-month data gap near the beginning of the record on account of a scan mirror anomaly in ATSR‑2. When this plot was created, the process of rigorous uncertainty analysis was still being carried out (Veal et al., 2012) and is therefore incomplete. For this reason, an estimated uncertainty is indicated, but this must be regarded as provisional. (University of Leicester)

retrieved SSTs, monthly global averages of observed SST have been computed for the duration of the three missions. A sample of the results is shown in Fig. 6. It should be emphasised that this is a preliminary calculation and, in Fig. 6, only the 3-channel dual-view data, which provided the most accurate SST values, are shown. Certain aspects of the data processing, such as the identification and screening of clouds in the field of view, are known to be less than optimal. For such reasons, data from ATSR‑1 have not been included in Fig. 6, mainly but not exclusively because that instrument lost the use of its powerful 3.7 µm thermal infrared channel at an early stage in the mission (in May 1992) with the result that, for the bulk of the ATSR‑1 mission, the most accurate SST retrievals could not be implemented, nor could the most efficient night-time cloud-identification scheme be used. Thus, continuous 3-channel dual-view data were not available until the launch of ATSR‑2 on ERS-2 in April 1995. The time series in Fig. 6, although it extends only from 1996 to mid-2011 shows strong variability. A detailed interpretation of these variations is beyond the scope of this chapter, but the effects of the substantial El Niño effect in 1997, as well as the strong reverse effect (La Niña) in 2008, are very pronounced and are perhaps the dominant features of this 15-year time series. Phenomena such as these underline the importance of obtaining long time series of global data in order to observe global changes without ambiguity. However, when all the known attributes of the data are taken into account, the SST values shown in Fig. 6 can be considered to have an uncertainty of approximately ±0.1K. It was mentioned above that certain aspects of the current SST data processing system are now known to be less than optimal. Many of these aspects have recently been further analysed and more fully taken into account in the course of a particularly important project, the ATSR Reprocessing for Climate (ARC). This project is led by C. Merchant of the University of Edinburgh and implemented by a consortium that includes the University of Edinburgh, the University of Leicester, the UK Met Office and the University of Southampton. As a result of this project, globally averaged SST data have been recalculated between 1993 and the present. There are important similarities and differences between the ATSR records and the Met Office Hadley Centre’s own in-situ-based analysis, HadSST3, a discussion of which is beyond the scope of this chapter. However, it is clear that the ATSR data have earned their place as a key component of this climate record. It is worth noting that in the course of the ARC project it has been established that, in the tropics at least, the ATSR SST record exhibits the stability of 0.03°C per decade (Merchant et al., 2012), which is well within the requirements previously set out by the climate research community. Such levels of accuracy and stability clearly demonstrate that the ATSR sensors certainly met their first objective.

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4.3 ATSR Data over Land

In addition to the accurate SST measurements, the ATSR was applied to measure the properties of land surfaces. The ATSR sensors were particularly well suited to measuring the radiative temperatures of land surfaces because the instruments were well calibrated radiometers, with channels selected to provide accurate atmospheric corrections in the thermal infrared and with a spatial resolution of 1 km. Although Land Surface Temperature (LST) is not classified by the climate community as an Essential Climate Variable (ECV), it has an important, even critical, role to play in the investigation of climate processes, particularly those involving soil moisture and the transpiration of water. Also, recent work by Ghent et al. (2010, 2011) has demonstrated that satellite measurements of LSTs have a positive impact on land surface characterisation models and could therefore play an important role in future weather and climate forecasting systems. Another possible application of LST data, especially in urban areas, is in the area of public health, where there is a growing awareness of the relationship between surface temperatures and the production of harmful polluting compounds in the lower atmosphere. Increased effort is now being devoted to optimising the LST data products generated from ATSR data. An example of an AATSR LST data product is an image of the UK during the exceptional heatwave of 2006, shown in Fig. 7. In order to make radiometric measurements of land surface temperature from space, it is a major challenge to characterise the land surfaces because they exhibit high variability in structure and, implicitly, in actual temperature values. The characterisation of land surfaces is thus one of the most challenging aspects of the data processing system. Also, on account of the inhomogeneities and varying topography of land surfaces, atmospheric behaviour is more heterogeneous and therefore the atmospheric correction problem is more complex than in most marine environments. In addition to this, the problem of unambiguous cloud identification over land surfaces is even more difficult, with no ‘magic bullet’ in sight at this time, making it perhaps the most challenging aspect of land-surface remote sensing. ATSR LST data products are the subject of ongoing research. The plot shown in Fig. 7 was produced in the course of the development programme and should be regarded as an experimental product. In 1995, ATSR‑2 was launched with additional channels in the visible and reflected infrared regions. These had the new objective of using the unique dual view to develop an atmospheric correction for the visible-wavelength reflectance measurements and the land cover data products that would follow. It had been shown that it was possible to use the dual-angle view as well as the multi-wavelength channels to infer Aerosol Optical Depth (AOD) and then correct for the presence of atmospheric aerosols over land. This meant that it would be possible to derive land surface properties from the visible channels of the ATSR instruments while utilising an atmospheric correction based on optical depth determinations that utilise the ATSR unique two-angle viewing geometry. Figure 8 shows some results from recent work (Bevan et al., 2011), where global maps of AOD and derived surface reflectance, inferred from the two-angle view in combination with a bidirectional reflectance model, can be seen. It should be noted that this technique can be used over both land and sea to produce the global plot shown in Fig. 8. This type of work opens up the possibility for a wide range of atmospherically corrected land surface products from the ATSR instruments.

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Figure 7. Land-Surface Temperatures over England, Wales and Scotland on 15 July 2006, during a heatwave, retrieved from AATSR data. Significant variations and spatial structure can be clearly observed. Some of the values are exceptionally high for a mid-latitude climate. The major conurbations, including London, Birmingham and Leicester, can be clearly identified on account of their higher surface temperatures.

Figure 8. Left: Aerosol Optical Depth (AOD) inferred from ATSR data using the two-angle view together with a bidirectional reflectance model. Right: Inferred land surface reflectance, obtained by using the AOD to correct the measured top-of-atmosphere brightness.

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4.4 ATSR Data and the Atmosphere

The processes of scattering, absorption and emission, by atmospheric aerosols, of infrared and visible radiation are not only relevant to atmospheric corrections for land surface reflectance measurements mentioned in the previous section, but they also provide the means to investigate the properties of atmospheric aerosols themselves. The ATSR instruments, with their well- defined infrared and visible spectral channels, together with ATSR’s two-angle viewing geometry, had considerable sensitivity to atmospheric aerosol content. It must nevertheless be recognised that the problem of retrieving comprehensive and accurate information about parameters such as particle size, particle content, particle density and spatial distribution present complex problems, both in the retrieval process and in validation of the data. This is largely because there are undoubtedly more variables than measurable quantities. However, there is an active community of strong research groups working to develop new aerosol products from ATSR data and this is seen as a major development area for the future. The universities and institutes contributing to this work include the University of Bremen, the University of Oxford, the Rutherford Appleton Laboratory, the Finnish Meteorological Institute, the DLR and the University of Swansea. Looking to the future, the development of new, robust and accurate aerosol products from ATSR data is certainly both a major challenge and an exciting prospect for the Sentinel‑3 mission.

4.5 The ATSR Fire Atlas

A space sensor that produces high-quality images of Earth’s surface on a regular basis is bound to have many applications, although reviewing every one of them is certainly beyond the scope of this chapter. One spectacularly successful application of ATSR data, which can claim tens of thousands of users, involved monitoring large fires and miscellaneous hotspots on a global basis. In the early 1990s ESA realised that ATSR was the only visible and near- infrared imaging sensor in space that was continuously producing global data at 1 km resolution, including data at 1.6 µm wavelength, which is particularly well suited to the high temperatures of fires. Thus, the ATSR Fire Atlas was created and has been operating continuously since 1996, and is now accessed by about 60 000 users each year. Figure 9 shows two examples of the types of observation made by ATSR: the existence of gas flares and the emergence of global trends.

Figure 9. Left: Gas flare monitoring from space using the ATSR instrument (Casadio et al., 2011; Arino et al., 2011). Right: Global time series of the number of hotspots observed each year, 1996–2010.

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4.6 ATSR Image Data

When ATSR started working, it immediately produced images of remarkably high quality, reflecting the excellent optical design and sensitive cooled detection system that the instrument uses. From the point of view of measuring sea surface temperature, the main function of the images was to identify clouds and land masses so that the processing system could screen them out, and also to monitor various aspects of data quality. It should be noted that the ATSR sensors produce two kinds of image at 1 km spatial resolution: namely, the generally familiar high-quality visible and near (reflected) infrared wavelength images, as well as thermal images, which reveal remarkable patterns of temperature variations that are invisible to the naked eye, forming the foundation of ATSR’s accurate SST retrievals. Examples of both types of image are shown in Figs 10 and 11, respectively. Apart from the attractiveness of the images and the general fascination they invoke, the ATSR image archive has an important role to play in environmental monitoring. This is because the orbits of ERS-1/ERS-2 and Envisat were highly stable for more than 20 years, and the ATSR sensors provided a global image dataset that was acquired with unique consistency of viewing angle and illumination angle throughout the three missions. This archive can be used to detect and monitor quite large-scale environmental changes in such parameters as ice coverage and the areas of lakes.

4.7 The Transition to Operational Status

Numerical Weather Prediction (NWP) requires a complete global SST field on a daily basis. To provide SST data in this way presents substantial practical and logistical challenges and, for that reason, analysis fields, based on available observations and on past observations in combination with sophisticated interpolation techniques, have generally been used for this purpose. Until recently, although it had always been envisaged that ATSR’s accurate SST observations would contribute to meteorological forecasting, there has

Figure 10. An unusually cloud-free view of the UK, acquired by AATSR during the early days of the Envisat mission, showing the potential of visible or near- infrared wavelengths for viewing the basic geographical features of the land. Note that the sea surface is uniformly dark, on account of the low reflectivity of water at visible wavelengths.

Figure 11. False colour image acquired by ATSR‑1, showing a view of the UK in the thermal infrared. The most striking aspect is the appearance of temperature gradients in the North Sea, in strong contrast with the image in Fig. 10. Over land, major cities can be identified as ‘urban heat islands’ and some of the geographical features also have visible thermal signatures.

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not been an available analysis field that includes ATSR data, for a variety of reasons principally to do with the method of delivery. The situation has changed recently on account of an ESA initiative to support the WMO’s Global Ocean Data Assimilation Experiment (GODAE), which set out a requirement that operational users of ocean data should be able to receive global data in an agreed format and on an appropriate timescale (typically within a day of acquisition) and, most important, with appropriate error and quality information for each data point provided. This requirement was clearly not within the scope of the standard Envisat ground segment and ESA, as a result, set up the ‘Medspiration’ project as part of their Data User Element (DUE) programme. At the same time, the GODAE High Resolution SST (GHRSST) project, since renamed the Group for High- Resolution SST was formed, with crucial ESA support. GHRSST coordinated the work of other SST data providers, including NASA, to ensure that data from other satellite sensors was available to operational users on the same basis as data from AATSR was provided through Medspiration. The availability of global SST data on a daily basis, tailored to the needs of operational users, presented a great opportunity for meteorological agencies to access a wealth of excellent SST data from space and other sources. As an example, the UK Met Office established a daily analysis scheme called the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA),1 from which the maps in Figs 12 and 13 were obtained. The OSTIA scheme, which uses optimal interpolation of data from seven satellite sources as well as in situ data, provides daily SST analyses field together with information regarding the accuracy and consistency of the various datasets involved. As an example, Fig. 12 shows a global SST analysis field from the OSTIA scheme. The spatial resolution of the final product is obviously less than that of the 1 km resolution satellite data from which it was compiled, because the daily and weekly data will always contain gaps because of coverage and cloudiness limitations, over which the analysis scheme has used interpolation techniques.

Figure 12. A global SST analysis field plotted for the week ending 24 September 2011. In this plot, data were obtained by six satellites plus in situ sources. The satellite sensors include three IR sensors, two microwave sensors and one geostationary IR sensor. (UK Met Office; Crown Copyright 2011)

1 See http://ghrsst-pp.metoffice.com/pages/latest_analysis/ostia.html for further details and up-to-date examples of data.

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Figure 13. The SST data from Fig. 12 plotted as anomaly fields by subtracting the NCEP analysis fields from the OSTIA data. (UK Met Office; Crown Copyright 2011)

The information content of these SST fields is greatly enhanced when they are plotted as anomaly fields. In Fig. 13, the corresponding National Centers for Environmental Prediction (NCEP) climatology field has been subtracted from the basic OSTIA SST field to provide the anomaly plot shown. It is interesting to note the very high anomalies that existed in the Arctic regions at that time. The OSTIA fields are of great potential interest to users of ATSR data because they are spatially and temporally complete, combining the advantages of coverage that the multiple sensors provide with the exceptional accuracy of the AATSR. In fact, AATSR SST values are used in the OSTIA analysis scheme along with the in situ data to derive bias corrections for the other satellite sensors. There can be no doubt that the incorporation of AATSR data into an important meteorological SST analysis scheme, on an operational basis, is one of the most significant developments in the application of ATSR‑type data, because it has overcome the shortcomings of coverage, which are important for mesoscale process studies. It also allows for an objective comparative assessment of AATSR’s very high data quality, as well as crossing the most challenging bridge in the transition from scientific to operational observing systems.

4.8 AATSR on Show

On account of its importance as an instrument for quantitative monitoring of a key climate parameter, the AATSR Engineering Model was included in an exhibition on ‘Atmosphere: Exploring Climate Science’ that opened in December 2010 at the Science Museum in London. Here, the AATSR instrument is on display for the first time, suspended in such a way that its Earth-facing side can be viewed from below, i.e. in its operational orientation, as seen in Fig. 14.

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Figure 14. The Structural/Engineering Model of the AATSR on display as part of the ‘Atmosphere: Exploring Climate Science’ exhibition at the Science Museum in London. The individual in the left panel has been included for scaling purposes.

Figure 15. Left: The exhibit on measurement techniques, with the AATSR Engineering Model suspended above and including a yellow Argo Float, as well a model of the ERS‑1 satellite lent by Chris Rapley, director of the Science Museum. Right: Noah, aged 3, and Freya, aged 5, viewing an interactive commentary on the AATSR.

In the Science Museum, the AATSR has been seen by thousands of visitors of all ages. AATSR is seen in the context of its continued contribution to the observations and measurements of climate properties, as illustrated in Fig. 15.

5. Summary and Conclusions 5.1 What ATSR has Shown Us

The first ATSR was conceived as the result of a strongly articulated need expressed by the scientific user community and translated into a practical solution by scientists and engineers, in response to an Announcement of Opportunity from ESA. ESA then showed its confidence in ATSR by accepting two successor instruments that were flown on ERS-2 and on Envisat. This was very important because of the amount of time needed to reach the necessary levels of data quality and general acceptance that AATSR enjoyed, and which qualified it to achieve pseudo-operational status. This process demonstrated the importance of strong coupling between the work of data users, those who are active and creative in the science of measurement processes and those who hold the key to the enabling technology that provides the necessary competitive edge (for example, the ATSR coolers and the blackbody reference targets), as well as the engineers and scientists in research institutes and in industry, who actually designed, built and tested

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the instruments. Not only were all these elements needed for a successful progression, but there must be natural and productive communication channels between parties. In addition, it must also be admitted that this success was also made possible by the massive advances in data handling and processing technologies during the ATSR missions and which were foreseen by the proposers but not yet available to them.

5.2 ATSR and the Future

There is little doubt that, over the past 20 years the ATSR sensors met their main objectives and there is still the clear prospect of further developments of substantial scientific progress, in the form of new data products, new applications and new discoveries. In addition, the AATSR data have found their way into the daily SST analysis fields used by some of the world’s major weather forecasting agencies. The next step must be to ensure that an ATSR‑type sensor is part of any future operational observing system that is used for meteorological applications. This will be achieved when the Sentinel-3 satellite is launched by ESA as part of the European GMES Space Element, because the payload will include a fourth along-track scanning radiometer. This is to be the Sea and Land Surface Temperature Radiometer (SLSTR), a new instrument designed by ESA, which features all the main characteristics of the current AATSR with substantial enhancements, including a significantly wider swath width and additional channels, some with higher spatial resolution, features that will be very much welcomed by users. This instrument will not only ensure the ATSR system of a place in the future operational observing infrastructure of Europe, but it will also, on account of these enhancements, open up new fields for science and applications. Thus, in addition to meeting its scientific objectives, the ATSR sensors will have also completed the transition from scientific to operational functionality, perhaps for the first time.

Acknowledgements

The substantial contributions of several organisations and, perhaps more crucially, of many individuals, were essential to the success of the ATSR series. Although it is not practical to mention them all here, a few that must be mentioned include:

—— UK Research Councils, which funded the early development and missions of ATSR‑1 and ATSR‑2; —— the UK Department of Environment (now the Department for Energy and Climate Change), which funded AATSR; —— many colleagues at RAL who designed, developed and built the first ATSR and who remain the prime centre of expertise regarding these instruments; —— my colleagues at the University of Leicester, whose efforts during the AATSR era spearheaded many of the achievements in the calibration and exploitation programmes of the ATSR sensors; —— many industrial partners who contributed to all parts of the programme; —E— SA, which had the confidence to accept ATSR for the first time and provided steadfast support over all the missions; and, in particular, —— the ERS project teams who designed, built and deployed ERS‑1, ESA’s first pre-operational polar-orbiting Earth observation satellite, at a time when it was the only such satellite available to the world’s EO community. They also sustained and consolidated the achievement by maintaining continuity of observations from 1991 onwards. Arguably, the main achievement of ERS‑1 has been to establish ESA as a world-leading agency in Earth observation.

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It is too difficult to list all the individuals who made crucial contributions, but it would, I believe, be appropriate to mention the contributions of Sir John Houghton. As a distinguished academic scientist, having done pioneering work in the development and use of atmospheric sounders in space, he later became Director-General of the UK Met Office. Most important for ESA, he was a particularly proactive chairman of the Earth Observation Advisory Committee (EOAC, now ESAC), and was the founding chairman of the IPCC Working Group 1. Together with the EOAC and ESA staff, he worked tirelessly and effectively to define and present the ERS‑1 mission in such a way that it was not only the optimum flagship for ESA’s applications programme, but also of immense scientific value. Without this work, it is unlikely that the ERS‑1 mission would ever have met the diverse aspirations of ESA’s Member States and won their full acceptance and enthusiastic support.

References

Arino, O., Casadio, S. & Serpe, D. (2011) Global night-time fire season timing and fire count trends using the ATSR instrument series. Rem. Sens. Environ. 113, 408–420. doi:10.1016/j.rse.2011.05.025 Bevan, S.L., North, P.R.J., Los, S.O. & Grey, W.M.F. et al. (2011). A global dataset of atmospheric aerosol optical depth and surface reflectance from AATSR. Rem. Sens. Environ. 116, 119–21078. doi:10.1016/j.rse.2011.05.024 Casadio, S. Arino, O. & Serpe, D. (2011) Gas flaring monitoring from space using the ATSR instrument series. Rem. Sens. Environ. 116, 239–249. doi:10.1016/j. rse.2010.11.022 Corlett, G.K. (2011). Private communication. Dundas, R.M. (1997): The use of ATSR data to measure the radiative properties of aerosol particles, PhD Thesis, University of Leicester, UK. Edwards, T., Browning, R., Delderfield, J., Lee, D.J., Lidiard, K.A., Millborrow, R.S., McPherson, P.H., Peskett, S.C., Toplis, G.M., Taylor, H.S., Mason, I.M., Mason, G., Smith, A. & Stringer, S. (1990). The Along Track Scanning Radiometer: Measurement of sea surface temperature from ERS‑1. J. Br. Interplanet. Soc. 43, 160–180. Ghent, D., Kaduk, J., Ardo, J., Remedios, J. & Balzter, H. (2010). Assimilation of land- surface temperature into the land surface model JULES with an Ensemble Kalman Filter. J. Geophys. Res. – Atmospheres 115, D19112. doi: 10.1029/2010JD014392 Ghent, D., Kaduk, J., Remedios, J. & Balzter, H. (2011). Data assimilation into land surface models: the implications for climate feedbacks. Int. J. Rem. Sens. 32(3): 617–632. Llewellyn-Jones, D.T., Edwards, M.C., Mutlow, C.T., Birks, A.R., Barton, I.J. & Tait, H. (2001). AATSR: Global-change and surface-temperature measurements from Envisat. ESA Bull. 105, 10–21. Llewellyn-Jones, D.T. & Remedios, J.R. (2012). The Advanced Along Track Scanning Radiometer (AATSR) and its predecessors ATSR‑1 and ATSR‑2. Introduction to the special issue on AATSR. Rem. Sens. Environ. 116. doi: 10.1016/j.rse.2011.06.002 Merchant, C.J., Embury, O., Rayner, N.A., Berry, D.I., Corlett, G.K., Lean, K., Veal, K.L., Ken, E.C., Llewellyn-Jones, D.T, Remedios, J.J. & Saunders, R. (2012). A 20 year independent record of sea surface temperature for climate from Along-Track Scanning Radiometers. J. Geophys. Res. 117, C12013. doi: 10.1029/2012JC008400 Read, P.D., Hardie, A.L., Magraw, J.E., Taylor, H.S. & Jelley, J.V. (1991). A Calibration system, using an opal diffuser, for the visual channels of the Along-Track Scanning Radiometer, ATSR‑2. Proc. 10th IOP Symp., IOP Conf. Ser. 121, 207–213. Veal, K. et al. (2013). A time series of mean global skin SST anomaly using data from ATSR-2 and AATSR. Rem. Sens. Environ. RSE-D-12-00191R2

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→→ERS A and tmospheric Composition Measurements: → GOME and the SCIAMACHY Project

GOME and SCIAMACHY

ERSA and tmospheric Composition→ Measurements: GOME and the SCIAMACHY Project

J.P. Burrows

Institute of Environmental Physics, University of Bremen, Postfach 330440, 28334 Bremen, Germany. [email protected]

1. Introduction

This chapter describes the evolution and development of the SCIAMACHY project, and its spin-off, the GOME instrument, which was part of the core payload on ERS‑2. GOME measured successfully from 1995 to the decommissioning of ERS‑2 in July 2011. Figure 1 shows the GOME instrument during testing at the Officine Galileo in Florence, Italy, and a schematic diagram of GOME’s optical components. The ERS‑2 satellite was tested at ESA’s European Space Research and Technology Centre (ESTEC) in Noordwijk, the Netherlands, prior to being shipped for launch from Europe’s Space Port in Kourou, French Guiana, in 1994. The material in this chapter was presented at the symposium to celebrate 20 years of ERS in September 2011, and is structured as follows. Sections 2 to 4 describe briefly the evolution of Earth’s atmosphere, the physical structure and chemical processes that determine its composition and the impact of man on the Earth system. Section 5 describes key aspects of the history of remote sensing from space. In section 6 the development of European remote sensing and the need for global measurements of atmospheric composition, the evolution of remote sensing prior to the ERS programme and the European component. With increasing globalisation and urbanisation, coupled with an increasing population, the need for this information is greater now than it was 30 years ago. Sections 7−10 discuss some of the highlights of the development of SCIAMACHY and its spinoff, GOME, the Phases C and D, the GOME instrument and the contributions of some of the scientists, engineers and administrators involved in this remarkable achievement of creating a unique atmospheric composition observing capability. The excellent performance of GOME on ERS-2 in orbit is documented in section 11. The development of the retrieval

Figure 1. Left: The GOME instrument during testing at Officine Galileo, Florence, Italy, showing the scan mirror and four spectral channels. Right: Schematic diagram of the optical layout of GOME. (Officine Galileo)

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algorithms to produce the GOME data products is explained in section 12. Section 13 focuses on some of the key scientific applications and discoveries made using the trailblazing experiment of GOME on ERS-2 and the follow-up using GOME, SCIAMACHY and GOME‑2 data. Some of the challenges facing Europe in establishing its contribution to a space segment providing adequate global measurements of key trace constituents and parameters are addressed in section 14. Finally, my conclusions and a summary of some of the issues involved in the development GOME, and highlights of some GOME successes are provided in section 15.

2.e Th Evolution of Earth and its Atmosphere

When Earth formed ~4.5 billion years ago its early atmosphere was in physicochemical equilibrium, with carbon dioxide, CO2, as the most abundant gas. Life began ~1 billion years later, and since then has evolved and adapted. During the Achaean eon, primitive anaerobic organisms thrived, utilising sulphate for their energy needs, and their descendants, known as extremophiles, still survive. However, the emergence of cyanobacteria, or blue- green algae, which use sunshine, water and CO2 to produce carbohydrates and molecular oxygen, O2, changed Earth’s atmosphere. Approximately 2.45 billion years ago, evidence that oxygen was becoming an important component of Earth’s atmosphere was identified through changes in the isotopic ratio of sulphur in rocks (Farquhar et al., 2000). Around the same time, oxidised iron began to appear in ancient soils and bands of iron were deposited on the seafloor, the latter being attributed to reactions with oxygen in seawater. The release of molecular oxygen by photosynthesis to the atmosphere resulted in feedback between the biosphere and the atmosphere. The atmospheric composition in any geological era is determined by biogeochemical photochemical processes, coupled with atmospheric dynamics. Plants evolved and incorporated symbiotic cyanobacteria, known as chloroplasts, to undertake photosynthesis. The release of O2 results in the production of ozone, O3, which absorbs biologically damaging short-wavelength ultraviolet radiation in the region known as UV-B. In addition, the biospheric modulation of atmospheric composition provided conditions suitable for the emergence of new life forms. Land plants are considered to have evolved from the charophycean lineage of green algae. The first plants, liverworts and mosses, had probably evolved by about 700 million years ago and land fungi by about 1300 million years ago. This is earlier than previous estimates of around 480 million years, which were based on the earliest fossils of those organisms. The recent examination of Namibian fossils also extends the first appearance of animals to around 800 million years ago. The genus Homo appeared only about 2.3−2.4 million years ago and evidence of the species Homo sapiens has been found from approximately 0.2 million years ago. At the Neolithic revolution, roughly 7000−10 000 years ago, human society made the transition from hunting and gathering to establishing settlements and agriculture. The population at that time is estimated to have been approximately 4 million. Although fluctuating as a result of disease, natural disasters and catastrophes, this population grew to 1 billion by 1800 AD. The sources of energy fuelling this growth and being used for domestic heating, agriculture and early industrial processes, was primarily biomass, but also some energy was obtained from coal as well as wind and water power. The earliest references to the use of coal date back to the ancient Greeks, and coal and oil were being used in China and Europe in the Middle Ages. Following the industrial revolution around 1800 AD, powered by the use of fossil fuel, the pace of changes in atmospheric composition and land use accelerated and both the human population and its standards of living increased almost exponentially. Anthropogenic activities have now impacted

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all aspects of the Earth system, with pollution at all scales from local to global. Within a geologically very short period of 200 years, the human population grew from 1 to 7 billion in 2011. To put this growth into context, it took until about 1925 for the global population to reach 2 billion, but only 20 years, since the launch of ERS‑1 in 1991, to add another 2 billion.

3. Physical Structure and Chemistry of the Atmosphere

Earth’s atmosphere comprises the gases released from its surface and produced by photochemically induced reactions. After Earth was formed the temperature of the surface was sufficiently high that gases and particles outgassing from the surface had sufficient kinetic energy to escape the gravitational field. After Earth cooled sufficiently, an atmosphere in geochemical equilibrium was formed, with a chemical composition that was very different from that of the present atmosphere: the latter being maintained in an evolving biogeochemical stationary state by the presence of life. Life on Earth produced reactive O2, which was released by bacteria. O2 does not have sufficient kinetic energy within the atmosphere to escape from Earth’s gravitational field and thus started to build up in the atmosphere. The photolysis of O2 protects Earth’s surface from short-wavelength UV solar radiation. This photodissociation results in the production of ozone, O3, in the upper atmosphere, which absorbs strongly UV radiation between 200 nm and 320 nm and weakly at longer wavelengths. Some molecular nitrogen, N2, is geological in origin, being emitted from volcanoes or outgassed from the mantle and crust. However, life also results in the bacterial decomposition of ammonium nitrate, NH4NO3, in soils to produce predominantly N2, some nitrous oxide, N2O, and nitric oxide, NO. The latter is also produced in biomass burning where ionic processes and temperatures in flames enable the equilibrium between 2 N and O2 to produce significant amounts of NO. N2 and N2O are chemically long lived in the lower atmosphere, where they are transported by convection and advection and are mixed by both turbulence and diffusion. In the upper atmosphere they are photodissociated by short- wavelength solar radiation. In this sense they provide a first protective layer against biologically damaging radiation. The atmospheric temperature and pressure as a function of altitude are shown in Fig. 2. Temperature is expected to fall with altitude in any planetary atmosphere, as a result of adiabatic expansion. This is the case in the troposphere, which, as a result of convection and advection, is well mixed. The O3 absorption heats the atmosphere creating a temperature inversion layer, which begins at the tropopause and is called the stratosphere. At the stratopause the heating by O3 absorption becomes negligible and the temperature in the mesosphere falls once more with altitude. At the mesopause, shortest-wavelength electromagnetic radiation from the Sun is absorbed by atoms and molecules. As a result the temperature rises in the thermosphere and the composition is no longer well mixed. The mesosphere marks roughly the separation between the heterosphere and the homosphere where the bulk composition of air is constant, comprising ~78% N2 and ~21% O2.

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Figure 2. Atmospheric conditions in the mid-latitudes, using the US Standard Atmosphere defined in 1976. The tropopause is shown for mid-latitude conditions and the cumulonimbus cloud for topical conditions where the tropopause is at around 16 km. (WMO, 2010)

3.1 Thermospheric and Mesospheric Chemistry

The thermosphere is heated by the absorption of short-wavelength solar radiation and the reaction of coronal mass ejections impacting in this region. These interact with the upper atmosphere. In addition, meteoritic dust enters the upper atmosphere. In the thermosphere, the photodissociation of O2 (λ < 214 nm) and N2 (λ < 100 nm) yield atoms that are in turn photoionised to ions and electrons. The result is a soup of ions, electrons, free radicals and molecules. The emissions from the upper atmosphere have been observed by GOME on ERS‑2, in particular, emissions of the metal atoms in the mesosphere.

3.2e Th Stratosphere and Stratospheric Ozone

Ozone plays a number of important roles in the stratosphere. By absorbing biologically damaging UV radiation, including UV-B (280−390 nm), O3 protects the biosphere and so was important in the evolution of life. The UV-B radiation at the surface is absorbed by DNA, leading to mutations and cancer. By absorbing the UV radiation the stratosphere warms causing the temperature inversion known as the tropopause. The height of the tropopause influences

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the weather in the troposphere, and thus the aviation industry. In the past 40 years it has been established that humans have influenced the amount and distribution of stratospheric O3 in a number of ways. Following the discovery of what turned out to be O3 around electrical discharges by van Muren in the 18th century and its identification and naming by Schönbein in 1839, significant progress was made during the late 19th and early 20th centuries with the identification 3O in the atmosphere. In 1879, Cornu observed a sharp cutoff around 300 nm in the UV solar spectrum. Subsequently, Hartley measured the O3 absorption cross section in the laboratory and recognised that the atmospheric UV cutoff is produced by the presence of ozone. The existence of a temperature inversion layer in the stratosphere was discovered independently by French meteorologist Teisserenc de Bort and German meteorologist Assmann in 1902. Huggins reported several absorption bands in the observed spectrum of the star Sirius between 320 nm and 360 nm in 1890. In 1913 Strutt (Lord Rayleigh) showed that the UV absorption does not happen in the lower atmosphere. In 1917, the bands identified by Huggins were attributed to the presence of ozone in the atmosphere by A. Fowler and R.J. Strutt. In 1920 at Marseilles, France, Fabry and Buisson used UV absorption properties to deduce that the thickness of atmospheric O3 was of the order of 3 mm STP, later to become 300 Dobson units. Our understanding of the global nature of atmosphere began with measurements of O3 in the stratosphere by Dobson and his contemporaries in the 1920s. This led to our understanding of the dynamics of the upper atmosphere, dominated by Brewer–Dobson circulation. Similarly, the production and loss of O3 was a matter of much debate. In 1929 Chapman explained the maximum of O3 by the competition production and loss of O3. The production of O3 in the upper atmosphere proceeds by the photolysis of O2 and the molecular reaction of oxygen atoms with O2,

O2 + hν (λ < 242 nm) " O + O (1) O + O2 + M " O3 + M (2)

This is followed by the loss of O3 via photolysis and the proposed ‘odd-oxygen’ reaction between O and O3:

1 O3 + hν (λ < 411 nm) " O( D) + O2 (3a) 3 + hν (λ < 1180 nm) " O( P) + O2 (3b) O + O3 " O2 + O2 (4)

Over the next 50 years the evolution of reaction kinetics enabled exact measurements of the elementary reaction rate coefficients. The rate of reaction (4) was found to be too slow to explain the loss of O3 in the upper atmosphere. Following the observation of emissions of excited OH by Meinel (1950), which is explained by the reaction of hydrogen atoms, H, with O3, Bates & Nicolet (1950) recognised that reactions of HOx might catalytically destroy O3. This introduces the generic odd oxygen loss mechanism, which is shown below:

Ozone catalytic destruction cycle 1-L:generic ‘odd oxygen’ cycle:

X + O3 " XO + O2 (5) XO + O " X + O2 (6) ______

Net: O + O3 " O2 + O2 where X is either a hydrogen atom, H, or the hydroxyl radical, OH.

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Ozone catalytic destruction cycle 1a-L: HOx ‘odd-oxygen’ cycle:

H + O3 " OH + O2 (7) OH + O " H + O2 (8) ______

Net: O + O3 " O2 + O2

Ozone catalytic destruction cycle 1b-L: HOx ‘odd-oxygen’ cycle:

OH + O3 " HO2 + O2 (9) HO2 + O " OH + O2 (10) ______

Net: O + O3 " O2 + O2

It was subsequently shown that NO, chlorine atoms, Cl, bromine atoms, Br, or even iodine atoms, I, are effective catalysts for this ‘odd-oxygen’ catalytic cycle 1L. The HOx (= ΣH +OH + HO2 ) cycle largely determines the loss of O3 in the mesosphere and to a lesser extent in the stratosphere. In the late 1960s, both Harold Johnston and Paul Crutzen recognised the potential importance of emissions of oxides of nitrogen from high-flying supersonic aircraft proposed at the time (Johnston, 1971, 1974; Crutzen, 1970, 1973). Crutzen further proposed a global odd-oxygen catalytic cycle involving oxides of nitrogen (X = NO) destroying O3. There are two sources of NO in the stratosphere: the first being transport down from the upper atmosphere, where it is produced by the interaction of short-wavelength solar radiation with the bulk atmospheric constituents. The second is the reaction of N2O: photolysis 1 and reaction of excited oxygen atoms, O( D), produced in the photolysis of O3 reaction (3):

1 O( D) + N2O " NO + NO (11a) " N2 + O2 (11b)

NO in the atmosphere is also produced by ion–molecule reactions in the upper atmosphere and by lightning. At the high temperatures that occur in plasmas, and jet and internal combustion engines, the equilibrium reaction of N2 and O2, as expected from Le Chatalier’s principle, is shifted to the right-hand side and produces NO.

N2 + O2 D NO + NO (12)

Currently the NOx odd-oxygen cycle is considered to determine approximately 70% of O3 loss in the stratosphere. The N2O is released at the surface by the bacteriological reduction of nitrate ions, NO3–, and the oxidation of ammonium ions, NH3+. The importance of N2O, whose atmospheric loading, as result of the dramatic increase in the use of fertilisers since the development of the Haber– Bosch process, both as a greenhouse gas and as a source of stratospheric NOx, is well recognised.

Ozone catalytic destruction cycle 1c-L: NOx ‘odd-oxygen’ cycle

NO + O3 " NO2 + O2 (13) NO2 + O " NO + O2 (14) ______Net: O + O3 " O2 + O2

The potential for a chlorine catalytic chain reaction in removing O3 was recognised by Stolarski & Cicerone (1974), who studied the impact of Space Shuttle exhaust on the upper atmosphere. However, Molina & Rowland (1974) proposed that the

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release of long-lived chlorofluorocarbon compounds (CFCs) in the troposphere, their transport to the stratosphere and the subsequent release of chlorine atoms, Cl, which then participate in the catalytic odd-oxygen ClOx cycle removing O3. Since the industrial production of CFCs began in the 1930s their concentrations in the stratosphere had increased exponentially. Further, it was recognised by Wofsey et al., (1975) and others that bromine atoms, Br, released from the halons used in fire extinguishers, and methyl bromide, CH3Br, would also destroy O3 in an efficient catalytic BrOx cycle. The international concern about the release of halogens into the stratosphere eventually led to the introduction of controls on the production of CFCs and other O3-depleting species. The appearance of the phenomenon now known as the ozone hole – i.e. very low values of the lower stratospheric O3 column in the polar vortex over Antarctica in spring – was entirely unexpected, and a game changer. This was first reported from ground-based observations by British Antarctic Survey scientists at the Halley Bay station in 1985 (Farman et al., 1985). Some indication of this behaviour from one season’s measurements had been reported by Shigeru Chubachi, a Japanese scientist at an ozone symposium around the same time. The global O3 observations from NASA satellite instruments, in particular the Solar Backscattered Ultraviolet (SBUV) spectrometer and Total Ozone Mapping Spectrometer (TOMS) flown on Nimbus‑7, showed the magnitude, extent and duration of this phenomenon, which begins in winter and extends long into spring. They also showed the influence of the stratospheric quasi-biennial oscillation, a tropical dynamical feature, on the development and size of these holes. After much international scientific endeavour, with aircraft campaigns led by NASA and NOAA, it was established that the destruction of stratospheric O3 at high latitudes in the austral spring was the result of a complex physicochemical mechanism involving CFCs. During autumn at high latitudes a dynamical feature called the polar stratospheric vortex spins up. This air mass has high potential vorticity and is therefore effectively separated to a first-order approximation from lower-latitude air masses and cools, enabling Polar Stratospheric Clouds (PSCs) to form. During these and subsequent international expeditions, it was established that these PSCs are complex mixtures. Sulphuric acid tetrahydrate, H2SO4.4H2O, forms at the low temperatures present in the polar vortex and these particles act as condensation nuclei for nitric acid trihydrate, HNO3.3H2O and ice, which are stable at temperatures below ~194K and ~185K, respectively, under the conditions present in the lower stratosphere. The mechanism of producing the ozone hole by processing on the PSC is complex and explained briefly below. During daylight in the lower stratosphere where oxygen atoms react preferentially with oxygen molecules to form O3, nitric oxide and nitrogen oxide participate in a ‘do-nothing’ cycle comprising reactions (13), (15) and (2):

Ozone and oxygen atom exchange cycle: ‘do-nothing’

NO + O3 " NO2 + O2 (13) NO2 + hν (λ < 390 nm) " NO + O (15) O + O2 + M " O3 + M (2)

NO2 reacts slowly with O3 to form NO3, which in daylight is photolysed in the visible:

NO2 + O3 " NO3 + O2 (16) NO3 + hν (λ < 580 nm) " NO2 + O (17a) + hν (λ < 7320 nm) " NO + O2 (17b)

Lovelock & Maggs (1972) had discovered that CFCs, in particular CFC-11, CF3Cl, were present in the troposphere in amounts similar to those produced industrially. Stolarksi & Cicerone (1974) recognised that chlorine could

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also catalyse O3 in an odd-oxygen cycle. However, Molina &Rowland (1974) proposed that the CFCs released in the troposphere were a new source of Cl in the stratosphere and would deplete O3. In the unperturbed stratosphere outside of the vortex chlorine and bromine atoms are produced as a result the release of, for example, CFC-11, CF3Cl, CFC-12, CF2Cl2, halon 1211, CF2BrCl, and methyl bromide, CH3Br, and in the meantime HCFCS, their transport to the stratosphere and subsequent photolysis:

CF3Cl + hν (λ < 300 nm) " CF3 + Cl (18) CF2Cl2 + hν (λ < 300 nm) " CF2Cl + Cl (19) CH3Br + hν (λ < 548 nm) " CH3 + Br (20) 1 O( D) + CF3Cl " CF3 + ClO (21) 1 O( D) + CF2BrCl " CF2Cl+ BrO (22)

The Cl and Br atoms then participate in the following catalytic destruction cycles for stratospheric O3:

Ozone catalytic destruction cycle 1d-L: ClOx ‘odd-oxygen’ cycle:

Cl + O3 " ClO + O2 (23) ClO + O " Cl + O2 (24) ______

Net: O + O3 " O2 + O2

Ozone catalytic destruction cycle 1e-L: BrOx ‘odd-oxygen’ cycle:

Br + O3 " BrO + O2 (25) BrO + O " Br + O2 (26) ______

Net: O + O3 " O2 + O2

Outside the vortex these atoms account for approximately 30% of the O3 removal. These mechanisms currently remove about 6% of the total O3 at mid-latitudes in the upper stratosphere and lower mesosphere. It is interesting to note that the BrOx cycle has a chain length about a factor of 10 or more longer than that for the ClOx cycle. As the industrial use of the cheaper CFCs was much greater than the halons and methyl bromides, this results several parts per billion of Cl but only 10–20 parts per trillion by volume of Br compounds. This turns out to be fortunate, because if as much bromine had been released to the stratosphere by anthropogenic activities as chlorine, then the O3 stratospheric layer would then have been reduced globally by a factor of 10 or more. This would have had far more dramatic consequences for the atmosphere and Earth’s surface, as well as much larger doses of UV-B to the biosphere and humans. At night, the equilibrium reaction (27), which is strongly temperature dependent in the polar vortex, results in the formation of acid anhydride of nitric acid, dinitrogen pentoxide, N2O5:

NO3 + NO2 + M D N2O5 + M (27)

During the growth of PSCs, complex heterogeneous reactions take place on and in liquid and ice surfaces. These involve the temporary reservoir compounds for the nitrogen and halogens such as N2O5 hydrogen chloride, HCl, chlorine nitrate, ClONO2 and hypochlorous acid, HOCl. The actual mechanisms involve the accommodation of species at the particular surface, diffusion across the interface, diffusion through the liquid or solid PSCs, and then reactions within the PSCs and the subsequent diffusion of products to the surface, diffusion across the surface and release back to the atmosphere. For simplicity, these

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complex elementary processes can be summarised by the following acid- catalysed exothermic equilibrium reactions:

N2O5g + H2Ol D 2HNO3l (28) ClONO2g + H2Ol D HNO3l + HOClg (29) HOClg + HCll D CL2g + H2Ol (30) ClONO2g + HCll D Cl2g + HNO3l (31) ClONO2g + H2Ol D ClONOg + HOCll (32) HOBrg + HCll D BrClg + H2Ol (33) BrONO2g + HCll D BrCl2g + HNO3l (34) BONO2g + H2Ol D HNO3l + HOBrg (35) HOBrg + HBrl D Br2g + H2Ol (36)

The aerosol and PSC surfaces provide opportunities for chemical reactions, bringing together chemical species for sufficient time that chemical reactions take place, which do not occur rapidly enough during a gas phase collision. The result, at the end of winter, is that the polar vortex in the southern hemisphere is populated by PSCs and photolabile halogen compounds – molecular chlorine, Cl2, bromine chloride, BrCl, and to a lesser extent molecular bromine, Br2, and chlorine nitrite, ClONO – which are then photolysed at sunrise in the polar vortex:

Cl2 + hν (λ < 493 nm) " Cl + Cl (37) BrCl + hν (λ < 548 nm) " Br + Cl (38) Br2 + hν (λ < 620 nm) " Br + Br (39)

The Cl and Br atoms released participate in catalytic destruction cycles of the type:

Ozone catalytic destruction cycle 2a-L: ClO dimer

2x(Cl + O3 " ClO + O2) (23) ClO + ClO + M D Cl2O2 + M (40) Cl2O2 + hν (λ < 620 nm) " Cl + ClOO (41) ClOO + M D Cl + O2 + M (42) ______

Net: 2O3 " 3O2

Ozone catalytic destruction cycle 2b-L: BrO ClO

Cl + O3 " ClO + O2 (23) Br + O3 " BrO + O2 (25) BrO + ClO " Cl + Br + O2 (43a) ______

Net: 2O3 " 3O2

Ozone catalytic destruction cycle 2c-L: BrO

2x(Br + O3 " BrO + O2) (25) BrO + BrO " Br + Br + O2 (44) ______Net: 2O3 " 3O2

The reaction (43) between ClO and BrO has several channels:

BrO + ClO " OClO + Br (43b) BrO + ClO " BrCl + O2 (43c)

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The OClO, which is itself rapidly photolysed, can be used to detect the activation of halogens in the vortex:

OClO + hν (λ < 433.9 nm) " O + ClO (45a) OClO + hν (λ < 4483 nm) " Cl + CO2 (45b)

An additional relatively slow catalytic destruction cycle for O3 by HOx is feasible in the lower stratosphere and troposphere.

Ozone catalytic destruction cycle 2d-L: HOx

OH+ O3 " HO2 + O2 (9) HO2 + O3 " OH + 2O2 (46) ______

Net: 2O3 " 3O2

The above processes are shown schematically in Fig. 3.

Figure 3. Schematic diagram of the production and catalytic destruction of stratospheric and mesospheric O3, showing the gas phase odd- oxygen catalytic destruction cycles and the perturbed chemistry involving heterogeneous and multiphase reactions on polar stratospheric clouds, the release of photolabile halogen compounds, their photolysis and the catalytic destruction of O3 cycles in the gas phase in the absence of NOx. (J.P. Burrows and S. Noel, University of Bremen)

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In the stratosphere the reactions between the different free radicals produce temporary reservoirs by gas phase in addition to the multiphase reactions (28) to (36):

OH + NO + M " HONO + M (47) OH + NO2 + M " HNO3 + M (48) Cl + CH4 " HCl + CH3 (49) ClO + NO2 + M " ClONO2 + M (50) 2NO2 + H2O " HONO + HNO3 (51)

The HONO and ClONO are both rapidly photolysed during the day in both the stratosphere and troposphere:

HONO + hν (λ < 579 nm) " OH + NO (52) ClONO + hν (λ < 1498 nm) " ClO + NO (53)

Based on the measurements of changing stratospheric O3, scientific studies of its origins and rising public concern, the Vienna Convention for the Protection of the Ozone Layer was agreed in early 1985, prior to the discovery of the ozone hole above Antarctica. The parties to the convention, which is implemented by UN Environment Programme (UNEP) and the World Meteorological Organization (WMO), then signed the Montreal Protocol in 1987, which banned the production of ozone-depleting substances (ODSs). The Montreal Protocol has very effectively reduced the production and release of CFCs and other ODSs, although the long lifetimes of these substances in the troposphere mean that levels of stratospheric halogens will not fall to pre-1980 levels before 2040. Since the decision to include GOME on ERS‑2, the ozone hole above Antarctica has developed and is now very stable. In the northern hemisphere the polar vortex is not as strong or as long lasting. However, GOME was able to measure the first and second clear ozone holes over the Arctic in 1997 and 2011. They occur when the dynamical conditions are suitable and produce cold, long-duration polar vortices in the northern hemisphere. One of the current issues in stratospheric science is the changing dynamics produced by a changing climate resulting from both natural phenomena and anthropogenic activities. Changing the circulation and the composition of the stratosphere has large potential consequences, which is why we need measurements of the total column amount and vertical profile of 3 O . The GOME observations of the total column O3 averaged for the month of March are shown in Fig. 4 for the two very large chemical depletions of O3 observed thus far in the polar vortex above the northern hemisphere in 1997 and 2011. These springtime ozone holes occurred in a cold stratosphere with stable polar vortices that lasted into March. They are explained by the chemistry and dynamics outlined above. From the above, it can be seen that climate change (Houghton et al., 1992, and subsequent IPCC assessment reports) is likely to have an impact on stratospheric O3. It had already been noted by Tuck and co-workers (Groves et al., 1978; Groves & Tuck, 1979) that the cooling of the stratosphere predicted for increasing atmospheric CO2 would be offset to some extent by an increase in the rate of production of O3 as reaction (2) has a negative temperature dependence, a phenomenon now termed the super-recovery of ozone. However, changes in the dynamics, coupled with changes in CH4, N2O and H2O, will also impact. GOME and other data have provided evidence of changes in the Brewer–Dobson circulation. It is early days in the study of this phenomenon and its consequences are not yet clear. The importance of the increasing N2O loading of the troposphere, resulting from anthropogenic fertilisation, both as a greenhouse gas and more recently as an ozone-depleting substance has been recognised (Ravishankara et al., 2009; for details of the basic mechanisms, see Wayne, 1992; Holloway & Wayne, 2010).

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Figure 4. Ozone holes above the northern and southern hemispheres (NH/SH) in spring, depicted using the merged total column O3 retrieved in Dobson Units, DU, from measurements by GOME in 1997 and by GOME‑2 in 2011. (M. Weber and J.P. Burrows, University of Bremen)

3.3 Tropospheric Chemistry, Air Pollution, Biogeochemistry→ and Feedback

Atmospheric pollution, its consequences and the need to legislate to control its impact are not new phenomena. Even in the 14th century, the burning of fires in London was prohibited, at least temporarily. The process of urbanisation and industrial processes, powered by fossil fuels, generate both water and air pollution. This has led to urbanisation with now over 50% of the world’s population living in cities. Since the Haber–Bosch process was invented about 100 years ago to fix nitrogen from the air to produce ammonia, NH3, the use of ammonium nitrate, NH4NO3, for fertiliser, together with industrially produced pesticides, has resulted in huge increases in food production, but also changes in land use, water and air pollution, and climate change. The impacts of human activities now extend from the local to the global scale. The meteorological conditions of a location often play an important role in the type of pollution observed. In 1905 H.A. des Voeux first used the term smog to describe the mixture of smoke and fog that occurred in London in winter. The smoke from domestic fires and industrial processes led to acidic smog particles containing a cocktail of hazardous materials. Visibility was so poor that this type of smog was described by Cockneys as ‘pea-soupers’. London’s great smog of 1952, although it was not considered particularly unusual at the time, caused an estimated 12 000 premature deaths and 100 000 cases of

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respiratory disease, and led the UK parliament to introduce the world’s first clean air act in 1956. Since then, governments around the world have introduced legislation to control local emissions, while international institutions have supported environmental agreements to attempt to manage the problem at the global scale. In 1977, UNEP concluded a World Plan of Action on the Ozone Layer, which called for international research and monitoring, and in 1981 drafted a global convention to protect stratospheric ozone. As mentioned above, the Vienna Convention for the Protection of the Ozone Layer, concluded in 1985, is a framework agreement in which states agreed to cooperate in relevant research and scientific assessments of the ozone problem, to exchange information, and to adopt ‘appropriate measures’ to prevent activities that harm the ozone layer. The obligations were general and contain no specific limits on chemicals that deplete the ozone layer. However, the latter changed with the Montreal Protocol, as described above. Since 1979 the Convention on Long-Range Transboundary Air Pollution (LRTAP), administered by the UN Economic Commission for Europe (UNECE), has addressed some of the major environmental problems of the UNECE region through scientific collaboration and policy negotiations. The Convention has been extended by eight protocols that identify specific measures that states need to take to cut their emissions of air pollutants. The LRTAP Convention and its protocols, and the Vienna Convention and its Montreal Protocol and amendments (see below), have had some success, but the UN Framework Convention on Climate Change (UNFCCC) and its Kyoto Protocol have been far less effective and are not reducing emissions of greenhouse gases to the atmosphere from anthropogenic activity. The LRTAP Convention addresses acidic deposition that is the result of emissions of SO2 and NO2. During the day, SO2 is oxidised by OH radicals in the gas phase:

OH + SO2 + M " HSO3 + M (54) HSO3 + O2 " HO2 + SO3 (55) SO3 + H2O " H2S04 (56)

Sulphuric acid, H2SO4, which has a low vapour pressure and is hygroscopic, immediately forms aerosol condensation nuclei and aerosol particles result. In addition, the SO2 is oxidised in the aerosol and cloud particles by a complex reaction involving hydrogen peroxide, H2O2, formed in the gas phase in the disproportionation of the free radical hydroperoxyl, HO2:

HO2 + HO2 " H2O2 + O2 (57) H2O2g D H2O2l (58) SO2g D SO2l (59) H2O2g + SO2l D H2SO4l (60)

Nitric acid, HNO3, is formed primarily during the day by reaction (48) and at night by reaction (28). It absorbs more strongly in the UV-B and UB-C regions, and is only slowly photolysed in the troposphere compared to HONO. In the troposphere, HNO3, which is soluble, dissolves in aerosol and cloud particles and often reacts with NH3, which is also very soluble. The NH3 is released from soils by the oxidation of NH4+ and the reduction of NO3–. In the 1940s, residents of Los Angeles experienced a different type of smog during summer (Haagen Smit, 1952; Haagen Smit et al., 1952). Again this was associated with reduced visibility, but in this case O3 and a variety of organic compounds, in particular peroxyacetyl nitrate, CH3CO.O2.NO2, were found to be present in large amounts, as well as aerosols. It turns out that the processes that produce this type of local smog also occur at the global scale. Pollutants released from Earth’s surface are characterised as short or long lived, according to their lifetimes in the troposphere. Air in the troposphere and

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the stratosphere is exchanged on a timescale of 5−7 years. The combustion of fossil fuels and biomass burning release both short- and long-lived pollutants. The short-lived oxides of nitrogen, NOx (Σ ([NO] + [NO2]) and VOCs, which include a large number of rapidly oxidised biogenic and man-made aliphatic and aromatic gases, have natural tropospheric sources. NOx is produced from the breakdown of NH4NO3 by bacteria in soils, and by lightning. Biogenic emissions release only VOCs. Fire and biomass burning release both NOx and VOCs. Fossil fuel combustion releases NOx, CO, CO2, SO2 and some aromatics. Fossil fuel and biomass burning and land use change have modified dramatically the inputs of NOx and VOCs. Methane CH4 and other VOCs are released by oil refining and from coal mining. The new industrial practice of fracking, used to release CH4 from shales, and the exploitation of tar sands are likely to release CH4 and other VOCs to the atmosphere. These substances, coupled with solar UV and visible radiation, are the ingredients or fuel for the low-temperature chain reactions that oxidise the VOCs and result in smog formation relatively close to their source release region. In addition, the release of these short-lived species to the troposphere results in their transformation into toxic and noxious secondary pollutants, such as ozone, peroxyacetyl nitrate, CH3CO.O2.NO2 and aerosols and their subsequent transport throughout the troposphere. These constituents and their precursors have negative impacts on both human health and agriculture, and may also result in significant radiative forcing. The oxidation of CH4, which is long lived and is present throughout the troposphere, results, in the presence of sufficient NOx, in a chain reaction that produces O3 via the following mechanistic pathways:

Cycle 1-P: ozone production catalytic cycle in the oxidation of hydrocarbons

OH + CH4 " CH3 + H2O (61) CH3 + O2 + M " CH3O2 + M (62) CH3O2 + NO " CH3O + NO2 (64) CH3O + O2 " HCHO + HO2 (65) HO2 + NO " OH + NO2 (66) 2(NO2 + hν (λ < 411 nm) " NO + O) (15) 2(O + O2 + M " O3 + M) (2) ______

Net: CH4 + 3O2 + hν " HCHO + H2O + 2O3

The formaldehyde, HCHO, is photolysed and oxidised by OH, producing HO2 and CO:

HCHO + OH " H2O + CO (67) HCHO + hν (λ < 331 nm) " H + CHO (68a) HCHO + hν (λ < 361 nm) " H2 + CO (68b) H+ O2 + M " HO2 + M (69) CHO + O2 " CO + HO2 (70)

The larger VOCs have more complicated oxidation pathways. Catalytic O3 production cycles involving VOCs such as isoprene and terpenes, or the anthropogenic release of VOCs such as aromatic or aliphatic compounds, take place above the land close to their source. Their oxidation produces large amounts of the peroxy radicals, HO2 and RO2, both of which react with NO via reactions (64) and (66) and lead to the catalytic production of NO2, which in turn is photolysed to produce O3. Another catalytic cycle that produces O3 is that involving CO, cycle 2P. As a result of the supply of CO from the oxidation of CH4 and VOCs, this cycle operates throughout the troposphere, where sufficient NOx is present. This cycle was first pointed out by Fishman & Crutzen (1978, and references therein).

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Cycle 2-P: ozone production catalytic cycle in the oxidation of CO

OH + CO + O2 " HO2 + CO2 (71) HO2 + NO " NO2 + OH (65) NO2 + hν (λ < 425 nm) " NO + O (15) O + O2 + M " O3 + M (2) ______

Net: CO + 2O2 " CO2 + 2O3

The oxidation of VOCs also produces not only aldehydes but also ketones such as acetone, CH3.CHO, which is oxidised to form acetyl peroxy radicals, which in turn produce peroxyacetyl nitrate and the characteristic components of summer smog, along with acidic aerosols:

OH + CH3CHO " CH3.CO (72) CH3CO + O2 + M " CH3.CO.O2 (73) CH3.CO.O2 + NO2 + M " CH3.CO.O2.NO2 + M (74)

In the absence of sufficient NOx, O3 is removed globally by the HOx catalytic destruction cycle:

Ozone catalytic destruction cycle 3-L: HOx

OH + CO + O2 " HO2 + CO2 (71) HO2+ O3 " OH +2 O2 (46) ______

Net: CO + O3 " CO2 + O2

In addition to the above reactions, another source of halogen for the troposphere and in particular the boundary layer are biogeochemical mechanisms that release the organohalogens methyl chloride, CH3Cl, methyl bromide, CH3Br, methyl iodide, CH3I, dichloromethane, CH2CL2, dibromomethane, CH2Br2, bromochloromethane, CH2BrCl, bromoform, CHBr3, and others. These compounds also absorb in the ultraviolet and visible and are photolabile, releasing halogen atoms.

I2 + hν (λ < 792 nm) " I + I (75) CH3I + hν (λ < 498 nm) " CH3 + I (76) CH3Br + hν (λ < 406 nm) " CH3 + Br (20) CH3Cl + hν (λ < 342 nm) " CH3 + Cl (77)

Chlorine atoms react rapidly with aliphatic VOCs, including CH4, to produce HCl. Br and I, in contrast, do not react with aliphatic hydrocarbons. The bimolecular disproportionation reaction of ClO is lower than that of BrO dimer ClOOCl. As a result the chain length of the Cl catalytic removal of O3 is short compared with that of bromine via a loss cycle similar or equivalent to that of bromine atoms in cycle 3c-L above. The Br atoms catalytically destroy O3, via the cycle 2c-L and the mixed chain reaction 2e-L:

Ozone catalytic destruction cycle 2e-L: BrOx–HOx

OH + O3 " HO2 + O2 (9) Br + O3 w " BrO + O2 (25) BrO + HO2 " HOBr + O2 (78) HOBr +hν (λ < 425 nm) " OH + Br (79) ______

Net: 2O3 " 3O2

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However, on the surface of sea salt aerosols and very efficiently on cold brine under what is called potential frost flower conditions at high latitudes, this reaction becomes the multiphase chain reactions 3a-L and 3b-L (Kaleschke et al., 2004). 3a-L is autocatalytic in Br, provided there is a sufficient source of OH.

Ozone catalytic destruction cycle 3a-L: BrOx–HOx–HBr

OH + O3 " HO2 + O2 (9) Br + O3 " BrO + O2 (25) BrO + HO2 " HOBr + O2 (78) HOBrg D HOBrl (80) HOBrg + HBrl D Br2g + H2Ol (36) Br2 + hν (λ < 620 nm) " Br + Br (39) ______

Net: OH + HBrl + 2O3 " 3O2 + H2O + Br

Ozone catalytic destruction cycle 3b-L: BrOx–HOx–HCl

OH + O3 " HO2 + O2 (9) Br + O3 " BrO + O2 (25) BrO + HO2 " HOBr + O2 (77) HOBrg D HOBrl (79) HOBrg + HCll D BrClg + H2Ol (33) BrCl + hν (λ < 548 nm) " Br + Cl (38) ______

Net: OH + HCll + 2O3 " 3O2 + H2O + Cl

This chain reactions 3a-L and 3b-L are accelerated at low temperatures, as the equilibriums (36) and (33) have relatively large enthalpies of reaction and are acid catalysed. Cations are lost by precipitation from the brine at low temperatures, forming ikaite, CaCO3·6H2O, calcite (CaCO3) and other minerals. This results in quasi-liquid layers of brine-covered frost flowers, surface snow or aerosols that are readily acidified. These multiphase chain reactions are thus accelerated in at least two ways at low temperature. As a result, sea salt aerosols at mid-latitudes release Br but at much slower rates than in the potential frost flower conditions at cold high latitudes. Chlorine atoms participate in a similar cycle, but the chain length is not as effective as that of bromine atom, Br, because the rate of reaction Cl with CH4 is much less than that of Br + CH4. Nevertheless, the release of Cl2 produces Cl, thereby removing some O3 and CH4. This results in the formation of the methyl peroxy CH3O2 and the hydoxyperoxyl radical HO2 at least in the troposphere.

Ozone catalytic destruction cycle 2f-L: IO

2 × (I + O3 " IO + O2) (81) IO + IO " I + I + O2 (82a) ______

Net: 2O3 " 3O2

The iodine oxide, IO, disproportionation reaction has several channels and produces not only iodine atoms but also OIO and the dimer I2O2. The oxides of iodine then produce higher oxides, which lead to the production of the acid anhydrides I2O4 and I2O5, which are hygroscopic and result in new particle formation:

IO + IO " OIO + I (82b) IO + IO + M " I2O2 + M (83) IO + OIO + M " I2O3 + M (84)

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These pathways are currently the subject of much research investigating the importance of this natural source of aerosol condensation nuclei. The roles of glyoxal, CHO.CHO, and methyl glyoxal, CH3CO.CHO, which are formed in the oxidation of biogenic and aromatic VOCs and related compounds, in the formation of secondary organic aerosols is also of much interest. Current studies focus on understanding better their emission to the troposphere, their natural biogeochemical cycling, the transport and transformation of the emitted species and the anthropogenic modification of these processes. The global measurements by GOME and subsequent instruments (SCIAMACHY and GOME-2) have greatly improved our understanding of global air pollution and its transport and transformation. Important new areas of application include numerical predictions of the environment, and the impact of and feedback between climate change, which changes the temperature fields and momentum fluxes in the atmosphere, and atmospheric chemistry and dynamics and/or the hydrological cycle. The global measurements of water vapour, aerosols and cloud parameters are significant here. Figure 5 shows a schematic representation of these processes. NOx species, the sum of NO and NO2 are very important in tropospheric chemistry. Through the do-nothing cycle mentioned above, NO and NO2 are often coupled together during the day through the Leighton photostationary state defined by reactions (2), (13) and (15), where

[NO2]/[NO] = k13[O3]/J15

At lower NOx, the rate of reaction of NO with peroxy radicals and halogen oxides becomes significant and the ratio of NO to NO2 becomes more complex.

Figure 5. Schematic representation of chemical and physical processes as well as some biogeochemical interactions in the troposphere that control ozone and aerosols amounts. (J.P. Burrows and S. Noel, University of Bremen)

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It turns out that even HO2 has a pressure-dependent pathway in its reaction with NO. This forms HNO3. As the organic peroxy radicals become larger, the equivalent pathway, which produces organic nitrates, competes ever more effectively with the channel producing NO2. Thus the rate of production of O3 is dependent on the rate of the reaction of HO2 and the smaller organic peroxy radicals with NO for the branch producing NO2. HNO3 and the organic nitrates in the troposphere are often hygroscopic and enter the liquid phase. Nitrites, which can be carcinogens, sometimes result (for details of tropospheric chemistry, see Finlayson-Pitts & Pitts, 1986). Also depicted in Fig. 5 are the surface sources and fluxes. The interaction of pollutants with the surface and the impact on and modification of the natural biogeochemical cycling has become a focus of scientific studies. In addition to the chemically short-lived atmospheric constituents, a variety of long-lived gases are released by natural phenomena anthropogenic activity. Species such as the CFCs, their hydrogenated shorter-lived substitutes, the hydrochlorofluorocarbons (HCFCs), polyfluorinated compounds (PFCs) and halons, have been created by industry for applications such as refrigeration, fire extinguishers, electrical circuit board cleaners, etc., and were not present in the pre-industrial atmosphere. They are also greenhouses gases but they are infamous as they are transported to the stratosphere, releasing halogens, in particular Cl and Br, which then catalytically deplete stratospheric O3. In addition, as a result of fossil fuel combustion and land use change, copious amounts of CO2, CH4 and N2O are being released to the atmosphere. The changes in temperature and precipitation patterns result in feedback with chemical processing and dynamics. Understanding the Earth system and its atmosphere has been a scientific endeavour since the birth of the modern scientific method during the Renaissance. Many famous scientists, including Boyle and Hooke, conducted key experiments of relevance to the atmosphere and the Earth system. Apparently, Einstein considered that his determination of Avogadro’s number was likely to be more important than his papers on the photoelectron effect or special relativity, which were published in the same year. Climate change driven by anthropogenic activity is also not as novel as it sometimes appears. Fourier had already noted in 1824 that Earth’s surface is warmed by the presence of an atmosphere, and in the late 19th century John Tyndall, following studies of the interaction of radiation with material, demonstrated that individual gases had different warming effects. In 1896, Svante Arrhenius calculated that a doubling of atmospheric CO2 would lead to a surface warming of approximately 4°C. But it is only in the last four decades that the significance and importance of global pollution and climate change, driven by anthropogenic activity, has been recognised. This is now underpinned by objective scientific evidence provided by observations of changing atmospheric composition and more recently of the diminishing extent of sea ice and rising sea surface temperatures. The rate of development of understanding of the atmosphere and its interactions with Earth’s surface, described briefly in this chapter, has followed the exponential growth in the demand for energy since the industrial revolution. In the search for knowledge, scientific curiosity endeavours to understand the workings of the Earth system and its past behaviour and to predict its future behaviour. It is recognised that achieving sustainable development will require global management strategies. There are currently three relevant international agreements: the UNECE Convention on Long- Range Transboundary Air Pollution, the UN Vienna Convention for the Protection of the Ozone Layer and its Montreal Protocol on Substances that Deplete the Ozone Layer, and the UNFCCC and its Kyoto Protocol. Since the dawn of the space age, Earth observation has evolved as the science that meets the need for the global observations of the key parameters describing Earth’s atmosphere and surface. In order to understand the atmosphere and its changing behaviour, global measurements of key constituents (gases, clouds

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and aerosols) are required to test our understanding. The development of space- based measurements of atmospheric composition and surface parameters has been one of the greatest societal, scientific and engineering feats of the past half century. The following sections document some of the roles played by the ERS satellites in this process.

4.e Th Earth System and the Impact of Anthropogenic Activities The Earth system comprises the Sun, the atmosphere, land and oceans, and the physical, chemical and biological processes and their feedbacks, determine the conditions experienced by the biosphere. The system is in a complex biogeochemical equilibrium that has been evolving since life began some 3.5 billion years ago. As noted above, between the Neolithic revolution and about 1800, the human population rose from several million to about 1 billion, powered primarily by biofuel and alternative energy sources. Since the industrial revolution there has been a rapid increase in the Earth’s population and its standard of living. The explosion of economic growth in all its various incarnations has relied on the availability of relatively inexpensive energy produced from fossil fuels. This has led to unprecedented wealth and previously unimaginable supplies of food and luxuries, at least for those in the developed world. Mankind has implicitly assumed that Earth’s resources – the land, oceans and the atmosphere – are effectively inexhaustible. It is now recognised that resources are not unlimited, and they are rapidly being used up. The rise of humans has been accompanied by the destruction of ecosystems and biodiversity, which impacts on ecosystem services. The influence of human activities on Earth’s environment since the industrial revolution has created a new geological , called the Anthropocene. This is generally considered to have begun with the industrial revolution, although some argue that it started at the Neolithic revolution. One important societal issue has been the creation and establishment of political and economic systems that ensure an acceptable, fair and commensurate distribution of the growing affluence during the Anthropocene. A new concern is the ability of mankind to manage the human impact on the Earth system and thereby preserve the environment and the biosphere, achieving sustainable economic development. Perhaps the most obvious consequences of economic development have been the rising levels of air and water pollution. Less obvious but equally important has been the anthropogenic modification of the climate. Any strategy to cope with the consequences of the release of atmospheric pollutants requires changes in the way we use land and oceans. Pollution now extends across all scales from the local to the global. Global measurements of key atmospheric constituents are needed to manage the Earth system, and to improve

—— our understanding of the role of biogeochemical cycling and natural phenomena within the Earth system; —— assessments of the impacts of anthropogenic emissions and modification of the land and oceans; —— our knowledge of feedback mechanisms within the Earth–atmosphere system; —— our ability to identify and provide early warnings of change; —— the accuracy of predictions of weather and climate change; and —— the evidence base for international policies aimed at mitigating and adapting to climate change.

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Remote sensing instruments on orbiting satellite platforms provide potentially such objective global measurements, and complement the measurements made by the sparse network of measurements made at ground stations. The SCIAMACHY project and its spinoff, GOME, were responses by the scientific community to the need for global measurements of atmospheric constituents and the provision of an early warning of change, developed in the early 1980s. GOME flew on ESA’s second Earth Research Satellite, ERS‑2, which was launched on 20 April 1995, and its decommissioning was completed on 5 September 2011. ERS‑2 is now in circular orbit at around 570 km and will re- enter the atmosphere in less than 15 years, in line with current agreements for space debris management. Throughout and up to the end of the mission, GOME functioned without significant problems. SCIAMACHY, the more ambitious instrument, was flown on Envisat and, like GOME, has also worked excellently in space. The following chapters describe some of the unique aspects of the development of GOME and its success.

5. Atmospheric Remote Sensing from Space before ERS‑2 and Envisat The development of spaceborne measurements of atmospheric composition began at the dawn of the space age. After an initial phase of technology demonstration by both the Soviet space programme and NASA, focused geophysical missions began to be developed. The retrieval of information about trace gases by remote sensing techniques utilises knowledge of the absorption, emission and scattering of electromagnetic radiation in the atmosphere. Gases exhibit characteristic fingerprint spectra in emission or absorption:

—— rotational transitions are observed primarily in the far-infrared and microwave spectral regions; —— vibrational rotational transitions may be observed in the infrared; and —— electronic transitions are mainly in the UV, visible and near-infrared spectral regions.

This section discusses the advantages and disadvantages of each of these spectral regions, and describes the various instruments utilising remote sensing techniques to determine atmospheric composition. It documents developments prior to ESA’s decision to build atmospheric measurements into ERS‑2. The characteristics of some of the instruments that were flown prior to or during the development of the SCIAMACHY project, and their objectives, are summarised in Table 1 i.e. before and up to the early 1990s.

5.1 Remote Sensing of Trace Gases in the Far-Infrared and Microwave Spectral Regions

All gases having a dipole moment exhibit active rotational transitions in absorption and emission in the microwave (mm) and far-infrared (sub-mm) spectral regions. The Doppler line widths of rotational spectra are relatively narrow and the observed atmospheric emission or absorption is pressure- broadened by collisions with air molecules. While on the one hand, this enables the amount of gas at different pressures to be retrieved from an understanding of the line shape of the spectral features, on the other, it results in the broadening of the absorption or emission as the pressure in the atmosphere increases, as shown in Fig. 2, leading to a loss of specificity in the lower atmosphere. This limitation is compounded by strong atmospheric absorption by water vapour and CO2 and results in saturation in many parts of the spectral region.

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Ground-based measurements are restricted to atmospheric windows where the water vapour absorption is relatively small. For limb space-based atmospheric sensing, this is less of a problem but retrievals of trace gases in the upper troposphere are restricted to a limited number of candidates. For many gases such measurements are optimal for sounding the stratosphere and mesosphere. For the lower stratosphere and upper troposphere, there are some interesting possibilities, but the range of applications is restricted. Passive remote sensing instruments for both the millimetre, submillimetre or far-infrared spectral regions have been developed for atmospheric remote sensing. Some examples are briefly described in the following.

5.1.1 Microwave

Millimetre and submillimetre wave radiometers have been successfully used from a variety of platforms to measure stratospheric gases. By using microwave local oscillators and filter banks, or appropriate spectrometers, the observed brightness temperatures can be measured at various frequencies or across an emission line from a particular molecule. Significant American and European efforts have gone into the development of microwave and sub-mm sounding of the stratosphere. Some highlights of these programmes have been measurements of elevated amounts of ClO using ground-based (DeZafra et al., 1987), balloon-borne (Waters et al., 1981, 1984; Stachnik et al., 1992) and aircraft-borne instruments (Crewell et al., 1993). OH has been observed from an aircraft (Titz et al., 1995). The Jet Propulsion Laboratory (JPL) research team have successfully flown the (MLS) on the Upper Atmospheric Research Satellite (UARS), which has been measuring stratospheric profiles of ClO, 3O , H2O and HNO3 since 1992 (Waters et al., 1993) (see Table 1). The Microwave Atmospheric Sounder (MAS), which flew three times on the Space Shuttle, also measured ClO, O3 and H2O and provided a unique set of observations (Hartmann et al., 1996). The Swedish, French Canadian and Finnish collaboration Odin was initiated after Envisat and GOME and has made very successful measurements since 2001. Its Sub-millimetre Microwave Radiometer (SMR) instrument has retrieved many interesting species in the stratosphere and mesosphere. The Microwave Limb Sounder (MLS) on NASA’s Aura significantly extended the measurements of free radicals, in particular OH. Aura was launched in 2004 and the MLS is continuing to work well.

5.1.2 Far-infrared

The spectral region 7−200 cm−1 has been used to study molecular species in the stratosphere and mesosphere. In the far-infrared region, vibrations having weak force constants and rotations of light molecules are active. For these stratospheric measurements, Fourier Transform Spectrometers (FTS) with relatively high spectral resolution have been used. Two balloon-borne instruments have been flown a number of times (e.g. Carli et al., 1984; Chance et al., 1989). More recently, an FTS instrument has been developed for flight on a high-flying Safire aircraft (Table 1). With the exception of H2O and O3, trace gas measurements made prior to ERS-2 launch have exploited these long wavelength ranges exclusively for stratospheric measurements.

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Table 1. Some of the atmospheric remote sensing instruments flown prior to or during the development of the SCIAMACHY project , i.e. in or before the early 1990s, and, the height range of measurements, target species and the satellite platform.

Instrument Height of Target species Platform measurements*

ATMOS − Atmospheric Trace Molecule ST and upper TR O3, NOx, N2O5, ClONO2, Space Shuttle Spacelab-3 (1985); ATLAS-1, -2 Spectroscopy HCl, HF, CH4, CFCs, etc. and -3 (1992–94) AVHRR − Advanced Very High Resolution TR Smoke, fire, clouds TIROS-N, NOAA-6, -8, -10 (four channels); Radiometer aerosols, vegetation NOAA-7, -9, -11, -12, -13 (five channels) (1978−present) BUV − Backscatter Ultraviolet Ozone Experiment ST, TR, profiles O3 Nimbus‑4 (1970–74) LIMS − Limb Infrared Monitor of the ST CO2, HNO3, O3, H2O, Nimbus‑7 (1978−79) Stratosphere NO2 LRIR − Limb Radiance Inversion Radiometer ST CO2, O3 Nimbus‑6 (1975) MAPS − Measurement of Air Pollution from TR CO STS-2 (1981); Space Shuttle (1984, 1994) Satellites MAS − Microwave Atmospheric Sounder ST ClO, O3, H2O Space Shuttle ATLAS-1, -2 and -3 (1992–94) SAM II − Stratospheric Aerosol Measurement II ST Aerosols Nimbus‑7 (1979−90) SAMS − Stratospheric and Mesospheric ST, ME CO2, H2O, CO, N2O, Nimbus‑7 (1979−90) Sounder CH4, NO SBUV − Solar Backscatter Ultraviolet Ozone ST, TR, profiles O3 Nimbus‑7 (1979−90) Experiment SBUV-2 − Solar Backscatter Ultraviolet Ozone ST, TR, profiles O3 NOAA-9 (1985–present), Experiment 2 NOAA-11 (1989−95), NOAA‑14 (1995−present) SCR − Selective Chopper Radiometer ST CO2, T Nimbus‑4−5 (1970−75) SME − Solar Mesosphere Experiment ST, ME, profiles O3, O2(1∆), NO2 NASA (1983) TOMS − Total Ozone Monitoring Spectrometer ST, TR, profiles O3 Nimbus‑7 (1979−92) Earth Probe (1996−present) (1992−94) * ST – stratosphere; TR – troposphere; ME – mesosphere.

5.2 Remote Sensing of Trace Gases in the Thermal → Mid-infrared

In the mid-infrared, molecules with a dipole moment have active vibrational rotational transitions, which can be observed in both emission and absorption. For the observation of tropospheric species, pressure broadening of the spectral features is considerably less problematical than at longer wavelengths. This is because the Doppler line width is proportional to the frequency of the light and is therefore larger in the mid-infrared than at longer wavelengths. Nevertheless, broadening of lines results in some loss of specificity of the measurements. In addition, in the mid-infrared strong absorptions by H2O and CO2 restrict the available spectral windows. Passive remote sensing by mid-infrared spectroscopy has been applied to the measurement of many stratospheric trace constituents and some upper tropospheric constituents. Initially, measurements were made from mountain tops (e.g. Zander, 1981), and balloon and aircraft experiments were subsequently developed (Fischer et al., 1980; Murcray et al., 1975; 1979; Coffey et al., 1981; Brasunas et al., 1988; Kunde et al., 1988). Both absorption and emission experiments have been made using a number of instruments. The Limb Radiance Inversion Radiometer (LRIR) and Limb Infrared Monitor of the Stratosphere (LIMS) are infrared radiometers that were flown on Nimbus‑6 and -7, respectively, and recorded data in 1978 and 1979 (Gille et al., 1980; Gille & Russell, 1984). The six channels of LIMS observed emissions by CO2, HNO3, O3, H2O and NO2 from 15−65 km.

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Starting with the Selective Chopper Radiometer (SCR) on Nimbus‑4 and -5, the Department of Physics at the University of Oxford developed a series of instruments to observe infrared emission from the atmosphere. A pressure- modulated instrument was flown on Nimbus‑6 and the Stratospheric and Mesospheric Sounder (SAMS) on Nimbus‑7 (Table 1). SAMS measured in limb- viewing geometry the gases CO2, H2O, CO, N2O, CH4 and NO in the stratosphere and mesosphere. An improved version SAMS was flown on the UARS and added channels for NO2, N2O5, HNO3, O3, and H2O (e.g. Barnett et al., 1992) (Table 1). Fourier transform spectrometers had already been used to sound the stratosphere and upper troposphere in the 1980s. One of the most important successes was the Atmospheric Trace Molecule Spectroscopy (ATMOS) project (Farmer et al., 1987; Gunson et al., 1996). The ATMOS instrument was flown on Spacelab 3 and the Atmospheric Laboratory for Applications and Science (ATLAS) on Space Shuttle missions (Table 1). ATMOS performed solar occultation measurements and retrieved a variety of trace gases in the upper troposphere and stratosphere. From its launch in 1991 until 2005, the UARS circled Earth in a low- Earth non-Sun-synchronous orbit and carried three infrared experiments. In addition to ISAMS (described above), the Cryogenic Limb Array Etalon Spectrometer (CLAES) and the Halogen Occultation Experiment (HALOE) made infrared measurements designed to yield information about stratospheric and tropospheric trace gas constituents. Some of the measurements were of shorter duration. The CLAES used a high-resolution etalon to measure the limb emission in the infrared (Roche et al., 1982; Roche & Kumer, 1989). The target gases and parameters were N2O, NO, NO2, HNO3, CF2Cl2, CFCl3, HCl, O3, ClONO2, CO2, H2O, CH4 and temperature (UARS, 1987). HALOE used broadband filter radiometry to measure CO2, H2O, O3 and NO2 and gas filter correlation radiometry to measure HF, HCl, CH4 and NO (Baker et al., 1986) (Table 1). All four stratospheric remote sensing missions (MLS, ISAMS, CLAES and HALOE) on the UARS achieved their goals. The HALOE and MLS were still measuring after eight years and produced a unique record of the stratosphere. The UARS eventually ceased operation in 2005 after 14 successful years. The Japanese National Space Development Agency (NASDA) launched its Advanced Earth Observing Satellite (ADEOS) in 1996. The payload included an Improved Limb Atmospheric Spectrometer (ILAS), an Interferometric Monitor for Greenhouse Gases (IMG) and a Total Ozone Monitoring Spectrometer (TOMS) for atmospheric sensing. IMG and ILAS are nadir and limb sounding infrared instruments (Table 1). ADEOS I was launched in August 1996 but failed in June 1997. ADEOS II with ILAS‑II was launched in December 2002 but communication was lost in October 2003. Prior to the development of the atmospheric sensors on Envisat and ERS‑2, the only experiment that specifically used infrared information to probe the troposphere was the Measurement of Air Pollution from Satellites (MAPS) experiment. This was a nadir sounding gas correlation, which made global measurements of CO in the middle and upper troposphere. It flew three times between 1981 and 1994 on the NASA Space Shuttle (Reichle et al., 1986; 1990; Connors et al., 1991).

5.3 Remote Sensing in the UV, Visible and Near-IR

The source of radiation for passive remote sounding of the atmosphere in the ultraviolet, visible and near- and short-wave infrared regions is the Sun. The Sun’s maximum emission is around 580 nm. Beyond 300 nm the Sun corresponds fairly well to a black body with a temperature around 5800K. Absorption by atoms in the Sun produce the Fraunhofer lines, having many strong features in the ultraviolet and visible but are less significant at longer

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wavelengths. Below 300 nm the solar emission deviates from simple blackbody behaviour and the solar output is determined to a significant extent by processes occurring at the edge of the Sun and correlate with solar activity. In 1957 it was proposed that satellite measurements of backscattered ultraviolet (BUV) radiation from the terrestrial atmosphere could be used to deduce ozone profiles on a global basis (Singer Wentworth, 1957). The method relies on two effects, i.e. the scattering of light at short wavelengths and the absorption of ozone. Rayleigh scattering of light by air molecules has a strong dependence on wavelength, whereby the intensity of scattered light is a function of the inverse of the fourth power of the wavelength. Similarly, ozone absorption is strongly wavelength-dependent. These effects combine and as a result the penetration depth of light in the atmosphere varies strongly between the ozone maximum absorption in the Hartley band around 250 nm and its minimum beyond 380 nm in the Huggins bands. Numerical techniques for the determination of vertical profile information were also studied by NASA for the determination of total ozone in the atmosphere (Dave & Mateer, 1967; Mateer et al., 1971). The development of these retrieval techniques has continued up to the present (Bhartia et al., 1996). The earliest measurement utilising the BUV technique was undertaken by Rawcliffe & Eliot (1966) using a photometer observing at 284 nm. Ozone distributions were determined utilising measurements from the USSR Cosmos satellites, which flew a double monochromator in 1965 and 1966 (Krasnopol’skiy, 1966; Iozenas et al., 1969a,b). The Backscatter Ultraviolet atmospheric ozone experiment (BUV) was the first in a series of instruments produced by NASA and later NOAA, which has successfully made long-term measurements of the BUV for the vertical profile and total amount of ozone (Heath et al., 1973) (Table 1). BUV was launched on the Nimbus‑4 satellite into a circular polar orbit at an altitude of 1100 km. This orbit is Sun-synchronous and the satellite crosses the equator in an ascending mode every 107 min close to local noon. This instrument concept was developed and resulted in the Solar Backscatter Ultraviolet (SBUV) and Total Ozone Mapping Spectrometer (TOMS) launched on Nimbus‑7 (Heath et al., 1975). The SBUV instrument was further improved to the SBUV-2 and has been flown by NOAA on a series of satellites (Table 1). After Nimbus‑7 (1979−92), TOMS was also flown on the Russian Meteor platform (1992−94), on the Japanese Advanced Earth Observing Satellite (ADEOS, 1996−97) and on Earth Probe (1996−present) (Table 1). These measurements have been used to derive a unique ozone dataset. Readers are referred to the literature about the TOMS data record for more details. Recognising the importance of long-term calibration and validation of space-based instrumentation, NASA developed the Shuttle SBUV (SSBUV), which flew eight times on the Shuttle to calibrate radiometrically the BUV instruments (Hilsenrath et al., 1988; 1996). The attention paid to the detail of the calibration of the NASA and NOAA BUV instruments has established the quality of these datasets (Hilsenrath et al., 1995). The TOMS retrievals of total column O3 have been combined with data from the SBUV or the second Stratospheric Aerosol and Gas Experiment (SAGE II) to provide information about tropospheric O3. TOMS had also been used to demonstrate the emissions of SO2 during volcanic eruptions into the troposphere and the stratosphere. The first SAGE instrument (SAGE I), flown on NASA’s Atmosphere Explorer mission from 1979 to 1981 (Table 1), was a spectrometer that measured the absorption of sunlight by ozone with four channels centred at 0.385, 0.45, 0.6 and 1.0 μm (McCormick et al., 1979; Chu & McCormick, 1979). SAGE II is a seven-channel instrument from the same team (Maudlin et al., 1985), which was launched on NASA’s Earth Radiation Budget Satellite (ERBS) and is still in operation today. A third-generation SAGE has been developed, which was

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launched on the Russian Meteor-3M in 1999 and deployed on the International Space Station in 2002. SAGE determines atmospheric absorption in occultation and measures at sunrise and sunset. So far the SAGE series of instruments has provided reliable data for a number of atmospheric constituents. An interesting set of measurements was obtained by the Solar Mesosphere Explorer (SME), which was flown by NASA in 1983. It contained limb-scanning ultraviolet, visible and near-IR channels for measuring stratospheric and mesospheric O3, O2(1Δ) and NO2 (Rusch et al., 1984; Thomas et al., 1984; Mount et al., 1984).

5.4 Remote Sensing Techniques for Aerosols and Clouds

Molecular or Rayleigh scattering of electromagnetic radiation is strongly dependent on wavelength (~λ−4), whereas scattering by aerosols and clouds obeys approximately the Mie theory and is relatively weakly dependent on wavelength (~λ−(1−2)). Aerosols have been identified by their scattering effects both actively and passively. Prior to the launch of ERS‑2 in 1995, the first passive remote sensing experiment to measure successfully the abundance of atmospheric aerosols from space was the Stratospheric Aerosol Measurement (SAM II) on Nimbus‑7 (McCormick et al., 1979). This experiment was a single- channel radiometer observing in solar occultation and was the forerunner of SAGE. Stratospheric aerosols have also been measured by their infrared absorptions (e.g. HALOE). Tropospheric aerosols and smoke have been retrieved from several types of remote sensing data. For example, algorithms to retrieve tropospheric aerosols have been developed for data from the Advanced Very-High-Resolution Radiometer (AVHRR) data over the oceans (Ignatov et al., 1995). Smoke from burning biomass was observed by AVHRR and also from TOMS data (Hsu et al., 1996). In the ultraviolet, visible and near-infrared spectral regions, radiation is strongly scattered by clouds, enabling its presence to be detected, provided that the spatial resolution is sufficiently high and the difference between the effective cloud albedo and the spectral reflectance of Earth’s surface is sufficiently large. Several algorithms have been developed that aim to utilise the oxygen absorption for the determination of cloud top heights (Guzzi et al., 1996; 1998 and references therein). Clouds emit long-wavelength infrared radiation, the spectrum of which depends on their temperature. Observations of the infrared emissions by clouds with instruments on the platform and related meteorological satellites are routinely used to estimate the cloud top height and cover. The visible channels flown on the Along-Track Scanning Radiometer (ATSR-2) were planned and used to determine aerosol and clouds. The French Polarisation and Directionality of the Earth’s Radiance (POLDER) instrument on the Japanese platform ADEOS-1 measured polarisation parameters (Table 2), which were used to study polarisation, aerosols and clouds in the atmosphere (Deschamps et al., 1990). After ERS‑2 and GOME, in Europe the national agencies continued the development of SCIAMACHY for Envisat, and ESA developed GOME‑2 for Eumetsat’s MetOp series of platforms. NASA offered to fly the Dutch Ozone Monitoring Instrument (OMI) instrument on its Earth Observing System Chemistry (EOS-CHEM) mission, which was later renamed Aura.

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6.e Th Development of European Observation of Atmospheric Composition To understand the development of the European component of global measurements of atmospheric composition and Earth observation for Earth system science, it is necessary to follow the evolution of modern atmospheric science and the birth of the space age. While observing Montgolfier balloons over Paris in 1783, Benjamin Franklin had noted and foreseen the potential value of remote sensing of the Earth from elevated platforms. This was prior to the development of spectroscopy by Fraunhofer and colleagues. The identification of an ultraviolet absorber in the upper atmosphere by Cornu in 1879 and its attribution by Hartley in 1880 to ozone may have been the first modern application of remote sensing of atmospheric composition. This was followed by the in situ discovery aboard balloons of the temperature inversion at the tropopause and the stratosphere. Other important milestones in the development of atmospheric Earth observation were the development by Dobson of a spectrophotometer for the remote sensing of atmospheric ozone in the 1920s and the observation of aurora phenomena from the ground. The pioneering development of rocketry in the first half of the 20th century was accelerated by the recognition of their use for military applications after the Second World War. However, the superpowers that emerged in the post- war period also engaged in a technological race, in part to demonstrate their ideological superiority in the civil exploitation of space. The birth of the space age began with the launch of Sputnik in 1957 by the Soviet Union and the creation of NASA in the USA. The first American satellite, Explorer 1, was launched in 1958 and carried instrumentation for studying the upper atmosphere and magnetosphere, and led to the discovery of the van Allen belts. In 1960 NASA began the Television Infrared Observation Satellite (TIROS) programme and a series of platforms to test experimental television techniques designed to develop a worldwide meteorological satellite information system. In the 196Os the Soviet Union began the first measurements of atmospheric ozone. At the same time NASA began to develop the Nimbus and other research platforms carrying instrumentation designed explicitly to measure atmospheric composition with increasing accuracy. At this stage, Europe was still recovering economically and politically from the Second World War, and its space exploits were limited to national programmes and collaborations with NASA. However, the need for an overarching European dimension to was recognised and both the European Space Research Organisation (ESRO) and the European Launcher Development Organisation (ELDO) were created in the early 1960s. ESA was formed by a merger of these organisations in 1975. ESA immediately began to develop Earth observation programmes to meet the evolving need for atmospheric research and operational meteorological purposes. This resulted in the first European geostationary meteorological satellite, Meteosat, whose operation was transferred to the newly created European Organisation for the Exploitation of Meteorological Satellites (Eumetsat) in 1986. In the 1960s and 1970s Europe’s leading scientists had no direct access to space for Earth observation other than through participation in national programmes, which were linked to NASA or sometimes the Soviet space programme. While international collaboration in space remains a cornerstone of activity, the creation of ESA and later Eumetsat, and the broadening of national space agency objectives, changed dramatically the role of European Earth observation. In its early period, ESA’s Earth observation programme focused on the development of satellite instrumentation for numerical weather prediction and surface mapping. It was not concerned with atmospheric trace constituent composition. The evolution of ESA resulted in the development of the European Remote Sensing programme in the 1980s, with two satellites, ERS‑1 and ERS‑2. The ERS team at ESA correctly recognised the need for continuity and

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improved measurements, and this has been the most consistent and important recommendation of all ESA scientific user consultations. The ERS programme addressed the need to achieve long-term global datasets and to benefit from technological developments to improve the spatial resolution and temporal sampling of measurements. The payload of ERS‑1 comprised Earth observation instruments that gathered information about Earth (land, water, ice and atmosphere) using a variety of measurement principles:

—— RA (Radar Altimeter) – a single-frequency nadir-pointing radar altimeter operating in the Ku-band; —A— TSR-1 (Along-Track Scanning Radiometer) – a 4-channel infrared radiometer and microwave sounder for measuring temperatures at the sea surface and at the top of clouds; —S— AR (Synthetic Aperture Radar) – a radar operating in the C-band to detect changes in surface heights with sub-millimetre precision. —W— ind Scatterometer – to calculate information on surface wind speeds and direction; and —M— WR (Microwave Radiometer) – to measure atmospheric water, as well as to provide a correction for the atmospheric water for the altimeter.

The ERS-1 payload also included the Precision Range and Range-Rate Equipment (PRARE) and a Laser Retroreflector. The PRARE was non- operational since launch. The Retroreflector was used for calibrating the Radar Altimeter to within 10 cm. In summary, ERS‑1 had only one instrument, the MWR, for measuring atmospheric composition. Since the 1970s, the impacts of human activities on the atmosphere were becoming increasingly apparent. In 1984 a team led by myself from the Max Planck Institute in Mainz developed a concept called the measurement of air pollution from space, MAP, to fly on ESA’s European Retrievable Carrier (EURECA). This was submitted to the ESA call for EURECA instruments in early 1985. Although the concept was unfortunately not selected, 1985 was a momentous year for atmospheric science. Farman, Gardner and Shanklin from the British Antarctic Survey published their discovery of the loss of ozone in the polar vortex in spring above the Halley Bay research station in Antarctica (Farman et al., 1985). The term ‘ozone hole’ results from the first observations from NASA’s TOMS and SBUV O3 data, which showed the large extent and evolution of this phenomenon, which filled the whole of the polar vortex. The discovery shocked the public and demonstrated unambiguously the impact of pollution, particularly the release of CFCs and other ozone-depleting substances in the northern hemisphere, on a region as far from the point of release as is possible. Looking back, it is difficult to imagine the impact of the discovery of the ozone hole and its importance. The scientific community, funded by the American and European governments, then embarked on an ambitious set of experiments to understand the precise origin of this phenomenon. Indeed, as suggested by Farman et al., (1985), the ozone hole was the result of the release of CFCs, halons, methyl bromide and other substances, although the mechanism (described above) is much more complex. The discovery of the ozone hole focused attention on the need for global measurements of trace gas species. In Germany, an investigative commission was established to provide the parliament with advice. The commission was led by Bernd Schmidbauer and included prominent scientists such as Paul Crutzen, Reinhardt Zellner and others. The commission’s interim report, ‘Protecting the Earth’s atmosphere: an international challenge’, had a significant impact on German scientific policy aimed at controlling ozone-depleting substances and the measures needed to monitor and assess the impact of international agreements. It recommended scientific programmes including studies of3 O .

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The commission specifically discussed the need for global measurements from space using SCIAMACHY or equivalent instrumentation to provide the information needed to facilitate the development of national, European and international policy with respect to ozone-depleting substances and tropospheric pollution. This was of key importance for the development of GOME and SCIAMACHY.

7.e Th SCIAMACHY Project and the Spin-off of GOME

During the 1980s, ESA was growing and its mission evolving. In 1985 ESA appointed a new director of Earth observation, Philip Goldsmith. He came from the UK Met Office and had studied in Oxford under the guidance of the atmospheric scientists Brewer and Dobson and had worked closely with Sir John T. Houghton. He was therefore aware of the need for atmospheric composition measurements from space. He brought with him to ESA another atmospheric scientist, Christopher Readings, also from the UK Met Office. While administering many different aspects of ESA, these men brought additional expertise in atmospheric research and its needs to ESA. They complemented the efforts of the both the then ERS Mission Manager Guy Duchossois, who worked tirelessly for the development of European Earth Observation throughout his career, and the ERS team at ESTEC. European satellite-based meteorology began on the 23 November 1977 when Meteosat, developed by ESA, was launched from Cape Canaveral into a geostationary orbit. Meteosat was also ESA’s first Earth observation satellite, launched just two years after ESA had been established and nine years before Eumetsat was created. In the intervening years ESA had promoted the establishment of Eumetsat to address the needs of the European operational community for numerical weather prediction. The provision of an adequate space infrastructure was a key issue and Eumetsat was created in 1986 under the leadership of John Morgan, also from the UK Met Office, to manage the operational satellites, developed by ESA, for the meteorological community. In late 1981 I decided to move from the UK Atomic Energy Research Establishment at Harwell, Oxfordshire, and my collaboration with Richard Wayne at the then Physical Chemistry Laboratory of the University of Oxford, to take an opportunity to join the Max Planck Institute (MPI) for Chemistry in Mainz in the new department of air chemistry led by Paul Crutzen. Initially I worked with Geert Moortgat and David Griffith on laboratory kinetics and spectroscopy of free radicals and reactive species. In 1982 Dieter Perner (1934–2012) also joined Paul’s department at the MPI to establish an optical spectroscopy group for atmospheric measurements. After three years in the laboratory I joined the optical spectroscopy group as one of its leaders. As a laboratory spectroscopist and kineticist researching atmospheric reactions at the time, it became clear to me that the technique of Differential Optical Absorption Spectroscopy (DOAS), which had been developed for active long path measurements by Dieter Perner and Ulrich Platt at the Jülich Research Centre in the 1970s, should work from space. From 1984 onwards, supported by Paul Crutzen, Dieter Perner and Wolfgang Schneider, who worked closely with me for many years, I began to develop a concept for measuring trace gases from space. Our first effort, called Mapping of Atmospheric Pollutants (MAP), was proposed prior to the discovery of the ozone hole in 1985, to ESA for flight on EURECA. DOAS uses the electronic vibrational rotational structure of trace gases to identify in a fingerprint manner their presence in the atmosphere. It separates the spectral features of gases from broad-band absorption and scattering by a mathematical high-pass filter technique using a low-order polynomial to capture the broad-band absorption and scattering. The technique utilises the Beer−Lambert law, but requires explicit consideration of multiple scattering

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in the atmosphere for the determination of amounts and distribution of trace gases. This and more sophisticated retrieval approaches were planned from the outset for SCIAMACHY and GOME. In the mid-1980s ESA’s Earth Observation Directorate organised meetings with the European research and operational meteorological community to discuss and assess the needs of the different Earth observation disciplines. At one such meeting in 1986, Paul Simon from the Belgian Institute for Space Aeronomy (BIRA) and I were tasked with describing the requirements for atmospheric trace gas measurements from a co-orbiting platform. We proposed observation using scanning imaging remote sensing in the solar spectral region for the retrieval of trace gases using the DOAS approach. At this time, ESA’s Columbus programme had developed the Free Flyer or Polar Platform, also known as part of the Polar Orbiting Earth Observation Mission (POEM-1). Building on our preparations for the Announcement of Opportunity (AO) call for EURECA, I began organising a science team at the MPI in Mainz. International experts were invited to join the team, supported by the European global atmospheric chemistry modelling community. In due course ESA issued an AO for ‘the provision of Earth observation instrumentation on the European Polar Platform of the Space Station’ in January 1988. In response to this new AO, I led the development of a science team proposal, supported by my managers and colleagues at the MPI. I invited scientists from Europe and America to join our initiative. I christened our proposed instrument the Scanning Imaging Atmospheric spectroMeter for Atmospheric CHartographY, or SCIAMACHY, which in Greek means chasing or hunting light and shadows, and in part has the meaning of undertaking an impossible task. As part of the preparation of the proposal we contacted aerospace companies and selected Dornier in Friedrichshafen, Baden- Württemberg, Germany, to be the Prime Contractor for SCIAMACHY proposal preparation. The Dornier department of optical instrument development, led by Elmer Weber and supported by the Dornier management, agreed to develop an industrial assessment of the scientific instrument concept for the SCIAMACHY proposal. Edgar Hölzle, Rolf Mager and colleagues at Dornier then began their analysis, design and cost assessment for developing a SCIAMACHY instrument based on our concept. Dornier later became part of DASA, which subsequently became part of Astrium, which is now part of EADS. Although the team at Friedrichshafen evolved over the years, their hard work has been one of the reasons for the success of SCIAMACHY, ERS-2 and Envisat. Particularly valuable contributions at the beginning of the development of the objectives and scientific justification for the proposal were made by Wolfgang Schneider, Dieter Perner, Paul Crutzen and Geert Moortgat from the MPI. Both the experimentalists Ulrich Platt, then at the Institute for Chemistry of the Nuclear Research Centre in Jülich, and Jean-Pierre Pommereau at CNRS in Paris, who with his colleagues had developed the SAOZ balloon and ground-based instruments, joined our SCIAMACHY science team. In addition, Paul Simon and Christian Muller from BIRA brought their solar physics and aerosol expertise to the team. As well as Paul Crutzen, other scientists from the European Global Modelling of Atmospheric Chemistry (GLOMAC) project such as Ivar Isaksen of the University of Oslo, Han van Dop of the Royal Netherlands Meteorological Institute (KNMI), Erik Rockner of MPI Hamburg, Hilding Sundquist of the University of Bergen and Henning Rhode of the Meteorological Institute of Stockholm University, joined the SCIAMACHY project becoming the data user group within the science team. John Geary, an expert in detector technology for space applications, and Kelly V. Chance, who provided sensitivity analyses from the Harvard Smithsonian Astrophysical Observatory, completed the first SCIAMACHY team. In addition, Jack Fishman of NASA, USA, John Harries of the Rutherford Appleton Laboratory, UK, and Richard Wayne of the University of Oxford agreed to act as scientific consultants to the SCIAMACHY principal investigator and science team.

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The SCIAMACHY proposal, conforming to the ESA guidelines for the AO for POEM, was organised and written in late spring and summer 1988, by myself with contributions from the science and industrial teams. I would like to thank Wolfgang Schneider, Dieter Perner and Paul Crutzen for their help in the preparation of the final proposal. In addition to excellent scientific objectives, affordable and feasible instrument design, calibration and validation strategies, which were of great importance for the potential success of the project, the support of the German government was essential. Fortunately, the then German Federal Ministry of Research and Technology (BMFT) in the department run by Mr Spät and Mr Käsmeier, agreed that their department would support the proposal, provided that SCIAMACHY was selected for Phase A studies for flight on the Polar Platform by ESA’s technical and scientific review. The SCIAMACHY instrument concept at this time comprised two instruments both able to undertake nadir limb and solar or lunar occultation measurements: one focused on limb observations and the other on nadir observations during the Sun-synchronous orbit. Measurements were to be made during solar and lunar occultation in both hemispheres. This approach was and is a very attractive concept but was later descoped to one instrument, making alternate limb and nadir measurements, as well as solar and lunar occultation when available, in order to reduce costs. During the time between proposing the MAP instrument for EURECA and the winter of 1988, there were many developments. The publication of the first observation of what came to be known as the ozone hole by Farman, Gardner and Franklin from the UK Natural Environment Research Council; the British Antarctic Survey had led to many campaigns to assess the origin of this unique phenomenon. NASA traced the development of the ozone hole using maps retrieved from both TOMS and SBUV instruments, and detected a large loss in the vertical profile of 3O using the SAGE-II instrument. To address the needs of users, ESA hosted a meeting with the EO user community in Paris in November 1988, organised by Chris Readings, Guy Duchossois, and others. One objective was to develop a set of priorities. John Pyle of the University of Cambridge and I on behalf of the atmospheric scientists presented our findings. One important conclusion was that a European instrument observing atmospheric trace gas composition was needed much more quickly than the schedule for the launch of the Polar Platform would allow. At the end of the meeting Jan Berger, then director of ESTEC, asked me what sort of instrument the atmospheric community needed to address atmospheric chemistry and composition. I explained that sounding spectrometers could make a valuable contribution. Subsequently the ESA Earth Observation Directorate invited proposals for ideas for an instrument to measure atmospheric composition for ERS‑2, which was already being built. The ERS team, led by Reinhold Zöbl, Peter Edwards and Peter Dubock, had identified that ERS‑1, which had been built, had some valuable contingency. They proposed that in ERS‑2, this contingency could be used to accommodate an additional small instrument. As a result Philip Goldsmith, Director of Earth Observation and Microgravity at ESA, supported by Guy Duschossois and Chris Readings, issued a call for instrumentation for measurements of atmospheric constituents for ERS‑2 in late November 1988. In response to this call, I developed the SCIA-mini proposal, supported by Paul Crutzen and selected members of the SCIAMACHY international science team, and submitted it to ESA in December 1988. Part of the feasibility assessment of SCIA-mini was again supported by the Dornier industrial team. The instrument concept comprised two spectrometers, both capable of limb nadir and occultation measurements. Like SCIAMACHY, one was focused on nadir and one for limb. However, as ‘off-the-shelf’ technology was required it was later restricted to using available detectors and was limited to the UV, visible and near-IR spectral regions.

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In February 1989, ESA concluded its review and selection procedures for the payload instrumentation for the Polar Platform to enter Phase A studies at a meeting in Paris. The meeting was fraught, as all the proposed instruments were of unique value. In addition, the task of selecting in a transparent manner an adequate compliment of instruments for the atmospheric composition remote sensing payload was challenging. Many exciting concepts were presented, including the Advanced Microwave Atmospheric Sounder (AMAS), based on the successful Microwave Atmospheric Sounder, which flew on the Space Shuttle, proposed by a team led by Klaus Künzi of the University of Bremen. Another was the Doppler Wind Sounder (DWS), proposed by David Rees and his team from University College London. The Dynamic Limb Sounder (DLS) proposed by a team led by John J. Barnett (1948–2010) of the University of Oxford, and based on pressure modulated experience with the Stratospheric and Mesospheric Sounder (SAMS) on Nimbus‑7 and the Improved Stratospheric and Mesospheric Sounder (ISAMS) on UARS. This was subsequently merged with an American instrument to become the High-Resolution Dynamic Limb Sounder (HRDLS) that flew as part of NASA’s Aura mission. The Michelson Interferometer for Passive Sounding (MIPAS) was proposed by Karlruhe University and the Medium-Resolution (MERIS) were also presented. Finally, the Global Ozone Monitoring by Occultation of Stars (GOMOS) proposal was presented by Jena-Louis Berteaux. I presented the SCIAMACHY concept, supported by Paul Crutzen and Wolfgang Schneider at this review. Jean-Pierre Pommereau of CNRS, an expert in atmospheric remote sensing and a member of the science teams of both GOMOS and SCIAMACHY was tasked to summarise and explain to the ESA panel, the relative advantages and disadvantages of GOMOS and SCIAMACHY. After its technical and scientific review, ESA announced that it planned to initiate Phase A studies of atmospheric instrumentation for the Polar Platform. Of the instruments in the competition, MIPAS and MERIS were selected to become ESA-developed instruments, and GOMOS and SCIAMACHY were selected as nationally funded AO instruments. The ESA Programme Board for Earth Observation (PB‑EO) and subsequently the ESA Council agreed to this proposal. The German BMFT, in the department led by Mr Spät and Mr Käsmeier, then funded an industrial study of SCIAMACHY led by the Dornier team. The funding was managed by the DLR Aerospace team, then at Köln Portz, which later became Deutsche Agentur für Raumfahrtangelegenheiten (DARA), and eventually the German Aerospace Centre (DLR) in 1997. This enabled work to commence on the design and Phase A for SCIAMACHY. In April 1989 the German BMFT initiated the Atmospheric Trace Molecule Spectroscopy (ATMOS) project, coordinated by Wolfgang Mett. ATMOS was a concept for a national satellite for atmospheric and ocean science, with an instrument payload comprising the Advanced Millimeter Wave Atmospheric Sounder (AMAS), the MIPAS, the Reflective Optics Imaging Spectrometer (ROSIS) and SCIAMACHY. This provided a much-needed boost to the development of the instrument and mission concepts for both MIPAS and SCIAMACHY. Ultimately, after an ineffective collaboration with CNES, who developed the Globsat concept, ATMOS and Globsat missions sadly were not pursued. However, the ATMOS project then provided funding for the development of SCIAMACHY for the Polar Platform. After SCIAMACHY was selected for Phase A studies for the Polar Platform, and for ATMOS, Albert Goede of the Netherlands Institute for Space Research (SRON) and Roel Hoekstra (1932–5) of Netherlands Organisation for Applied Scientific Research, Institute of Applied Physics (TNO-TPD) visited me in my role as PI to discuss the potential Dutch interest and proposed that the Dutch participate in the industrial consortium to build SCIAMACHY. After discussion with Dornier, work packages were agreed for SRON and TNO-TPD who then joined the industrial consortium with Dornier as the Prime Contractor. TNO- TPD was tasked with optical design issues and the SRON team with the

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design of the focal plane assemblies. R. von Konijnenberg of the Netherlands Institute for Aerospace Programmes (NIVR), the Dutch delegate to ESA’s PB-EO, actively supported this activity. Thus the Netherlands joined the consortium supporting the SCIAMACHY design and industrial procurement. Huib Visser and Kees Smorenburg of TNO-TPD began working with me and the SCIAMACHY industrial team led by Edgar Hölze on key aspects of the optical design. During Phase A the German company OHB System in Bremen also joined the consortium, with responsibility for developing the electronic brain of SCIAMACHY. Later, in Phase B, the Belgian Ministry joined the funders of SCIAMACHY to help support some of the Dutch contributions. As a result the polarisation monitoring device was provided by a Belgian company, and the Dutch brought in Fokker Space, now called Dutch Space. ESA introduced a fast-track review process for its plan to add an atmospheric composition remote sounding instrument to ERS‑2. Although ESA selected SCIA-mini, in the summer of 1989 the EO Programme Board and the ESA Council believed there was a high risk that the instrument would not be developed on time for flight on ERS‑2 in 1994. They therefore recommended that a short Phase A study of a descoped version of SCIA-mini be produced. The study was led from the ESA side by Peter Dubock and Chris Readings. Initially, I called this instrument SCIAS, a Scanning Imaging Absorption Spectrometer for ERS‑2 and, together with Wolfgang Schneider and Paul Crutzen, submitted a proposal to ESA for a study of SCIAS. ESA’s condition was that we were to design an adequate instrument that would fit the resources available on ERS‑2 identified by the ESTEC team: volume 1 m3, mass 40 kg, a data rate of 40 kbit/s and 40 W power. In order to achieve these goals it was necessary to descope SCIA-mini and, after consultations within MPI, I proposed that the nadir mode had the advantage that it could make both tropospheric and stratospheric measurements. In the autumn of 1989, I proposed that TNO- TPD join the study, and Huib Visser and Kees Smorenburg became part of the Phase A industrial study team for the optics for GOME. The intention of the ESTEC team was to obtain a design that, for reasons of geographical return, could be developed in Phase B in Italy. The study team included myself as lead scientist, W. Schneider from MPI Mainz, who defined the performance and instrument requirements, P. Dubock, A. Lefebvre, R. Zöbl, P. Edwards, B. Pieper, E. Leimann and B. Duesmann from the ESTEC ERS team, and H. Visser and C. Smorenburg from TNO-TPD, who developed the optical design based on the smaller, reduced-complexity version of SCIAMACHY. Valuable inputs were provided by J. Geary from the SCIAMACHY team on detector selection and G. Mount, then at NOAA, on the readout electronics used for example in the NOAA instruments (Mount et al., 1987). Nevertheless, the correlated sampling approach used in SCIAMACHY to reduce the readout noise was not implemented. This team quickly developed a viable concept using much of the development work undertaken for SCIAMACHY in previous years. The instrument was then renamed the Global Ozone Monitoring Experiment, GOME, by Chris Readings. Peter Dubock became the GOME Instrument Manager for the ERS team at ESTEC, and I became the lead scientist of GOME, complementing my role as the PI of SCIAMACHY. The first meeting of the GOME Science Steering team took place at ESTEC on 15 February 1990, chaired by Chris Readings and P. Dubock. The participants included members of the SCIAMACHY Science Team and some ESA nominees. The initial team comprised myself as GOME Lead Scientist and SCIAMACHY PI (MPI Mainz), Ulrich Platt (University of Heidelberg), J.-P. Pommereau (CNRS, France), C. Muller (BIRA, Belgium) and A. Goede (SRON, the Netherlands) from the SCIAMACHY science team, B.J. Kerridge (Rutherford Appleton Laboratory, UK) and R. Guzzi (IMGA/CNR, Italy). The second meeting of the now renamed GOME Science Advisory Group (GSAG), took place under my leadership at the MPI in Mainz, with W. Schneider, D. Perner and P. Crutzen (MPI) G. Fiocco/A. di Sarra (University of Rome), H. van

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der Woerd (Netherlands Institute for Public Health and the Environment, RIVM) and K.V. Chance (Smithsonian Astrophysical Observatory). Rudolfo Guzzi and Adelbert Goede also joined the group in this first phase. Chris Readings chaired the subsequent GSAG meetings until his retirement in 2001. I acted as scientific secretary or organised this position for the next eight years. Some 23 years later, GSAG still exists, with several of the original members. GSAG now supports GOME‑2 on the MetOp series of platforms and is managed by Rosemary Munro of Eumetsat and Tobias Wehr of ESA. The SCIAMACHY project comprised the development of SCIAMACHY for flight on the Polar Platform and of the SCIA-mini, later SCIAS−GOME proposals. The research and technological development in this project provided the basis for the GOME Interim Science report. The heritage of SCIAMACHY and GOME included:

—— the European development of Differential Optical Absorption Spectroscopy (DOAS) in the 1970s by Ulrich Platt and Dieter Perner; —— the remote sensing research of John Noxon at NOAA and the Solar Mesosphere Explorer (SME) team at the Laboratory for Atmospheric and Space Physics (LASP) in Boulder, USA; —— the development of ground-based and balloon measurements by Jean-Pierre Pommereau and Florence Goutail of the Système d’Analyse par Observation Zenithale (SAOZ); and —N— ASA’s BUV, SBUV and TOMS instruments that began measurements in 1974.

Early in the SCIAMACHY project, I recommended that D. Heath, who with A. Kruger led the development of BUV, SBUV and TOMS at NASA, become a consultant on the design and development of radiometric calibration of GOME and SCIAMACHY. This led to a fruitful collaboration with the NASA team at the Goddard Space Flight Center in Maryland. In particular, the contributions of Earnest Hilsenrath, James Gleason and Scott Janz to the GOME and SCIAMACHY Science Advisory Groups (GSAG/SSAG) were invaluable for the two projects. The SCIAMACHY science team worked largely on its own resources to support the scientific development of both SCIAMACHY and GOME. ESA did manage to use their general studies funding mechanism to support some focused studies on key aspects of the science and the algorithm developments. SCIAMACHY is the more substantive instrument, having nadir, limb occultation and calibration viewing modes, whereas GOME, being a smaller version of SCIAMACHY, could be much more readily realised. Utilising the inputs from the preparation for SCIAMACHY, and the SCIA- mini industrial proposal, the industrial Phase A study for GOME took place in early 1990 by the ESA ERS team, led by P. Dubock and R. Zöbl, who were responsible for the whole of ERS at the at the time, and myself as lead scientist. The GSAG, under the leadership of Chris Readings and myself, wrote the GOME Interim Science Report (Burrows et al., 1993), which, together with the successful design studies developed by the above team, was used as the basis for ESA’s proposal to the PB-EO and the ESA Council to develop GOME for flight on ERS‑2, which at that time was planned to be launched in 1994. This proposal was successful and in June 1990 the GOME ESA project began in earnest. However ESA's PB-EO had demanded, the development of GOME was not to interfere with the production of ERS‑2, and no delay in the development of ERS‑2 to resolve GOME development issues would be allowed. The GOME Phase B industrial study took place between July 1990 and March 1991. The contractors for GOME Phase B comprised Officine Galileo, in Florence, who were responsible for the GOME optical subsystem; Labem Italia in Milan, who were responsible for the electronics; and TNO-TPD in the Netherlands, who supported the development of the optical design and prepared the optical calibration unit.

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This distribution of effort reflected the demands of geographical return on resources by ESA member states. This is one of the realities of European development of its space segment. It has its merits, but obvious limitations. In spite of the excellence of the subsequent work undertaken in Italy, the loss of some key industrial partners of the SCIAMACHY industrial consortium, including the Prime Contractor Dornier was, at least in my view suboptimal with respect to the overall development. From that point onwards the development of SCIAMACHY for the Polar Platform, later renamed Envisat, and that of GOME began to diverge. The SCIAMACHY science team, which participated within the GSAG, was able to remain fairly united with respect to comparability of performance. The GOME baseline design review took place in Noordwijk, the Netherlands on 12 and 13 March 1991, after which ESA led the organisation of Phases C and D. During this period the GSAG created subgroups that wrote specifications and requirements for the characterisation and calibration, data and algorithm and validation of GOME. These were endorsed and reviewed together with the GOME instrument specification document, and provided ESA with a clear set of requirements for the GOME development.

8. GOME Industrial Phases C and D

At the end of the GOME Phase B, Peter Dubock took on new responsibilities within the Envisat project, later managing the Atmospheric Dynamics Mission (ADM), and eventually becoming Inspector General of ESA before retiring. Jörg Callies, who had previously worked on DOAS, joined the ERS team to support GOME. Achim Hahne, who had been ESA’s Phase A study manager for SCIAMACHY, became ESA’s GOME project manager, with Alain Lefebvre becoming responsible for the development of the electronics. This team, who reported to P. Edwards and R. Zöbl at ESA, managed the industrial development of GOME at Officine Galileo in Florence, led by A. Mariani, R. Veratti, M. Fibbi and M.S. Mazzoni, and supported by many others. Under the guidance of the Officine Galileo team as the Prime Contractor, the development of GOME proceeded well, meeting all the ERS‑2 project deadlines. Officine Galileo were flexible and facilitated changes in their schedule needed to obtain the required instrument performance. As GOME was new, the engineering approach was to create a breadboard/engineering model and then the flight models. The Critical Design Review took place in May 1993. At this meeting the issue of straylight in channel 1 was addressed and this resulted in the channel 1 and 2 boundary being adjusted. This had several unforeseen consequences, including the loss of spectral overlap between channels 2 and 3. Later it was discovered the spectral resolution, as measured by the full width half maximum (FWHM) of the instrument spectral slit function, changed rapidly between 314 nm and 425 nm in channel 2. Both a breadboard model and two flight models of GOME were made. Overall the approach worked well with the characterisation of the breadboard, providing essential knowledge for the Flight Model (FM) development. The breadboard model was also used in the validation campaigns, making zenith sky measurements. The Officine Galileo and the entire industrial team are to be commended for their efforts to produce and deliver the GOME FM on such a tight schedule. As the GOME Lead Scientist, I continued to support ESTEC and the Officine Galileo industrial team developing the GOME instrument. The key issue in such collaboration is to maintain the performance of the instrument and the resultant quality of the Level 1 and 2 data products, but to do so by staying within the financial envelope foreseen for the project. This period was an exhilarating, if challenging, phase of the development of GOME. In particular, the analyses of the characterisation and calibration measurements with GOME,

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which demonstrated the potential capability of the instrument inflight, were enthralling. Many of the GOME preflight characterisation and calibration activities, which were designed to meet the GSAG Characterisation and Calibration requirements document, were contracted to the department led by R. Hoekstra at TNO-TPD. Carina Olij (later van Eijk-Olij), Erik Zoutmann from TPD-TNO and colleagues organised this project. This was also supported by Officine Galileo as the GOME prime. The TPD-TNO team later took on similar responsibilities for the SCIAMACHY spectral and radiometric calibrations. The accuracy of pre-flight and in-flight calibrations is of key importance for the Level 0 to 1 processing as they provides many parameters required for processing to generate Level 1 and Level 2 data products. Two GOME FMs were developed and the Breadboard Model was later upgraded to a Flight Model. The GOME FM, selected for flight, was calibrated and delivered on time for integration to the ERS‑2 platform in 1994. The ESA and industrial GOME team are to be congratulated on their efforts. It is a pity that there were no opportunities to fly the other FMs. The GOME FM worked excellently for 16 years and was switched off when ESA decided to decommission ERS‑2 in July 2011.

9.e Th Development of Retrieval Algorithms and the GOME Ground Segment When the ESA PBEO and the ESA Council agreed on the project GOME for ERS‑2, funding was made available to support the industrial development of the hardware. The LS for GOME supported by GSAG, and the GOME Team at ESA, agreed that the development of Level 0, 1 and 2 data products from GOME, provided to the scientific community, was the idealised strategy to maximise data usage. The German BMFT agreed to develop a processor for GOME data product generation within the DLR. The development of the Level 0 to Level 1 processor, which determines the extraterrestrial and the Earth shine radiance upwelling at the top of the atmosphere as a function of wavelength, required careful collaboration between the calibration scientists and experts within the GSAG and DLR in particular with respect to polarisation. Wolfgang Balzer of the DLR led this project. The development of suitable algorithms for Level 1 to 2 processing required the definition of the data products and the development of scientific prototype by the science team. These could then be operationalized within the ERS‑2 GOME ground segment. Initially only a limited number of data products were agreed by ESA but these grew over the years as the science team developed and demonstrated its competency. After the successes with GOME and SCIAMACHY, a larger remote sensing team within the optical spectroscopy department of the MPI was required. This group had the task of supporting the development of the GOME and SCIAMACHY instruments and data product retrieval algorithms. After an excellent collaboration over a decade, Wolfgang Schneider left the MPI to take up a position at the DLR in 1991. Wolfgang has remained a strong supporter of SCIAMACHY, GOME and atmospheric remote sensing and now advises the German minister of economics on space issues. In 1989 Vladimir V. Rozanov from the atmospheric physics research group led by Yuri Timofeyev at Leningrad/St Petersburg State University, joined the SCIAMACHY/GOME team at the MPI in Mainz. Using his expertise on radiative transfer modelling, Rozanov addressed a key issue for the development and processing of the Level 1 to 2 retrieval algorithms, i.e. the development of a fast radiative transfer forward model. This we called GOMETRAN (Rozanov et al., 1997) and later was later further developed to become SCIATRAN, which is

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available from the University of Bremen. These models are suitable for use with either optimal estimation or DOAS techniques (Platt & Perner, 1980). One unique feature of the radiative transfer model GOMETRAN/SCIATRAN was the linearisation procedure for calculating weighting functions developed by Rozanov to meet the need for a rapid convergence during fitting iterations. The development of SCIATRAN continued over the next 23 years. It now comprises a family of programmes, which simulate all relevant absorption scattering and emission phenomena in the atmosphere and at Earth’s surface including Raman scattering, fluorescence from land plants , oceanic biomass, and atmospheric emissions, for all relevant viewing geometries, e.g. nadir, limb, solar or lunar occultation and ground. SCIATRAN calculates both as a scalar or a vector model, where the polarisation is explicitly considered. The SCIATRAN forward radiative transfer model and inversion programs have become workhorses for atmospheric science. There is now a significant and large group of SCIATRAN users. Dorothy Diebel joined the MPI team in 1991 and provided valuable support to the development of algorithms and management of the growing GOME and SCIAMACHY activities, prior to her leaving to join Eumetsat, where she worked initially on GOME‑2 issues in 1995. Robert Spur also joined my research group in 1991, leaving in 1994 to join the DLR. He provided scientific secretary support for GSAG and programming support for the development of GOMETRAN. In 1991 I was elected Professor of Physics at the University of Bremen. As a result part of the optical spectroscopy group at the MPI moved with me in early 1992 (D. Diebel, A, Ladstatter Weißenmayer, M. Weißenmayer, V.V. Rozanov and R. Spur). Thereafter I continued a close collaboration with Paul Crutzen, Dieter Perner and Geert Moortgat at the MPI in Mainz. At the University of Bremen my research group and that of Klaus Künzi were initially part of the Institute of Remote Sensing. In 1993 Klaus Künzi, Wolfgang Roether and I formed the Institute of Environmental Physics (IUP), which included the Institute of Remote Sensing as the section dedicated to long-term projects using remote sensing using instrumentation from the ground, ship, aircraft or satellite platforms. In the summer of 1992 an international atmospheric conference was held in Berlin, organised by Harold Schiff (1923–2003) from York University in Canada and Ulrich Platt, who had moved to the University of Heidelberg in 1989. This provided an opportunity to showcase GOME and SCIAMACHY scientific objectives and developments. I organised a session on atmospheric remote sensing, which focused on DOAS applications and the potential of GOME and SCIAMACHY. Ulrich Platt has been an excellent colleague over the past 25 years, participating with his research team initially at the Jülich Research Centre and from 1989 onwards at the University of Heidelberg, in the development of the GOME and SCIAMACHY retrieval algorithms, the validation of data products and data exploitation. Our research groups have worked closely together on the development of ground-based, aircraft-borne instrumentation and some balloon experiments. Over the past two decades, the University of Bremen team has benefited from the scientific collaborations and support of the Institute of Environmental Physics at the University of Heidelberg and that of the Max Planck Institute for Chemistry. From 1992 onwards, the University of Bremen became the Principal Investigator or Lead Scientist Institution for both GOME and SCIAMACHY. A large effort was invested by the University in this evolving team to provide scientific support for these exciting, at the time, novel and challenging projects. In my years in Bremen a number of young scientists, who would make outstanding contributions to the development of GOME, SCIAMACHY and the remote sensing of atmospheric constituents from space, ground or aircraft, began their research careers. These included Andreas Richter (DOAS ground-based and satellite measurements of trace gases), Michael Buchwitz (detector development, radiative transfer model development and trace gas retrieval), Thomas Kuroso (cloud and

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aerosol radiative transfer modelling), Bernd Deters and Stefan Himmelmann (kinetics and spectroscopy of trace atmospheric constituents), Ricarda Hoogen (retrieval of vertical profiles of 3O ), and Michael Eisinger (retrieval of trace gases from ground-based and satellite observations; he later moved to ESTEC to work on GOME and GOME‑2, and more recently EarthCare). In 1995 Mark Weber, Johannes Orphal, Heinrich Bovensmann and Stefan Noel joined the IUP/UB team to undertake research dedicated to the retrieval of O3 total column and vertical profiles, laboratory spectroscopy and databases, sensor development and calibration and characterisation, funded through the German Aerospace Agency (DARA, now DLR). These activities addressed in part the development of reterival algorithms for data products from GOME and their validation. Another important aspect of the GOME calibration was the preparation of the Calibration Apparatus for Trace Gas Absorption Spectroscopy (CATGAS). Andreas Türk and then Angelika Dehn, now at ESRIN, worked for their diploma dissertations on the development of the CATGAS, supported by the experimentalists mentioned above. Although ESA originally wanted to restrict this period of calibration, the team from the University of Bremen worked double shift system over the two weeks given to them to measure at five temperatures the O3 and NO2 spectra using GOME and spectra of SO2, and, very usefully as it turned out, SO2 at room temperature. These absorption spectra and derived absorption cross sections were then repeated and calibrated at the IUP/ UB and a paper was published. Twenty years later, that paper was one of two milestone publications in the category ‘remote sensing’, selected by the Journal of Quantitative Spectroscopy and Radiative Transfer to celebrate its 50th anniversary in 2010. As a result of these initiatives, the GOME and SCIAMACHY programmes also helped to improve our spectral database. Inadequate knowledge of spectral parameters about the trace constituents in the atmosphere or Earth’s surface are fundamental limitations for remote sensing. Further spectroscopic improvements are needed to exploit the full potential of GOME. In 1992 the SCIAMACHY Science Advisory Group was established and held its first meeting at the University of Bremen. The SSAG and GSAG had in large part complementary memberships. For the first years the Dutch co-PI was Han van Dop of the University of Utrecht. Christian Muller (BIRA) became the Belgian co-PI and has continued in this role up to the present. Han van Dop was succeeded by Adellbert Goede, who led the SRON contributions to GOME and SCIAMACHY prior to his moving to the Royal Netherlands Meteorological Institute in 2001. He continued until 2007 when he was succeeded by Ilsa Aben. After 20 years the SSAG is also continuing to support SCIAMACHY and Envisat. For many years the GOME and SCIAMACY scientists organised workshops for the development of data products, a tradition that continues with the SCIAMACHY data and algorithm subgroup. Between 1990 and 1994 ESA funded two general studies to support the scientific development of aspects for Level 1 to 2 processing for GOME. One was won in a competitive tendering procedure by a team the University of Bremen and comprising the University of Heidelberg and Rutherford Appleton Laboratory with support from the Smithsonian Astrophysical Observatory. A study of aerosols was also undertaken by a team at the University of Lille in France. Later, a study of aerosol and cloud retrieval, led by R. Guzzi at the Italian National Research Council in Modena, aimed at developing the total ozone column and vertical profile retrieval algorithms. All of these studies provided key inputs for the development of the operational processor. The operational processor was completed on time by the DLR and was ready for the launch of GOME. At the same time, the GOME/SCIAMACHY scientists were primed to investigate the full potential of the GOME data information content, using their algorithms running under scientific control, and deliver trace gas, aerosol and cloud data products, which had not been foreseen in the first operational processor. These efforts to develop the retrieval algorithms were key to the success of a mission.

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10.e Th GOME Instrument on ERS-2

Figure 1 showed the ERS‑2 satellite, the GOME‑2 instrument during testing at Galileo Officine and a schematic of the optical layout. The success of the engineers, scientists and administrators in creating both ERS‑1 and ERS‑2 is a great credit to this team. After the launch of ERS‑2, the ESTEC ERS team managed the operation of the satellite during its commissioning phase, while ESOC was responsible for the operation of the instrument in orbit. After the commissioning phase the ESRIN and ESOC team managed the ERS‑2 and GOME project during its Phase E. The primary molecules and parameters targeted for retrieval from the nadir viewing observations of GOME and SCIAMACHY are shown in Fig. 6. The spectral resolutions and coverage of the first four spectral channels of GOME and SCIAMACHY are similar but not identical. For further details about the GOME instrument, see the GSAG’s GOME Interim Science Report (Burrows et al., 1993, 1999, and references therein). For comparison, the SCIAMACHY instrument and targets are described by Burrows et al., (1990, 1995) and Bovensmann et al., (1999). Figure 7 shows details of the GOME instrument as originally configured in Phase B, including the optical path of electromagnetic radiation collected. GOME has only one moving part, the scan mirror. The instrument comprises a scan mirror telescope and spectrometer, and equally important thermal and electronic subsystems (not shown). A key issue for instruments aiming to retrieve trace gas atmospheric absorptions and emissions is the thermal stability of the instrument and the reduction of the electrical and digital noise such that the signal to noise is dominated by the photon shot noise. For this reason the SCIAMACHY instrument is cooled and its temperature actively stabilised. For GOME the detectors are maintained at −37°C to reduce the dark read signal to a minimum, and around the orbit the temperature change is of the order of a few degrees. The impact of this can be readily observed in the small shift of 0.008 nm around the orbit, which fortunately can be adequately taken into account in Level 0 to 1 and Level 1 to 2 processing. The solar electromagnetic radiation upwelling from the atmosphere is collected by the GOME scan mirror and is directed via the telescope onto the entrance slit of the spectrometer. The beam is then collimated and directed onto a predispersing prism forming an intermediate spectrum. The two UV channels are selected by a partially reflecting prism. The visible and near-

Figure 6. Left: Target molecules and parameters and the spectral coverage of the instruments GOME on ERS‑2 and SCIAMACHY on Envisat. Right: Differential trace gas absorption cross sections of a selection of target trace gases. (TNO and University of Bremen)

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Figure 7. Schematic diagrams of the GOME instrument (left) showing the four-channel spectral channel measuring simultaneously in nadir, and the ERS‑2 in orbit with the GOME scan pattern in forward scan (right). (ESA)

IR electromagnetic radiation is then directed onto a dichroic mirror that separates the radiation into two channels. Each spectral channel comprises a transmission grating optics producing the spectrum focused at the linear one-dimensional detector array with 1024 detectors. In this manner the region between 230 nm and 793 nm is measured simultaneously. The spectral resolution, defined as the Full Width at Half Maximum (FWHM), is approximately 0.2 nm below 400 nm, and 0.4 nm above. The channel 1 and 2 boundary was changed after the review of the GOME breadboard model performance and the new boundary was selected to be 315 nm. The original pictures from Phase A in Fig. 7 do not show this change. About 4% of the incoming radiation, which is polarised parallel to the entrance slit, is reflected from the predispersing prism onto a small array of detectors, corresponding to channels 2, 3 and 4, which are the polarisation monitoring detectors. Combination of these measurements with those of the spectrally resolved channels enables the polarisation of the upwelling radiation to be determined and then accounted for using the radiometric calibration. The data rate available to GOME limited its spatial resolution. A scan mirror was used to direct the image of the entrance slit of the spectrometer onto the ground. This resulted in an instantaneous field of view of the order of 40 km (along track) × 7 km (across track). The Reticon diode array detectors in GOME can be read out at different frequencies. This enabled channel 1 to be separated into channel 1a and channel 1b beginning around 280 nm. The mirror was scanned and the detectors read synchronously. This resulted in the channels 1b, 2, 3 and 4, having a spatial resolution of 40 × 320 km. Channel 1a was read out less often to increase its signal to noise by on chip detector integration. The scan pattern for the majority of the measurements after the commissioning of ERS‑2 led to 320 × 40 km for channels 1b to 4, with channel 1a having a 200 × 960 km footprint. For three days in 30 the higher spatial resolution mode was used by reducing the scan from 960 to 240 nm. This resulted in 80 × 40 km for channels 1b to 4 and 240 × 200 km for channel 1a. The extraterrestrial solar irradiance was observed once per day by turning the scan mirror when the Sun came into the view of the calibration unit. For about six months of the year lunar measurements are possible close to full . These measurements, combined with those of the onboard emission line lamp, the LEDs in each channel and the measurements of the dark signal provided the basis for the inflight calibration of GOME. In addition vicarious calibration was achieved using selected targets.

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11. ERS‑2 and GOME in Orbit

ERS ‑2 was launched at 22:30 h on 20 April 1995 from the European in French Guiana. My thanks go to ESA for inviting me to this event. Sadly, the launch coincided with the Oklahoma City bombing and the world’s media perhaps did not recognise the long-term importance of Europe beginning global measurements of air pollution from space. The ESTEC ERS‑2 team were busy up to the launch. For GOME as an optical instrument it was critical to maintain the cleanliness of the instrument. The ERS‑2 was launched by the Ariane 4 V72 into orbit. This had the advantage of maximising the fuel on-board for the mission. At the decommissioning of ERS‑2, which began in July 2011, there still remained more than adequate fuel for many years of operation. GOME was initially kept warm and allowed to outgas. A critical issue for all optical instruments is the condensation of water and ice on the cold parts of the instrument. Water is outgassed from the surface of the multilayer insulation and the electrical boards, resulting in thin layers of ice that produce interfering etalons and affect radiometric calibration. In spite of the efforts to outgas the instrument indeed GOME detectors when cooled do grow a small layer of ice from the atmosphere within ERS‑2 and GOME. Although this needed to be taken into account for the absolute radiometric calibration, it did not significantly reduce the performance of the instrument. This ice layer takes about two days to stabilise after the cooling of the detectors is switched and can be monitored by the observations of the Sun and the internal line lamp. The thin ice layer combines with the intrinsic etalon produced by the passivating outer layer on the diode array detectors. With hindsight the selection of a 3 µm passivating layer was suboptimal and 1 µm would have been more favourable. This is because the combined complex etalon forming the ice layer and the passivating outer Si layer of the detector can have features that correlate with trace gas absorption. Over time two issues did arise one of which was expected and one not. The slow absorption of UV absorbing material was expected because the phenomenon had been observed with all similar instruments in space. My own explanation is that organic compounds released from the electrical boards of the instrument under the hard ultraviolet condition present in space result in low-volatility organic compounds having π orbitals, i.e. delocalised orbitals that absorb in the 200−400 nm region. It is well known, for example, that glycine and ketene, which exhibit such bonding, are present in gas clouds in space. These types of compound, which have low vapour pressure, then deposit on the instrument. In the case of GOME this deposition was identified to be primarily on the scan mirror. This created an absorbing etalon that grew over the years reducing the throughput in an oscillating manner. Snel and Matthijs Krijger from SRON developed a physical model to explain this behaviour for both GOME and SCIAMACHY, and the thickness of the layer can be determined. Through inflight characterisation procedures, the impact of this layer could be minimised. The second and unexpected issue of this type was the outgassing of the dichroic mirror, which resulted in a slowly shifting etalon particularly in long- wavelength part of the channel 3 between 440 nm and 600 nm. This behaviour could in part be accounted for in retrieval studies and removed in the final reprocessing of Level 0 to 1 processing of the GOME datasets. As part of the lessons learned from the SCIAMACHY and GOME‑2 instruments, the gratings selected to have lower polarisation sensitivity. Overall, GOME worked almost perfectly throughout its lifetime in space. The errors in the instrument, such as the etalons described above, were design risks , which had an impact but could in large part be accommodated for in the retrieval algorithms. In June 2003, after eight years of data delivery, the second backup tape recorder system failed after only six months in operation. After this time, data from ERS‑2 could only be downlinked by direct broadcast. At the recommendation of the Lead Scientist supported by GSAG, ESA built up a system

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allowing about 30−40% of the GOME data to be downlinked. Wolfgang Lengert, who later became the ERS‑2 Mission Manager, developed this system. Not all at ESA were initially in favour of this approach. For example the then ERS‑2 Mission Manager and others were concerned about the cost implications. As a result it took some time to develop this system but ESA’s partners around the world, including NASA, helped by using their ground stations to downlink the data. Later, as a result of the failure of the ERS‑2 gyros, the ERS‑2 team developed a unique steering approach for the platform. This reduced the observations of the extraterrestrial solar irradiance for GOME. However the data obtained from GOME was of excellent quality and the retrieval algorithms were successfully adjusted to take these effects into account. GOME continued to work excellently up to the time that ERS‑2 was decommissioned by ESA in July 2011. It showed no substantial or unexpected behaviour throughout the 16 years of its measurement period in space. One important point to make with respect to Earth observations is that there are two intrinsically unique and important aspects of making measurements of atmospheric composition and surface parameters from space. The first is their global or, in the case of geostationary satellites, regional character, and the second depends on their duration. Measurements from the ground or from aircraft provide snapshots or local temporal records, whereas those from space provide global or regional data over potentially long periods of time. The value of a dataset for many scientific applications from an orbiting platform is proportional to a higher power of the number of years of well calibrated and comparable data. The longer the dataset, the more valuable it becomes. GOME, with its eight years of global coverage and an additional eight years of 30−40% coverage of the globe, has been a trailblazing success. The scientists and engineers who built GOME and ERS‑2 and controlled its performance in space are to be congratulated on this unique achievement. It is one of a limited number of instruments that has worked for such a long time. I have no doubt that GOME would have continued to work well for many years if ERS‑2 had not been decommissioned. After the launch of ERS‑2, and the GOME commissioning phase, formal responsibility for GOME was transferred from ESTEC to ESRIN. The ESTEC team began then to concentrate on the development of GOME‑2 as part of the MetOp series of platforms built for Eumetsat. As expected, the data processing of any instrument evolves during its life in space, for a number of reasons. One significant factor has been the continuous improvement in computer storage systems and the speed of the world wide web, providing an almost exponential growth in calculation power and the ability to manipulate mathematically and transfer large datasets even remotely. In retrospect, the data rate of GOME at launch seems trivially small. But in 1995 a significant logistical effort was involved in ensuring the delivery of data to the then small team of scientific and pre-operational data users. GOME provided what at the time were uniquely large amounts of data and much attention was paid to their calibration. Given the necessary funding and staff, the origins of systematic errors in retrieval algorithms and degradation of the instrument inflight can be identified by instrument monitoring and removed by improving the algorithms and reprocessing of the data. This reprocessing of GOME data and the addition of new operational data products is an evolving process. Similar to NASA projects, in the GOME/SCIAMACHY project, data reprocessing typically takes place on a two-year cycle. The results from the outset were outstanding but the improvements in precision and accuracy through this reprocessing cycle were of key importance in providing a dataset of value for environmental, atmospheric and climate change research. In addition to their use in scientific applications, these data are required to monitor objectively atmospheric pollution and thereby provide datasets for use in the development of international environmental policy, in particular for their transparency and verification.

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12. Retrieval of Data Products from GOME

As a passive remote sensing instrument, GOME measured the upwelling radiation from the atmosphere and the extraterrestrial solar irradiance. The detectors used in GOME are monolithic linear diode arrays. GOME has 1024 detectors per spectral channel and three Polarisation Measurement Devices (PMDs). In channel 1 about 75% of the channels are read out. The linear diode arrays comprise 1024 Si semiconductor capacitances, which are charged on reading. The interaction of electromagnetic radiation with the detectors discharges the capacitance. The linear array detectors are read serially by a multiplexer, which recharges them, as it reads. An analogue to digital converter then generates the Level 0 product, which is stored either on the tape recorder for periodic downlink or, after June 2003, recovered by direct broadcast when close to a network ground station. The latter system was established by Wolfgang Lengert, who worked on GOME and was the last ERS Mission Manager at ESA. The geophysical Level 1 data product is generated by the Level 1 processor. The Level 1 data products for GOME are the extraterrestrial solar irradiance and the Earthshine radiance at the solid angle of viewing. The calibration of GOME involved both extensive preflight characterisation and calibration of radiometric and spectral measurements and inflight calibration. The latter requires the determination for the ~3840 individual diode array detectors and the three PMDs of GOME of the following:

—— the radiometric calibration, which uses the dark signal during eclipse; observations of the Sun via the diffuser plate on a daily basis; observations of the full Moon, when available, via the diffuser plate, for long-term radiometric calibration; and the LEDs in each spectral channel; —— the spectral calibration, which uses periodic observations of the Pt/Cr/Ne hollow cathode discharge line emission lamp, and observations of Fraunhofer solar lines.

The Level 1 to 2 processor is used to invert the retrieved radiance and irradiance into Level 2. Utilising the Beer–Lambert law and considering radiative transfer through the atmosphere, the intensity of solar radiation reaching GOME can be approximated by the following equation:

I= cI0 exp - 'i SCDi - * i SCDi - Ray SCDRay - SCDMie ^mh ^ m h $ / v^ mh / v^ mh v^ mh ^ v hMie ^ m h . i i (1)

I^mh= cI0 ^ m hexp -/ v'i ^ mhSCDi -/ v* i ^ mhSCDi - vRay ^ mh SCDRay - ^ v hMie ^ m hSCDMie $ i i .

where I0(λ) is the intensity of solar radiation reaching Earth, i represents the gas, c is a factor that depends on the surface spectral reflectance and multiple scattering in the atmosphere; the absorption cross-section, σ(λ), of the absorbers separated into the slowly varying low-pass contribution σ*(λ) and strongly varying high-pass or differential partσ′ (λ) σ(λ) = σ*(λ) + σ′(λ); SCDi is the slant column density of the gas; SCDray is the slant column of air; SCDMie is the slant column of particles. This can then be approximated by

p I^mh= cI0 ^ m h -/ v'exp i ^ mhSCDi -/ ap m (2) $ i p .

p where apλ is a polynomial that fits the broadband or low-pass scattering and absorption terms, and the high-pass or differential structure term Σσ′i(λ)SCDi yields the set of SCDi. The equation is written as

I0 ^mh p ln = //v'i ^ mhSCDi + ap m (3) I^mh i p

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Standard or simple DOAS works best for weak absorptions when ln (I0(λ)/I(λ)) approximates well to (ΔI/I0(λ)), where ΔI = I0(λ) – I(λ). The I0(λ) is the solar extraterrestrial spectrum measured by GOME over its diffuser plate with the Doppler shift taken into account. The I(λ) signal is the Earthshine radiance upwelling from the atmsophere measured by GOME. In the fitting it is necessary to account for gaseous absortions, the Raman infilling of Fraunhofer lines known as the ring effect and some undersampling structures. Examples of this standard fitting of the gases NO2, SO2, HCHO and BrO are shown in Figs 8−11 for a single ground scene at different locations around the world. Having derived the Differential Optical Density (DOD) of a gas, the slant column of the gas is obtained by dividing the DOD by the differential absorption cross section. This is then converted into a vertical column amount by dividing by the Air Mass Factor (AMF). These calculations are performed using numerical models that solve the radiative transfer equation. For this purpose the GOMETRAN/SCIATRAN was developed (Rozanov et al., 1997; Buchwitz et al., 2001, and references therein).

Figure 8. Simple DOAS fit of NO2 reference spectrum in the spectral region 425–450 nm from GOME measurements above Europe. (A. Richter and J.P. Burrows, University of Bremen).

Figure 9. Simple DOAS nonlinear least- squares fit of SO2 in the spectral region 312–328 nm from GOME measurements of the plume of the Nyamuragira volcano in DR Congo, close to the equator, 4 December 1995. (M. Eisinger, A Richter and J.P. Burrows, University of Bremen)

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Figure 10. Nonlinear least-squares fit of the differential optical density of HCHO for a ground scene over Africa. (F. Wittrock, A. Richter and J.P. Burrows, University of Bremen)

Figure 11. Nonlinear least-squares fit of the BrO differential optical depth, retrieved from a single GOME scene. (A. Richter and J.P. Burrows, University of Bremen)

For stronger absorptions, modifications of the DOAS approach are used to fit the vertical column taking the radiative transfer into account. Similarly, the optimal estimation approach can be used (Rozonov et al., 1998). This type of retrieval is also called the full retrieval method. Briefly, in this case, a forward radiative transfer model is used to generate the Earthshine expected at the top of the atmosphere, and the vertical profiles and concentrations of trace gases are adjusted until a convergence criterion is met. Vertical profiles of 3O were retrieved in this manner from GOME observations (see Hoogen et al., 1998). For further details of the preparations for GOME, see Burrows et al., (1994). The GOME Level 2 data products envisaged at the beginning of the project comprised the following:

—O— 3 total column amount, —O— 3 vertical profiles, —— total column amounts of NO2, —— tropospheric column amount of NO2, —— total column amount of SO2, —— total column amount of HCHO,

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—— total column amount of BrO, —— total column amount of OClO, —— total column H2O, —— cloud fraction within a scene and cloud top height.

In addition the total column amounts of the trace gases IO and CHO.CHO, which were retrieved first from SCIAMACHY and then GOME‑2, will be retrieved in the future from GOME. The cloud fractions and cloud top heights are obtained using the O2 A‑band absorptions around 760 nm. The GOME data products have been produced and some interesting results are highlighted below. First, however, one trivial lesson learned from GOME is that the value of the data products is dependent on the investment in the development of the data products and their distribution system. A significant fraction of the investment in the development of the retrieval algorithms was funded through and thus relied on the idealism of the scientific community. There is clearly an imbalance between ESA’s mandate to invest in the industrial procurement of instruments and the organisation of science teams to exploit the data. NASA appears to work on a more systematic approach, creating instrument support for validation and scientific exploitation. In this context GOME and SCIAMACHY have been a voyage of discovery both for atmospheric remote sensing and the development of human capacity and understanding the limitations in the current ESA and national space agency approaches to deliver scientific return as opposed to support for the development of hi-tech industry. Although in many respects GOME established the template approach for trace gas retrieval in European remote sensing, the approach did not provide adequate resources for the scientific exploitation. The generation of data products and their use in research and applications was in some senses fortuitous, relying on the research infrastructure and the idealism of many researchers. This is particularly challenging as in the case of GOME the experiment had a life of over 20 years. The differences in the ability to fund the industrial contractors, the scientific performance in space and that specifically targeted for the scientific exploitation of the data are dramatic. Until this issue is addressed and the delivery of scientific data products and their exploitation given adequate resources, the full value of the investment of both resources and intellectual effort will not be realised.

13. Scientific Exploitation of GOME Data

This section provides examples of the unique set of data products from the SCIAMACHY project, and in particular GOME. Solar, stratospheric and tropospheric examples are provided. For tropospheric composition, a comprehensive explanation of retrieval algorithms and compilation of results was published in 2011 (Burrows et al., 2011).

13.1 Extraterrestrial Solar Spectral Irradiance, Proxies and Solar Activity

Quantification of the extraterrestrial solar irradiance as a function of wavelength is of fundamental importance for our understanding of the Earth system and of the interactions between the Sun and Earth’s atmosphere. During its calibration GOME made some unique measurements of the solar spectral irradiance (SSI) between 230 nm and 793 nm at spectral resolutions of between 0.2 from 230 nm to 400 nm, and 0.33 nm between 400 nm and 793 nm. Absolute radiometric calibration requires extra care and complete knowledge of the inflight degradation of the instrument in space, this remains a challenge. The absolute calibration of the SSI from GOME requires further focused study using measurements of the Moon and target regions of Earth of

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Figure 12. Solar activity measured by GOME and SCIAMACHY. A composite of the Mg II index including the contribution of GOME. (M. Weber and J.P. Burrows, University of Bremen)

known surface spectral reflectance. GOME has nevertheless produced a unique record for solar and climate science. Proxies for solar activity are very valuable for deriving solar behaviour. The Mg II index is one such proxy, which measures the depth of the solar Mg II Fraunhofer line. The record of the Mg II index derived from GOME by Mark Weber is shown in Fig. 12. The solar cycle is readily observed.

13.2 Stratospheric and Upper Atmospheric Science

In the 1990s a key issue for stratospheric science was the chemical depletion of O3 above the northern hemisphere in spring. The complex Brewer–Dobson circulation, coupled with wave activity, is such that fortunately O3 is more abundant above the northern hemisphere and a warmer stratospheric polar vortex. This results in much better protection for the biosphere from biologically damaging radiation in the northern hemisphere than in the southern hemisphere. Over the period 1995–2000 the research programme on stratospheric ozone of the German Ministry of Science and Technology, through both its support for the German Aerospace Agency (DARA, later DLR) and the national ozone research programme, provided funding for our research using GOME data in Bremen. R. Zellner (University of Duisberg-Essen), coordinator of the national programme, supported by U. Schmidt (University of Frankfurt). They and their colleagues were strong supporters of the GOME activities. An additional source of funding was from the EU Framework Programmes. The then Stratospheric Unit within the European Commission and its Ozone Coordinating Unit, organised by John Pyle and Neil Harris of the University of Cambridge, also supported the development of the GOME science applications. My colleagues Klaus Künzi from the University of Bremen and Richard Wayne from the University of Oxford also provided great support in these years. This enabled the team in Bremen and our colleagues across Europe to begin using GOME data for research. Unfortunately ESA did not or was not allowed to provide direct support for scientific exploitation of GOME data. The Global Monitoring for Environment and Security programme, now renamed Copernicus, was a double- edged sword with respect to funding for scientific applications. This was and is because research is explicitly not funded by this programme.

13.2.1 Total Column of O3

The total column of O3 is retrieved using O3 absorption in the Huggins bands typically between 325 nm and 335 nm. This spectral window was recommended

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by the IUP/UB team and its partners in 1994 as an optimal spectral window for the first retrievals of the total column amount of 3O from GOME. This spectral window has become a standard and has been used for the retrieval of total O3 from GOME, SCIAMACHY and GOME‑2. There is more information about O3 available in the GOME measurements, which can be used in the future to improve the precision and accuracy of the measurements and thereby provide more information about O3 changes in the atmosphere. Figures 13 and 14 were produced using a merged dataset of GOME, SCIAMACHY and GOME‑2 data, generated at IUP-UB. Figure 14 shows the development of the ozone hole over the northern hemisphere, retrieved using the so-called weighting function modified DOAS approach (Rozanov et al., 1998). There are now several merged datasets for O3 from the European early morning platform produced using different but related techniques and giving similar results. There are also data assimilation schemes that use the different measurements, but these are considered to combine model and measurement information and are therefore less valuable for trend or change analyses.

Figure 13. The total O3 column amount above the northern hemisphere in March, 1996–2012, shown in a polar projection using the merged GOME, SCIAMACHY and GOME‑2 data record. The background colours indicate the contributions of the different instruments. (M. Weber and J.P. Burrows, University of Bremen)

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Figure 14. The total O3 column amount above the southern hemisphere in October, 1995–2011, shown in a polar projection using the merged GOME, SCIAMACHY and GOME‑2 data record. (M. Weber and J.P. Burrows, University of Bremen)

The colour scheme in Figs. 13 and 14 indicates the different instruments that provided data for the northern and southern hemispheres. In Fig. 14 the behaviour of the O3 hole over Antarctica over the period 1995–2011 was clearly stable, except in 2002 when a sudden warming in the southern hemisphere was first observed. This was studied extensively using GOME and SCIAMACHY, both of which were fortunately working during the southern hemispheric winter and spring of 2002. Many groups worked on this phenomenon. It remains unclear whether this was an unusual event or an early warning of increasing wave-driven circulation in the upper atmosphere.

13.2.2 Vertical Profiles of O3

The derivation of the vertical profile of 3 O from GOME measurements is one of the more challenging retrievals. The idea was first suggested by Singer & Wentworth (1957). The concept utilises the wavelength dependence of penetration depth of solar ultraviolet radiation to invert mathematically a vertical profile for 3O (Hoogen et al., 1998, 1999a,b).

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The retrieval approach to determine vertical profiles is explained schematically in Fig. 15 together with some validation results. Different variants of this approach are used by different research teams. The approach is to use a forward model to determine the upwelling radiance in the ultraviolet and potentially also the visible, i.e. covering the Hartley and Huggins bands. The use of the visible spectral region with the backscattered radiation was found to have correlations between the surface absorptions, primarily those of chlorophyll and the Chappuis O3 features. Work on this problem is continuing. However, the chlorophyll absorption has been used for type 1 waters to produce phytoplankton maps. The NASA programme first developed the vertical profile retrieval for BUV and SBUV. They later applied this to SBUV-2 together with the NOAA/NASA SSBUV instrument. A total of nine teams developed different vertical profiling algorithms of relevance for GOME. Figure 15 shows a schematic diagram of the general approach and some results. The issue now is to develop consistent and consolidated datasets using the data from the early-morning platforms. Using GOME observations, the temporal evolution of the O3 concentration at a given potential temperature within the vortex could be observed. Figure 16 compares the O3 concentrations at the potential temperatures of 475K, ~19 km, and 550K, ~22 km, the total O3 and NO2 column amounts on 2 April 1997 for one of the largest ozone holes observed above the northern hemisphere thus far. The depletion of NO2 and O3 is readily observed, the latter being dominant in the lower stratosphere. The right panel of Fig. 16 shows a transect along the orbit of the vertical profile of 3O retrieved from GOME above Europe and the Atlantic. The depleted O3 in the polar vortex is readily identified. The chemical depletion and the increase in O3 resulting from subsidence as the air mass sinks over the lifetime of the polar vortex are clear. Figure 17 shows this behaviour during winter and spring over the period 1997–2000. In this manner, the chemical loss and the dynamical subsidence of O3 at two potential temperatures can be studied during the evolution of the vortex. The average O3 sonde values within the vortex during this period are also shown. Good agreement between sonde measurements and satellite retrieved average

Figure 15. Left panel: The approach used for the retrieval of vertical profiles of O3. Right panel: Some comparisons of the retrieved profiles with the initial guess or a priori measurements from an O3 balloon sonde and from the HALOE instrument on NASA’s UARS (M. Weber, K.‑U. Eichmann, K. Bramstedt and J.P. Burrows, University of Bremen).

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3 Figure 16. Left panels: Retrieval of the vertical profile of O3, showing the O3 concentration (molecules/m ) at the potential temperatures of 2 475K, ~19 km, and 550K, ~22 km, in the lower stratosphere together with the total column of O3 (in Dobson units) and NO2 (molecules/cm ), retrieved from GOME measurements on 2 April 2002. Right: Latitudinal transect along the orbit, showing the high O3 concentration outside the polar vortex and the chemical depletion of O3 within the vortex. (ESA/DLR/M. Weber, K.‑U. Eichmann, K. Bramstedt and J.P. Burrows, University of Bremen)

Figure 17. Average O3 volume mixing ratio (VMR) at 475K within the polar vortex, determined using the GOME profile retrieval for the Julian day of the year in 1997 1998, 1999 and 2000 (blue points), and the values from sonde data (yellow points). (K.‑U. Eichmann, M. Weber and J.P. Burrows, University of Bremen

vortex value for O3 was achieved in these years. This was made possible by European and German funding of polar ozone research.

13.2.3 Stratospheric OClO and NO2 observed by GOME

During late autumn and early winter the polar vortex literally spins up. In the polar night the polar vortex cools, and NOx is converted first to 2 N O5 via the chemical reactions (13), (16) and (27) described in section 3. N2O5 is then transformed on aerosols and Polar Stratospheric Clouds to nitric acid by reaction (28). Eventually, the PSCs become nitric acid trihydrate, HNO3.3H2O, which is the most stable azeotrope. The consequence of these reactions is shown in the

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observations of the total NO2 column amount above the northern hemisphere in January and March 1997 and 2001, see Fig. 18. The loss of NO2 is readily visible. This process has presumably always taken place, but the difference in the past 50 years is that the loading of halogens, resulting from the tropospheric release of ozone-depleting substances, has resulted in a change in the chemistry and the catalytic destruction of O3 in the lower stratosphere at high latitudes in spring. A sudden stratospheric warming is defined as an event where the polar vortex of westerly (eastward) winds in winter slows down abruptly (i.e. over the course of a few days) or even reverses direction. This is accompanied by a rise in stratospheric temperature by several tens of K and is a dramatic meteorological event. In Fig. 19 a sudden warming, which resulted in the polar

Figure 18. Denitrification and loss of NO2 in the polar vortex during the development of the first ozone holes over the northern hemisphere in 1997 and in 2001. (A. Richter and J.P. Burrows, University of Bremen)

Figure 19. Observations of stratospheric warming over Antarctica in 1997 and the total NO2 column density. The polar vortex was pushed away from the pole by a pulse of energy from the tropics, shifting it to lower latitudes where it broke up and dissipated. (A. Richter and J.P. Burrows, University of Bremen)

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vortex being pushed to lower latitudes, is observed in the NO2 vertical amount. The vortex air then mixes with the southern hemispheric air, diluting the low O3 over the hemisphere. An interesting test of our understanding of the stratosphere occurred in 2002, during a major warming event, defined as when the westerly winds at 60°N and 10 hPa (geopotential height) reverse, i.e. become easterly (westward), above Antarctica. This was a unique event in the southern hemisphere, whereas they are regular features in winter and spring above the northern hemisphere, and were first observed above Berlin in late January 1952. The question remains as to whether this was a sign of changing dynamics, resulting from the increased amount of energy in the atmosphere, the result of the absorption by the greenhouse gases released by anthropogenic activity, or a phenomenon that occurs periodically but had not been previously observed in modern times. This situation is similar to the first hurricane observed in the South Atlantic off the coast of Brazil in 2004. Figure 20 shows observations of NO2, OClO and O3, as well as estimates of the potential vorticity and temperature from ECMWF and UK Met Office data assimilation models. OClO is formed in the pathways of the reaction (43) between BrO and ClO. The figure shows clearly that OClO and thus ClO are activated as the NO2 is lost. The temporal behaviour of the OClO slant column density at a solar zenith angle of 90° and the NO2 vertical column between latitudes 70° and 80° is shown in Fig. 21. The latter focuses on the unusual behaviour in 2002 and the range of behaviours from 1996 to 2001. These data have now become diagnostics that are used in the WMO Global Atmosphere Watch reports on the monitoring of the ozone layer to characterise the behaviour of the polar vortex,1 and represent a unique data product provided from GOME measurements. The datasets have been

Figure 20. Observations of NO2, OClO and O3 and estimates of potential vorticity and temperature in the polar vortex before 1 September 2002, during 25 September 2002 and after the first major sudden warming of the polar vortex in the southern hemisphere. (A. Richter, M. Weber and J.P. Burrows, University of Bremen)

1 WMO Global Atmosphere Watch: www.wmo.int/pages/prog/arep/gaw/ozone

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Figure 21. Left panels: OClO slant column densities, observed at a solar zenith angle of 90°, plotted for each day of the year in the northern and southern hemispheres between 70° and 80° latitude. Right panels: The corresponding vertical column densities of NO2. Data retrieved from GOME observations. The ranges of values for the years 1996–2001 are indicated in grey. (A. Richter and J. P. Burrows, University of Bremen) extended by the IUP/UB team to include data from SCIAMACHY. Following the loss of Envisat, these data products can be produced from GOME‑2, which flies an hour earlier than GOME on MetOp-A, with an equator crossing time of 09:30. OClO is an important indicator of the extent of chemical processing within the polar vortices on polar stratospheric clouds. However OClO is rapidly photolysed and as a result its stratospheric loading maximises at large solar zenith angles close to dusk. The appearance of OClO is anti-correlated with the amount of NO2 as this competes with BrO to react with ClO to form ClONO2. As can be seen in Fig. 20, in late September 2002 the polar vortex above Antarctica split into two parts as a result of the flow of energy from lower latitudes. One of the vortices moved to lower latitudes while the second moved back over the pole. This is clearly observed in the total O3, NO2 and OClO. In the lower panels of Fig. 21, the behaviour of NO2 and OClO was clearly affected in 2002 by the sudden stratospheric warming, which was observed for the first time in the southern hemisphere in spring in 2002.

13.3 Tropospheric Science using GOME Trace Gas Composition

Prior to the launch of GOME, measurements of the upwelling UV radiation by the TOMS instrument on NASA’s Nimbus‑7 had been used to observe tropospheric SO2 from volcanic eruptions. This instrument was focused on measurements of the total column of O3 and did not have sufficient sensitivity to provide measurements of SO2 in the boundary layer. The combination of TOMS and SBUV and TOMS and SAGE had been used to estimate the tropospheric O3 loading, and led to the discovery of elevated O3 in the tropics. GOME, proposed as an approach to deliver rapidly an important subset of objectives of the SCIAMACHY project, was the first remote sounding instrument to probe explicitly for tropospheric trace gas composition. The SCIAMACHY concept is intrinsically superior to that of GOME because it measures almost simultaneously the upper atmospheric limb profiles and total column amounts of

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key constituents such as O3, NO2, BrO and aerosols, which are present in the upper atmosphere in significant amounts. When SCIAMACHY and GOME were proposed in the late 1980s, many scientists were sceptical that valuable information about tropospheric composition could be retrieved. For this reason the name GOME focused attention on the instrument’s ability to observe global O3 from space. One of the most important contributions made by the ERS project has been to demonstrate unambiguously through GOME that accurate measurements of key tropospheric trace gases can be made from space. The data rate available to ERS‑2 for GOME was limited, and this in turn limited the spatial resolution of the results. However, GOME’s ability to retrieve tropospheric trace gases emitted by natural phenomena (e.g. biogenic emissions, volcanic eruptions, fires, surface fluxes from the land, the ocean or ice, etc.) and resulting from human activities (e.g. fossil fuel combustion, biomass burning, agriculture, etc.) represented a breakthrough. The amounts and distributions of these constituents, which are controlled by biogeochemical cycling, atmospheric chemistry and dynamics, can be objectively measured from space using nadir observations or, even better, the combination of nadir coupled with limb or occultation observations of the upwelling electromagnetic radiation from the top of the atmosphere. This limitation on GOME arising from the available resource on ERS-2 with respect to the data rate resulted in the poor spatial resolution of GOME of 320 × 40 km but global coverage at the equator in three days and more rapidly at higher latitudes. We knew at the time that we needed much higher resolution to resolve important tropospheric processes but had to accept this compromise. The three-day global coverage was sufficient to catch many phenomena. In addition, the somewhat higher spatial resolution of 80 × 40 km for 10% of the GOME observation time enabled assessments of the impact of the lower spatial resolution on our interpretation of trace gas retrievals. Only 50 years ago it was thought that the behaviour of O3 in the troposphere could be explained by transport from the stratosphere and deposition at the surface. We now know that there are large catalytic sources and sinks for O3 in the troposphere, which determine the chemical weather or air quality of a given air mass. These processes have been briefly described above. The modification of these cycles as a result of human interventions can have large impacts on air quality, agriculture and human health. The retrieval from space of the total column amounts of the key trace gases involved in the production and destruction of O3 and aerosols in the troposphere, has enabled the quantification of natural biogeochemical cycles and of human impacts on the lower atmosphere from local to global scales. Some examples of the results and highlights of retrievals of tropospheric species from GOME measurements are described in the following sections.

13.3.1 Tropospheric NO2 sa a Surrogate for Tropospheric NOx

The oxides of nitrogen, NO and NO2, are key free radicals that play important roles in the mesosphere and stratosphere. NO and NO2 also play special roles in tropospheric chemistry, because, as explained above, they are chain carriers in catalytic cycles producing tropospheric O3, react with OH to produce the acids HONO and HNO3, and in the dark their reactions produce NO2, an oxidant, andN2O5, the acid anhydride of HNO3. The sum of NO and NO2, NOx, and O3 in the troposphere are often in the Leighton photostationary state as a result of the ‘do-nothing’ cycle described in section 3.3. However, the reactions of RO2 and HO2 with NO to form NO2 are key propagation reactions in the catalytic production of O3 in the troposphere and lower stratosphere. NO has ultraviolet emissions, produced by the absorption of NO at short wavelengths in the upper atmosphere, which were observed by GOME‑ However, this absorption by NO occurs below 230 nm and was therefore not detected by GOME. As a result, NO cannot retrieve NO in the troposphere or

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stratosphere. In contrast, NO2 turns out to be an ideal gas for detection by passive atmospheric remote sensing techniques, such as DOAS, optimal estimation and related approaches. Its complex ultraviolet and visible spectra have many features suitable for its detection. One example was shown in Fig. 8. The retrieval of the tropospheric column using only nadir measurements requires the separation of the stratospheric column. This can be achieved by assuming that the NO2 loading is negligible in clean regions of the troposphere and attributing the column to that in the stratosphere. On average, assuming longitudinal homogeneity is reasonable over a period of a month, this enables the tropospheric slant column to be estimated. The latter is then converted into a tropospheric vertical column for cloud-free regions by dividing by an Air Mass Factor (AMF), which is calculated taking into account the path of electromagnetic radiation through the atmosphere, accounting for the vertical profile of NO2, absorption by gases and scattering by molecules and aerosols. If the surface spectral reflectance is large, as in the case of snow, then the AMF is close to the geometric factor of (1 + cosec(θ)), where θ is the angle between the line of observation of the satellite to the ground scene and from the ground scene to the Sun. Where surface absorption is strong then atmospheric scattering becomes significant, for example in the UV.

13.3.2 Global Tropospheric NO2

In 1997 the strong El Niño–Southern Oscillation (ENSO) resulted in drought in the Horn of Africa and in Indonesia. As a result fires, often ignited by humans, became an environmental issue. It is interesting to note that on 26 September 1997, an A300 crashed into woodland 18 miles from Medan, Indonesia, in low visibility, killing all 235 passengers and crew. The crash site was at an altitude of 3000 feet (915 m).2 Figure 22 shows the global tropospheric NO2 vertical column retrieved from GOME. This was sometimes also called the excess as it represents the difference between the NO2 at a given latitude and longitude and that at the same latitude around the international dateline of 180°. More sophisticated approaches have been developed but the underlying concept remains the same, i.e. the subtraction of the stratospheric column from the total column using regions known to have negligible NO2 in the troposphere. The pollution centres in the North America, Europe, Southern Africa and Asia are clearly visible in Fig. 22. In addition, biomass burning in South America, Africa and Indonesia resulted

Figure 22. The excess or tropospheric vertical column amount of NO2 in September 1997 retrieved from GOME measurements, generated by subtracting total column NO2 values along the dateline from those at the same latitude. The high tropospheric NO2 close to Antarctica results from the polar vortex being asymmetric, and an excess NO2 results from reduced amounts of stratospheric NO2 in the polar vortex. (A. Richter and J.P. Burrows, University of Bremen)

2 World Air Crash Disasters, Medan Indonesia: www.medanku.com/world-air-rash- disaster/#ixzz22IDRZfSD

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Figure 23. NO2 plumes correlated with fire counts (top right), lightning estimates (bottom left) and centres of population; average values, September–November 1997. (ESA/NASA/A. Richter and J.P. Burrows, University of Bremen)

Figure 24. Top: Annual cycle of tropospheric NO2 above equatorial Africa, as derived from SCIAMACHY in 2003, together with fire counts from NASA’s MODIS and lightning estimates from NASA’s OTD. (A. Richter and J.P. Burrows, University of Bremen) Bottom: Monthly values of NO2 over Montana, USA, as retrieved from SCIAMACHY in 2004. (NASA/T.H. Bertram and R.C. Cohen, University of California Berkeley, and A. Heckel, A. Richter and J.P. Burrows, University of Bremen)

in large NO2 plumes flowing out over the ocean. The fires, a consequence of ENSO and anthropogenic activities, are also visible. The outflows of NO2 from the Congo basin at low altitudes, and from East Africa at higher altitudes, can also be observed. It turns out that the NO2 in ship tracks can also be retrieved successfully from such pictures.

13.3.3 NO2 Emissions in the Tropics and Subtropics

In the tropics lightning, biomass burning, domestic heating, fossil fuel + combustion, the bacterial oxidation of NH4 and reduction of NO3– in soils are all sources of NO, which then reacts with O3 to produce NO2. It is important to

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understand the strength of the different emissions and the human impact on the production of NOx. The emissions of NO2 from fires were assessed by using fire counts for example from simultaneous measurements made by Envisat’s AATSR at night (Fig. 23). Lightning estimates used GOME data and observations of lightning by NASA’s Optical Transient Detector (OTD), carried as a secondary payload on Pegasus, an Orbital Sciences Corporation rocket launched on 3 April 1995 into Earth orbit at an altitude of approximately 710 km (446 miles), with an inclination of 70°. Part of the NO2 over the rainforest and savannah during the monsoon or rainy seasons can be attributed to the bacteriological reduction of NH4+ and the oxidation of NO3–, which releases NO, N2O and N2. The NO reacts with O3 to produce NO2 (Bertram et al., 2005). Figure 24 (top) shows the annual cycle of NO2 above Africa, as derived from SCIAMACHY in 2003 together with fire counts, in this case from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS), and lightning estimates from the OTD. Additional sources of NO2 attributed to soils and domestic fires are required to explain the observations. Figure 24 (bottom) shows observations of similar behaviour over Montana, USA, following the application of manmade NH4NO3 as fertiliser, using in this case the high spatial resolution of SCIAMACHY. The production of NOx from the bacterial degradation of fertiliser in soils has also been found to be significant over Asia and parts of Africa.

13.3.4e Th Changing Tropospheric NO2 Column

As discussed above, the increase in tropospheric NO2 is the result of air pollution in urban areas, from fossil fuel combustion for heating, industry and transport. At the University of Bremen, Andreas Richter and I began investigations of the spatial and temporal changes in tropospheric loading of NO2. This led to the discovery of large increases in the industrialising areas of Asia, particularly China. GOME was launched some 15 years after China began its economic reforms under Deng Xiaoping in 1979. Between 1995 and 2011, the lifetime of GOME, China’s GDP rose from US$0.7 trillion to US$8 trillion, fuelled by the growing use of fossil energy, and rapid improvements in standards of living. Over the same period, the introduction of legislation to curb pollution in Western Europe, and changes in industry and the transport fleet in eastern Europe following perestroika, resulted in reduced emissions of NOx and lower tropospheric column amounts of NO2 over Europe and parts of North America. The statistically significant annual changes in the tropospheric NO2 column are shown in Fig. 25 using GOME data from 1996 to 2002 (Richter et al., 2005). The large increase over China and the reduction over Europe during this period are clearly visible. When these remarkable findings were published in 2005, they were in disagreement with the estimated changes in bottom-up emission

Figure 25. Statistically significant linear annual changes in NO2 determined on a 1° × 1° spatial grid using the retrievals of NO2 column from GOME, 1996–2002. (A. Richter and J.P. Burrows, University of Bremen)

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inventory data for Asia available at that time. In the meantime agreement is now much better. In Fig. 26 the monthly and annual average composite values for NO2 tropospheric columns are plotted for data from GOME, SCIAMACHY and GOME‑2 for the region 30−40°N 110–124°E in eastern China from 1996 to 2010. The selection of the area is somewhat arbitrary. The ERS Envisat and MetOp A have or had equator crossing times of 10:30, 10:00 and 09:30, respectively, so that small changes as a result of the diurnal cycle of NO2 are also identifiable. The global data from GOME and SCIAMACHY overlap, as do those from SCIAMACHY and GOME‑2 data. The retrievals of tropospheric NO2 are in good agreement. As expected, a seasonal cycle in NO2 is observed, with higher values in winter than in summer. This is qualitatively explained by the seasonal cycles in the photolysis frequency of NO2 and the combustion of fossil fuels. The increase first observed in the GOME data continues. The impact of attempts to reduce pollution for the Olympic Games in Beijing in summer 2008 and the global economic recession in 2008 are clearly observable. Since then, the NO2 column over China has continued to increase. Figure 27 shows the first 15 years of the tropospheric columns of NO2 derived from GOME and SCIAMACHY over six regions where statistically significant annual changes could be determined from GOME data. The plot shows increases over Asia and reductions over Europe and North America. The improvement in air quality in Europe may now have stopped, while over China the NO2 column continues to decline. The area shaded blue in the figure indicates GOME data alone published some years ago.

Figure 26. Monthly changes in tropospheric NO2 over central eastern China (30°−40°N, 110–124°E) derived from GOME, SCIAMACHY and GOME‑2 data for <10% cloud cover. (A. Richter and J.P. Burrows, University of Bremen)

Figure 27. Merged annual tropospheric NO2 columns from GOME and SCIAMACHY relative to 1996, observed over six regions: central east coast of the USA, Western Europe, Poland, east-central Europe and Hong Kong. The area shaded blue indicates GOME data already published.

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Figure 28. Left: NO2 emissions from shipping retrieved from GOME‑2 data, 2007–9. (A. Richter and J.P. Burrows, University of Bremen) Right: Increases in NO2 emissions from ships crossing the Indian Ocean between Sri Lanka and Indonesia, retrieved from GOME, SCIAMACHY and GOME‑2 data, 2008. TEC, Total Electron Content. (A. Richter. V. Eyring, K. Franke and J.P. Burrows, University of Bremen)

13.3.5 NO2 in Ship Tracks and Inferences for NOx Emissions

One of the discoveries of the GOME and SCIAMACHY missions was the observation of NO2 emissions from ships (e.g. Richter et al., 2005). The research groups at the universities of Heidelberg and Bremen found clear evidence of increased emissions from ships using the route between Sri Lanka and Indonesia (Fig. 28; Richter et al., 2004; Franke et al., 2008/2009).

13.3.6 OVOCs from GOME as Surrogates for VOCs

Unfortunately, in the UVA–visible spectral regions, there is no strong absorption of CH4 or VOCs such as isoprene, terpenes or aromatics. However, the aldehydes and ketones do have absorption in these spectral regions as a result of their delocalised bonds. Thus one of the original target gases for GOME and SCIAMACHY were formaldehyde and HCHO, and glyoxal, CHO. CHO, was added later. HCHO is produced during the oxidation of CH4 and VOCs emitted from the biosphere or by anthropogenic activities. In addition, as a result of biomass burning, HCHO is injected in both directions and as a result of the oxidation within the plumes from biomass burning (Ladstättter-Weißenmayer & Burows, 1998; Thomas et al., 1998). Figure 29 shows the global average composite HCHO column amounts retrieved from GOME from 1996 to 2002. Initially, this type of picture was confusing but it was realised that both biomass burning and biogenic sources release large amounts of VOCs that are oxidised to HCHO. Anthropogenic pollution also adds VOCs. Later, using SCIAMACHY, CHO.CHO was also identified (Wittrock et al., 2006). The ratio of the columns of CHO.CHO to HCHO provides some sources speciation information as aromatic and biogenics have different oxidation product ratios. When coupled with NO2 the emissions to the air mass under investigation can be synergistically characterised. A source of CHO.CHO discovered over the ocean is considered to be a result of biogeochemical coupling between the ocean and the lower troposphere. Tropospheric O3 can also be retrieved from GOME measurements using a variety of algorithms. As approximately 90% of the atmospheric O3 is typically above the tropopause, it is necessary to obtain very accurate estimates of both the total column density of O3 and its column above the tropopause. In tropics and subtropics the tropopause is relatively well behaved and tropospheric O3 can be retrieved using a residual technique similar to that used

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Figure 29. Average composite HCHO column amounts retrieved from GOME data, 1996–2002. (F. Wittrock, A. Richter and J.P. Burrows, University of Bremen)

for the TOMS data (Fishman et al., 1990, 1991, 1992, 1996). The TTO algorithm is particularly elegant (Hudson et al., 1996; Hudson & Thompson, 1998; Kim et al., 1996). The TTO approach and an assessment of the profile regimes led to the discovery that the tropical tropopause is expanding (Hudson, 2012, and references therein). Another approach to derive tropospheric O3 used the cloud top heights (Ziemke et al., 1998). Figure 30 shows the tropospheric columns of HCHO, NO2 and the tropospheric excess column of O3, the latter being the difference between the total column density of O3 at a given location and that at the same longitude around the international dateline, which is assumed to have low and reasonably constant O3. The latter assumption is simple but useful for explanatory purposes. The O3 is produced by the chain reactions described above, resulting from both natural and anthropogenic emissions of NOx and VOCs. In the tropics, lightning, biomass burning and biogenic emissions release the VOCs and NOx required to produce this O3 photochemically. However, domestic heating and industry cannot be ignored. As can be seen in Fig. 30, this results in a plume of air producing O3 flowing from the Congo basin at low altitudes into the South Atlantic and, conversely, a plume of air at higher tropospheric altitudes, sending O3-rich air and its precursors towards Australia in the southern hemisphere. There are also retrieval algorithms that derive tropospheric O3 outside the tropics and subtropics, although this is more challenging as frontal systems vary the height of the tropopause. An adequate discussion of these retrieval approaches and their results is beyond of the scope of this chapter.

13.3.7 SO2 from Volcanoes and Pollution

As TOMS measurements had been used to derive SO2 from volcanic eruptions (Krueger, 1983; Krueger et al., 1996, 1998), GOME with its much superior spectral coverage was expected to measure the release of SO2 from volcanoes. Michael Eisinger, who later joined ESTEC to work on GOME‑2 from the DOAS group at IUP/UB, supported by Andreas Richter and myself, was the first to retrieve SO2 columns from GOME data. The spectral measurements made by the CATGAS team turned out to be invaluable as the GOME spectral channel 2 has some unique characteristics, including, as mentioned above, a spectral resolution that varied between 314 nm and 325 nm, and the FWHM changing from 0.6 nm at 314 nm to 0.2 nm at 325 nm (Eisinger & Burrows, 1998a,b). In addition, the first space-based observations of SO2 from tropospheric pollution were reported. The sources of this pollution include the burning of lignite and other brown coals, metal smelters and the combustion of oil containing sulphur. The sensitivity was not sufficient, however, to determine the

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Figure 30. Average composite tropospheric columns of HCHO, excess O3 and NO2, September 1997. The excess O3 is different from the O3 at a given longitude and latitude and that at the international dateline at the same latitude. (A Ladstätter- Weißenmayer and J.P. Burrows, University of Bremen)

background amounts of SO2 resulting from the oxidation of dimethyl sulphide, released from the oceanic biosphere, which is below the detection limit. The composite average SO2 column amounts retrieved from GOME measurements over the period 1996–2002 are plotted in Fig. 31. Several active volcanoes can be seen, including Nyamuragira (1.41°S, 29.2°E) in DR Congo, a large shield volcano similar to Mauna Loa on Hawai’i, which is one of Africa’s most active volcanoes. Popocatepetl (19.023°N, 98.622°W) and Colima (19.514°N, 103.62°W) in Mexico, and a number of erupting sea mounts can also be observed. The SO2 from fossil fuel combustion is visible over eastern Europe, the Middle East, the east coast of the USA and China. The monthly and annual changes in SO2 above eastern China is shown for merged GOME and SCIAMACHY data in Fig. 32. There is a clear seasonal cycle, with higher

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Figure 31. Average composite tropospheric SO2 vertical column amounts retrieved from GOME measurements, 1996–2002. (A. Richter F. Wittrock and J.P. Burrows, University of Bremen)

Figure 32. Monthly and annual changes in SO2 vertical column amounts above eastern China, retrieved from GOME and SCIAMACHY measurements. (A. Richter and J.P. Burrows, University of Bremen)

values in winter when more energy is produced for heating. Although the desulphurisation of coal-fired power plants began in China in 1998, the programme was rapidly expanded in 2007 to improve air quality for the Olympic Games in Beijing in 2008. The decrease in 2008, which may also result in part from the global recession, is readily visible. It appears that after 2010 the SO2 vertical column may have started to increase again.

13.3.8 Tropospheric Halogen Oxides: BrO and IO

One the most surprising discoveries in the early days of the analysis of GOME data was associated with the retrieval of BrO vertical columns. Large clouds of BrO at high latitudes in the Antarctic and the Arctic springs were discovered by the groups at the universities of Heidelberg and Bremen, respectively (e.g. Richter et al., 1998). Figure 33 shows examples for both hemispheres. This natural phenomenon represents a coupling and feedback between the cryosphere and the oxidising capacity of the troposphere, and has been the subject of much recent research. This remains an important research topic as the sea ice is changing as a result of global climate change and the impact on the processes producing BrO, explained in more detail in section 3.3, and the oxidising capacity of the troposphere is not yet clear. These clouds of BrO are associated with tropospheric O3 and mercury depletion events (Barrie et al., 1988; Simpson et al., 2007). Modulation of the tropopause can also cause some apparent increases in BrO similar to

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Figure 33. Total column BrO showing clouds of BrO in the troposphere at high latitudes above the sea ice in spring. (A. Richter and J.P. Burrows, University of Bremen)

the changes in column O3 around frontal systems. The tropospheric release is attributed to gas phase and cold brine chemistry and what is known as a bromine explosion, which involves the acid catalysed reaction between HBr and HOBr. This occurs globally on sea salt aerosols, but is triggered and accelerated in low-temperature conditions on cold brine. Conditions termed 'potential frost flower' appear to be involved in the release mechanism (Kaleschke et al., 2004 , and references therein; Sander et al., 2006). Other sources of halogen compounds in the troposphere include the production of halogen-containing compounds by the oceanic biosphere, followed by exchanges between the ocean and the lower atmosphere, and volcanic eruptions. In seawater the ratio of [Cl–] : [Br–]I : [ –] is roughly 1 : ~660 : ~15 000. However, the biosphere concentrates the bromine and iodine, releasing the organohalogen compounds described above into the oceanic troposphere. The organobromine and iodine compounds are photolabile. In the case of iodine, molecular iodine is released by various types of seaweed and algae. As a result of these processes a background Br and BrO in the troposphere contributes to its oxidative capacity. In the tropics even very short-lived species, such as halogen-containing gases in the biosphere, are transported by rapid convection to the stratosphere. Changes in the Brewer–Dobson circulation, which are expected in a warming world, could therefore also change stratospheric composition and its halogen loading. Astonishingly, the very reactive IO has been retrieved from SCIAMACHY and GOME‑2 data (Schönhardt et al., 2007/2008). Figure 34 shows an example of a global estimate of the average composite for IO retrieved from SCIAMACHY. The oceanic biosphere in the southern hemisphere appears to release more iodine than in the northern hemisphere. Iodine is released from areas of algae and kelp, and there are also significant releases from under or within the sea ice in Antarctica There are also inorganic mechanisms involving O3 photo- oxidation in the surface water layers.. Direct observations of ClO in the boundary layer are not possible in nadir viewing because its structure absorptions are below 320 nm. As noted above, OClO has strong structure absorption spectra, as do OBrO and OIO; the spectrum of OIO was discovered as part of studies at the IUP/UB for GOME and SCIAMACHY. However, OClO, OBrO and OIO have such short photolytic lifetimes that their daytime concentrations are low. OClO has been measured in the stratosphere close to the terminator during the day and at night by remote sensing. Similarly, OIO has been observed in the troposphere over kelp beds at night. At present no OIO or OBrO has been identified during the day from

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Figure 34. Average composite IO vertical column amounts, retrieved from SCIAMACHY measurements, 2003–2010. (A. Schönhardt, F. Wittrock, A. Richter and J.P. Burrows, University of Bremen)

GOME, SCIAMACHY or GOME‑2. The role of halogens in the troposphere and the feedback with climate change is currently a very active area of research.

13.3.9e Th Hydrological Cycle: H2Od an Cloud Data Products

The spectral resolution of GOME enable combination and overtone bands of water vapour that absorb in the GOME spectral region to be identified. GOME has strong and weak water vapour absorptions. The strong structure around 720 nm has been used together with the O2 B-band close to 670 nm to retrieve the total water vapour. Measurements of the H2O total column amount have been made over both land and oceans. Some results for annual variations in the total column water vapour are shown in Fig. 35. The monthly and seasonal variations, and the impact of ENSO events, are all clearly visible and have been studied. The annual changes in H2O were investigated on a 0.5° × 0.5° grid similar to those of NO2 using the GOME and SCIAMACHY data. The statistically significant changes derived for the first 10 years of data on H2O are shown

Figure 35. Global distribution of the total column of H2O, retrieved from GOME and SCIAMACHY, 1996–2006. (S. Noel and J.P. Burrows, University of Bremen)

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Figure 36. Statistically significant linear annual changes in total column H2O derived from GOME and SCIAMACHY on a 0.5° × 0.5° grid, 1996–2006. (S. Mieruch, S. Noel and J.P. Burrows, University of Bremen)

in Fig. 36 and have been validated by comparison with H2O sonde data. The reason for these changes is not yet clear and may be in part the result of natural variations in the hydrological cycle or global climate change. Changes in the height of the tropopause and the tropical extent of the tropical tropopause will both have impacts. Local changes attributable to water usage in urban areas have been identified. Measurements of reflectance using the higher-spatial-resolution Polarisation Measurement Device and the O2 absorption are used to determine cloud cover and cloud top heights. The O4 and the ring feature are also alternatives for determining cloud top heights. Although the cloud data products were initially intended to be used in the interpretation of the columns of trace gases retrieved from GOME, they have become important data products in their own right. Similarly, the absorbing aerosol index data products, first produced for TOMS, generated from GOME, SCIAMACHY and GOME‑2 measurements have also become useful data products. The complexity of the hydrological cycle is such that accurate separation of anthropogenic influences from natural occurring oscillations or changes requires long-duration, comparable and consistent datasets. The early morning measurements and resultant data products initiated with the launch of GOME are now beginning to be of significant value in this respect.

14. Challenges for the Evolution of European and ESA Earth Observation – Atmospheric Composition Observations from space – global observations from low-Earth orbit (LEO) satellites and regional observations from geostationary Earth orbit (GEO) satellites – provide objective unique measurements of atmospheric composition. For many applications, the true value of such data is proportional to the number of months or years over which such measurements are made, or the size of the consolidated dataset compiled from a series of instruments on different satellites. These long-term datasets need to improve their spatial resolution and temporal sampling in time as this becomes technically feasible. With such datasets it is possible to distinguish between anthropogenic and natural causes of change, provide early warning of change and increase the accuracy of predictions about the amount of air pollution, the stability of the stratospheric ozone layer and climate change. Europe initiated the industrial revolution, has produced about 30% of the ozone-depleting substances responsible for the loss of stratospheric O3 and the ozone hole, and is a major source of greenhouse gases. Europe invented

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the Haber−Bosch process, which brought about a revolution in agriculture resulting in changes in land management and land use across the continent and the world. European nations and the were also the first to recognise the consequences of air pollution and to introduce legislation to mitigate its impacts at national, regional and international scales. Europe has a complex system for developing and running long-term atmospheric composition measurements from space, which is still in its infancy. It is not yet clear how the system will evolve to maintain the key datasets required for science and policy making. Eumetsat has a mandate from its member states to make measurements needed for numerical weather prediction and some aspects of climate services. ESA is an intergovernmental organisation with a mission to provide for and to promote space science, research and technology applications for peaceful purposes. However, its member states have not given ESA a mandate to undertake science, equivalent to that given to NASA in its charter. One focus of activities at ESA is on the development of industrial capability. The EU is also involved in Earth observation through its funding of Copernicus, previously known as the Global Monitoring for Environment and Security (GMES) programme, and Galileo. The EU, ESA and Eumetsat are involved in Copernicus. Finally, many EU member states have their own important and significant space agencies. It is unclear to what extent any of these bodies has a mandate explicitly to undertake and support science and the generation of knowledge of atmospheric composition. To assess the needs of the science community, over the last 25 years ESA has organised a series of user consultation meetings of relevance to atmospheric composition or chemistry from space, and has published their recommendations. The 1991 user consultation meeting, for example (ESA, 1991), pointed to:

—— the urgency of having adequate atmospheric composition measurements; —— the need to ensure the continuity and validity of measurements; and —— the need for satellite measurements as part of an integrated atmospheric monitoring system.

Two decades later, the most recent ESA user consultation meeting was held in Bruges, Belgium, in June 2012, for the atmospheric community following the loss of ESA’s flagship Envisat. Its recommendations were similar to those made in 1991. This is because without Envisat there are no European nadir measurements of key greenhouse gases, no limb/occultation profiling of atmospheric constituents and parameters, and more. One of ESA’s programmatic objectives during the 1990s was to establish the Earth Explorer and EarthWatch missions. The Earth Explorers were envisaged as promoting innovation and the evolution of European Earth observation, while the EarthWatch missions would yield the long-term measurements of surface parameters and atmospheric composition required for applications such as numerical weather prediction, air quality monitoring and climate science. While the Earth Explorer missions for Earth observation have flourished to some extent, with ESA releasing eight calls for missions since 1998, the need for long-term measurements of key atmospheric pollutants and greenhouse gases as expressed in the EarthWatch approach has floundered. The continuity of atmospheric composition has not been achieved adequately. The loss of Envisat has resulted in the complete loss of European vertical profile measurements and nadir sounding of the total columns of the greenhouse gases CO2 and methane. In the 1990s, however, ESA and Eumetsat were able to establish and define the need for an ozone monitoring instrument for the MetOp series of platforms. The ESA-developed GOME‑2 was selected as part of the core payload for MetOp. This improved somewhat on the GOME design but ESA was unable to fund the addition of 2D detector arrays, which would have significantly improved the spatial resolution.

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The EU−ESA–Eumetsat is providing valuable opportunities for the development of instrumentation to deliver knowledge of surface parameters. Sentinel‑1, ‑2 and ‑3, currently planned for launch in 2013−14, will provide mainly remote sensing of the surface parameters and, in the case of Sentinel‑3, some aerosol and cloud parameters. Sentinel‑4 is an instrument that will be launched on the Meteosat Third Generation Sounder, MTG-S, in 2019. This will be the first geostationary instrument for measuring air quality, taking its heritage from the SCIAMACHY/GOME, the Geostationary Scanning Imaging Absorption Spectrometer (GeoSCIA)/GeoSCIA++/Geostationary Tropospheric Pollution Explorer (GeoTROPE)/GeoSCIA-lite proposals submitted to ESA in response to its calls for Earth Explorer missions in 1998 and 2002, a German national call in 2005, and subsequent studies by ESA and Eumetsat. A follow-on from GOME‑2, called Sentinel‑5, will extend the nadir observations somewhat, and is currently being developed by ESA for the Eumetsat Polar System (EPS) programme, which will deliver the MetOp Second Generation polar-orbiting meteorological satellites from 2020 onwards. Both Sentinel‑4 and Sentinel‑5 are funded by the EU’s Copernicus programme, which aims to develop the European capacity for Earth observation. ESA is also developing a Sentinel‑5 Precursor, which will include a Tropospheric Monitoring Instrument (TROPOMI), a contribution in kind by the Netherlands, and a shortwave infrared instrument that will measure CO, partly duplicating the GOME‑2 measurements at higher spatial resolution. As noted above, the European limb and occultation measurements and the nadir measurements of CO2 and CH4, which have been such important and successful aspects of Envisat, are now lost. To fill the data gap, the rapid development of missions for limb occultation measurements and for nadir measurements of greenhouse gases is required urgently. These missions and their data are needed for fundamental Earth system science and for the development of international environmental policy. In this context, the possibility of using of the International Space Station – a platform that already exists and to which Europe has access – should be investigated. It is interesting to note that it took 6.5 years between the proposal of SCIAMACHY and the launch of GOME. It will have taken 21 years to go from the GeoSCIA proposal to the launch of Sentinel‑4 on MTG-S in 2019. So the question is whether European remote sensing is moving forwards or backwards. The time for process rather than the time to build an instrument or mission is apparently now the dominant rate-determining step in developing a mission. Europe needs to rejuvenate its intentions and establish space missions, aimed at measuring atmospheric composition and addressing the key scientific issues in the next phase of the Anthropocene. An urgent issue is how to return and improve on the range of key atmospheric measurements that were being made until 7 April 2012 by European instrumentation on Envisat. Hopefully, European national bodies will address the challenge and develop a system that provides both the data and the scientific exploitation that are required to improve our knowledge and build up the evidence base needed to assess environmental and climate change, leading to the accurate attribution of change and measures to mitigate appropriately the anthropogenic impact.

15. Summary and Conclusions

European and Chinese societies were the first to recognise that local air pollution and later smog were related to human development and urbanisation. After intense episodes of air pollution resulted in large negative epidemiological impacts in cities both in winter and summer during the early postwar period, public concerns led the governments of the UK, other European countries and the United States to introduce legislation aimed at constraining and reducing air

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pollution. The UNECE Convention on Long-Range Transboundary Air Pollution was designed to manage and control the transport and transformation of pollutants. In spite of this early warning of the potential global importance of the emissions to the atmosphere by industry and society, European industry has produced about 30% of the stratospheric ozone-depleting substances released to the atmosphere. Europe has also modified agricultural systems through its production and use of fertilisers and pesticides. It has undertaken land use change on an unprecedented scale and is also one of the most important sources of the greenhouse gases CO2, CH4 and N2O. In response to the realisation of the importance of human impacts on the environment and the world’s climate, the European scientific community and the general public have recognised the need to understand, assess and manage the human activities that are influencing the Earth system. As a first step in this process, accurate, long-term measurements and global knowledge of meteorological parameters, surface emissions and atmospheric composition are required. The SCIAMACHY team led by myself, working together with ESA, developed the concept of GOME by descoping the successful SCIA-mini proposal in the late 1980s. GOME made the first global contiguous measurements of the backscattered and reflected solar radiation between 230 nm and 793 nm at the top of the atmosphere from Sun-synchronous low-Earth orbit, in order to retrieve the global amounts and distributions of key stratospheric and tropospheric trace constituents in the mid-1990s. Florence, the birthplace of the Renaissance, is where the GOME instrument was designed in detail, utilising the instrument concepts from the science team and the industrial Phase A studies, and then constructed by the Officine Galileo and their sub-contractors. The GOME data products, developed by the GOME scientists, have achieved one of the most important objectives of the SCIAMACHY project, i.e. the initial demonstration and proof of concept that the retrieval of accurate global measurements of the amounts and distributions of key atmospheric constituents and pollutants from passive remotely sensed measurements of backscattered and reflected solar radiation from space-based instrumentation in low-Earth orbit is feasible. This has led to a unique record of solar ultraviolet and visible output, and upper atmospheric and tropospheric trace gas composition. In orbit on ERS‑2, GOME performed flawlessly. Although the ERS-2 tape recorders both failed in 2003 partial global coverage was still possible until ESA began the decommissioning of ERS-2 in July 2011. GOME’s voyage represents a milestone in the history of global passive remote sensing of atmospheric composition. The close relationship between the scientists, ESA and industry, and the short time taken to build the instrument, meant that GOME was a relatively high-risk but in practice a low-cost instrument. Approximately €25 million was required to construct the three GOME flight- ready instruments. The scientific success of GOME and the SCIAMACHY project is evident from a search of the ISI web of knowledge, which finds over 1200 scientific papers, with approximately 18 000 citations, yielding an h-index of 57. The achievements of ERS and GOME may yet herald the arrival of a new age of global enlightenment, where the sustainable management of the global environment utilises objective evidence, provided in part by remote sensing from space-based platforms. GOME was originally selected for its ability to deliver total columns and vertical profiles of 3 O , but made a pioneering contribution to improving our knowledge of atmospheric chemistry and dynamics and quantifying air pollution in both the stratosphere and the troposphere. Its data products have provided early warning of environmental change produced by man, as well as an evidence base for assessments of the effectiveness of the international agreements such as UNECE’s Convention on Long-Range Transboundary Air Pollution and the UN’s Vienna Convention for the Protection of the Ozone

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Layer and its Montreal Protocol on Substances that Deplete the Ozone Layer. The pictures of the amounts and distributions of the trace gases, aerosol and cloud parameters resulting from GOME, SCIAMACHY and now GOME‑2 are compelling. They document the changing atmospheric composition, which is intrinsically fascinating, and demonstrate uniquely the increasing impact of man during this early phase of the Anthropocene. The success of GOME and SCIAMACHY and the value of their data products have been internationally recognised. The importance of this breakthrough in providing global, objective measurements of pollution and natural biogeochemical cycling cannot be underestimated for both atmospheric science and the development of global environmental policy. A review of the development of modern tropospheric remote sensing has been provided elsewhere (Burrows et al., 2011), and a book on SCIAMACHY is now in its second edition (Gottwald & Bovensmann, 2011). Turning to the European plans for the future, the picture is disappointing and high risk. This is primarily because progress is too slow compared with the growing need for accurate global information on atmospheric composition. The Copernicus Sentinel‑4 will provide the first measurements of diurnal variations in tropospheric trace gases over Europe from a geostationary orbit but not simultaneously over the tropics or sub-Saharan Africa. It is interesting to note that it took about six years from the proposal of SCIMACHY and SCIA-mini to the launch of GOME on ERS‑2, whereas it will have taken ~21 years from the proposal of GeoSCIA to the launch of Sentinel‑4. From 2020 onwards Sentinel‑5 will continue the early-morning measurement series currently being made by GOME‑2, and will improve the spatial resolution and add targets CO and CH4, like SCIAMACHY, but there are no plans to focus on CO2, according to ESA. In addition, the Sentinel‑5 Precursor will make measurements similar to those of OMI with the addition of a NIR/SWIR channel, but in the early afternoon, from 2017 onwards. Sadly, as a result of the sudden and unexpected loss of contact with Envisat on 8 April 2011, there will be no new data and therefore knowledge of many important processes occurring in the atmosphere and at Earth’s surface. Envisat had been in operation for twice its guaranteed lifetime, as defined by the industrial consortium that built it. However, its instruments were working excellently prior to failure, and in the case of SCIAMACHY no end-of-life issues for components were foreseen prior to 2020. The lack of an overlap with or plans for new European instruments to undertake nadir soundings of CO2, the most important greenhouse gas, or limb and occultation observations of the profiles of key atmospheric constituents from the upper troposphere to the thermosphere shows a systemic problem in the development of the European component of the required global measurement system. This is not a new issue. The gaps in datasets and slow progress in improving measurement spatial resolution and sampling is partly related to the responsibilities and mandates given by governments to the various research, space, meteorological and environmental agencies. There appears to be a lack of joined-up thinking, which leads to insufficient funding to maintain and deliver an adequate and integrated global observing system. The cost of providing the research and space segment for the required global atmospheric measurements is not trivial, but is, in my opinion, lower than that of not obtaining the knowledge from this system. This is because it is not possible to manage what is not being measured. I estimate that a 1 cent tax on a barrel of oil, coal and gas equivalent in Europe (i.e. 1/10000 of the current cost) alone would pay for much more than the global measurement systems required and defined by expert groups. The European EarthWatch concept, discussed in the 1990s, has not been realised for atmospheric composition. After three pioneering decades, a golden age, it appears that we are returning to a darker age when much less global atmospheric information on atmospheric composition will be available for

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scientists and policy makers. It appears that a global observing system that builds on the pioneering achievements of the SCIAMACHY project, other atmospheric composition measurements and in particular GOME on ERS‑2, will remain a distant objective. The creation of an adequate global observing system, associated ground segment infrastructure, and the human capacity to run the system are needed urgently to provide the information to assess human impacts on the evolution of the Earth system during the next decades of the Anthropocene. The latter is required to improve our understanding and the accuracy of prediction of the behaviour of the Earth system and its responses in a warming, increasingly urbanised world. This information would in turn provide the objective evidence needed to develop further and monitor the effectiveness of international environmental policy. The latter has the aim of achieving sustainable economic development and optimised global environmental management, at a time when the Earth’s population is heading rapidly towards and beyond 10 times what it was at the outset of the industrial revolution.

A cknowledgements

This chapter has provided a subjective, partially historical review of key aspects of the initiation and the development of GOME and SCIAMACHY, as well as some of the scientific results obtained from GOME. It is not intended to be a comprehensive objective analysis, but my personal view of the evolution of GOME and European remote sensing of atmospheric composition. Many individuals have contributed to the success of GOME and the SCIAMACHY project that I have not had space or time to list in detail. I therefore wish to acknowledge explicitly all the scientists, engineers and administrators who contributed to the success of these projects and to the ERS and Envisat programmes. These have been remarkable European civil achievements. There are many fathers and mothers of success, and without the funding agencies, who invested in this project it would not have happened. ESA, the German Ministry of Science and Education (BMBF), the German Aerospace Center (DLR), and the Dutch, Belgian and Italian space agencies are to be thanked, as well as NASA (in particular E. Hilsenrath, J. Gleason and S. Janz) for their collaborative support. My friends and scientific mentors Paul Crutzen, Dieter Perner (1934–2012), Wolfgang Schneider, Geert Moortgat at the Max Planck Institute for Chemistry in Mainz and Richard Wayne of the University of Oxford were a great strength and support, particularly during the development of the scientific objectives of SCIAMACHY and GOME. I would also like to thank Ulrich Platt and his team at the University of Heidelberg for their many and continuing contributions. Phil Goldsmith, Chris Readings, Guy Duschossois, Peter Dubock, Peter Edwards, Achim Hahne, Alain Lefebvre, Jörg Calleis and Reinhold Zöbl of ESA also made important contributions, without which GOME would not have been possible. The teams at the then Officine Galileo have had the pleasure of seeing their instrument becoming a world-beater. EADS Astrium was responsible for key aspects of the ERS and Envisat platforms and also the prime contractor for SCIAMACHY. I thank them all and many others not mentioned by name for their outstanding work. I am indebted to the State and University of Bremen for in part funding my research. I thank the faculty of Physics for hosting our Institutes of Environmental Physics and Remote Sensing. In particular I wish to thank the members of the research team at the Institute of Environmental Physics of the University of Bremen, IUP-UB, who have supported my efforts and made many important and original contributions of their own to various aspects of the GOME and SCIAMACHY projects. In particular, I thank my close colleagues of many years at IUP-UB – Heinrich Bovensmann, Klaus Bramstedt, Michael Buchwitz, Kai-Uwe Eichmann, Konstantin Gerilowski, Klaus Künzi, Alexander

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Kokhanovsky, Annette Ladstätter-Weißenmayer, Stefan Noel, Justus Notholt, Andreas Richter, Vladimir Rozanov, Wolfgang von Hoyningen Huene, Christian von Savigny, Heiko Schellhorn, Heiko Schröter, Anja Schönhardt, Marco Vountas, Folkard Wittrock, and Mark Weber − as well as my administrative staff and the many postdoctoral and graduate students who passed through my department at IUP-UB. Finally, I would like to thank my wife and family for their patience and support, which enabled me to invest fruitfully so much time and effort in the development and evolution of GOME, SCIAMACHY, GOME‑2, GeoSCIA/ Sentinel‑4, Sentinel‑5, CarbonSat and more recently SCIA-ISS , not to mention my studies of kinetics and spectroscopy.

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141

→→TheES R Scatterometer: Achievements and the Future

Scatterometer

The → eRS Scatterometer:→ achievements and the Future

A. Stoffelen1, W. Wagner2

1 KNMI Royal Netherlands Meteorological Institute, 3732 GK De Bilt, the Netherlands 2 Vienna University of Technology, 1040 Vienna, Austria

1. Introduction

The first Active Microwave Instrument was launched on the European Remote Sensing satellite, ERS‑1, in July 1991. When operated in Wind Scatterometer mode, the instrument acquired measurements of sea surface wind speed and direction. ERS Scatterometer (ESCAT) data are now widely used for hurricane prediction, Numerical Weather Prediction, marine ‘nowcasting’, oceanography, hydrology and studies of the cryosphere, with societal and economic benefits in areas such as marine safety, offshore activities, ship routing, wind energy and climate change monitoring. The ESCAT research mission was well prepared with aircraft campaigns, including the setup of the RENE campaign for Calibration and Validation (Cal/Val), the development of a Geophysical Model Function (GMF) and initial preparations for user applications, such as the near-realtime availability of the scatterometer winds. Nevertheless, initial comparisons of the ESCAT winds with other wind information showed large inconsistencies. This led to the development of novel analytical techniques using the measurement space of ESCAT, which confirmed the expected internal consistency of the ESCAT measurements and the low noise, but also provided an improved characterisation of the sensitivity to wind speed and direction, resulting in improved GMFs. Moreover, the visualisation of the ESCAT measurement space provided insights into the nonlinear wind retrieval problem, which has subsequently been improved, aided the discrimination of sea ice and water, and provided important insights into wind quality control. The ESCAT wind Cal/Val also led to the development of the triple collocation methodology, which is now widely used in satellite algorithm development. The lessons learnt from ESCAT were readily applied to the NASA scatterometer missions at the Ku-band and provided similar benefits, leading to improved rain screening and wind datasets. ESCAT has also been successfully applied in soil moisture, sea ice and snow characterisation, leading to operational applications. ESCAT’s heritage has also been important in the development of the Advanced Scatterometer (ASCAT) on MetOp for the Eumetsat Polar System (EPS). The EPS Second Generation ASCAT (ASCAT-SG) is now being designed, where the ESCAT C‑band static fan beam concept is being maintained due to its great success. This may well lead to over 40 years of C-band fan beam scatterometer data, which will be a tremendous resource for climate applications for studying wind climatologies, air–sea interactions, atmospheric processes, etc. The demand for the unique ESCAT data and services will therefore remain high in the years to come.

2. The ERS Scatterometer Instrument

Space -borne provide unique global ocean surface vector wind products at high spatial resolution. Since they operate at microwave (radar) frequencies, these instruments are not hindered by cloud cover and hence are able to reveal phenomena such as polar front disturbances and winds. Figure 1 shows mesoscale ESCAT winds (red) detected by the ESCAT

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Figure 1. Scatterometer winds from ERS‑2 at around 12:00 GMT on 24 November 1999 at 70°N and 0°W (www.knmi.nl/ scatterometer). The grey shading is a Meteosat IR image coherent with the scatterometer winds. Blue mask: areas where the sea surface temperature is below zero and sea ice is probable. Grey mask: land at 80°N and 20°W (top right). The red contours indicate surface pressure from the HIRLAM model at KNMI (3-h forecast). Blue and purple are the spatially smooth wind vectors from HIRLAM (the amount of purple increases with wind speed). The red wind vectors depict the spatially detailed ERS‑2 winds. The red dots indicate where winds were rejected because of a confused sea state (bottom left) or the presence of sea ice (top left). Scatterometers reveal more coherent spatial detail than NWP winds. (Eumetsat OSI SAF, 1999)

instrument on ERS‑2 and Numerical Weather Prediction (NWP) model winds (blue wind vectors). The ESCAT mesoscale wind details in the area where the cold northerly flow interacts with the warmer southerly flow line up well with the geostationary satellite cloud bands at the time of the ERS‑2 overpass. The NWP winds describe only the larger scales well. During the Second World War, when radar was used to detect and track hostile vessels, it was noted that this detection was hampered by increasing wind speed. Naturally, the idea of measuring wind near the sea surface by using microwaves, i.e. scatterometry (e.g. Moore & Pierson, 1967), was soon developed. Wind scatterometers measure the radar backscatter from wind- generated centimetre-sized gravity capillary waves and provide high-resolution vector wind fields over the oceans. All-weather scatterometer observations have proven accurate and important for the forecasting of dynamic and severe weather. Oceanographic applications have been developed since scatterometers provide unique forcing information on the ocean eddy scale. Scatterometers also provide useful information on soil moisture, snow cover and sea ice (see Fig. 2).

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Figure 2. An ‘impressionist’ view based on the radar map of Earth’s surface as observed by the ERS scatterometer. Sea ice is shown in violet, variable winds in grey and high winds in white, but with wind sensitivity depending somewhat on swath position. The yellow and blue in the northern and southern hemispheres indicate trade winds and correspond to higher signals in the fore and aft antennae, respectively, and thus indicate the directional stability of the wind in the intertropical regions. (Data processing by IFARS, Germany, for land surfaces, and by the ERS Product Control Service at ESRIN for oceans. (ESA) The first scatterometer in space was NASA’s Seasat-A Scatterometer System (SASS), which flew for three months in 1978. SASS had four antennae, two on each side of the satellite (see e.g. Stoffelen, 1998). Each set of two antennae covered a swath – one to the right of the subsatellite (ground) track and one to the left. In the horizontal plane, the fore and aft beams were pointing at 45° and 135° azimuth, respectively, with respect to the . A location in the swath was first hit by the fore beam, and a few minutes later by the aft beam. Thus, each Wind Vector Cell (WVC) in the swath revealed two backscatter measurements obtained with a 90° difference in azimuth. Figure 3 illustrates the analysis of two such measurements. For each measurement it shows the wind speed solution as a function of all possible wind directions. Given the dependency of the backscatter signal on the basic harmonic wind direction, four solutions exist in this general case. This ambiguity poses a strong limitation on the usefulness of the SASS wind data, and extended manual efforts were needed to remove the ambiguity in order to obtain an acceptable wind product (Peteherych et al., 1984). The usefulness of this product has been demonstrated (see, e.g. Stoffelen & Cats, 1991). The scatterometers on ERS‑1 and ERS‑2 (here denoted ESCAT) were identical and each had three antennae that illuminated the ocean surface from three different azimuth directions (e.g. Stoffelen, 1998). A point on the ocean surface would be hit first by the fore beam, then by the mid-beam and soon after by the aft beam. Since this provided three measurements to determine two

Figure 3. Wind speed as a function of wind direction for fore and aft beam measurements of backscatter from SASS data. The arrows indicate the four possible solutions for this typical case. (Utrecht University, Stoffelen)

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parameters, i.e. wind speed and direction, the ESCAT wind retrieval problem is overdetermined in principle. Moreover, the dominant harmonic azimuth dependence of the radar backscatter is a double harmonic (Long, 1986), which is ideally sampled by three azimuth angles 45° apart, enabling rather constant wind direction sensitivity of the ESCAT instrument. It was therefore anticipated (Attema, 1991) that this measurement geometry would generally result in (only) two opposite wind vector solutions and some residual information. Based on the available technology, the C-band wavelength was chosen for the ERS microwave mission. With hindsight, this was a very fortunate choice as it provided sufficient wind sensitivity and little sensitivity to rain effects. Several campaigns were conducted to prepare the ESCAT mission (e.g. PROGRESS and TOSCANE). Long (1986) developed empirical GMFs to match backscatter measurements with the measurement geometry and local wind conditions. Inversion of this GMF at each WVC revealed two wind vector solutions. As the satellite propagates along its orbit it thus produced a swath of WVCs with ambiguous winds, so that statistical techniques that exploited prior information on the spatial structure of the surface wind field were developed to resolve these ambiguities (e.g. Offiler, 1987).

2.1 Launch of the ERS Scatterometer

At the launch of ERS‑1, the European Centre for Medium-Range Weather Forecasts (ECMWF) was prepared to validate the ESCAT backscatter data and winds. However, comparisons of ESCAT and ECMWF backscatter and wind distributions (see Fig. 4), showed large differences depending on the radar beam, incidence angle, wind speed and wind direction. Since the ECMWF routinely monitors the quality of their winds and this had been intensified prior to the ERS-1 launch, it was clear that there were some problems with the

Figure 4. Difference between backscatter data from ESCAT and the ECMWF wind analysis using the CMOD2 GMF in September 1991. A large bias existed depending on the ERS‑1 radar beam and incidence angle. The blue, green and red curves represent the fore, mid- and aft beams, respectively.

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ESCAT winds. Since the bias patterns were very stable and repetitive, there was hope that they could be removed.

2.2 ESCAT Measurement Space

Cavanié et al. (1986) made a 3D plot, where radar cross sections of the fore beam were plotted on the horizontal (x) axis, those of aft beam on the other horizontal (y) axis and mid-beam backscatter on the vertical (z) axis, as depicted in Fig. 5. They realised that due to wind speed and direction sensitivity, a two-parameter manifold or surface should emerge in such a 3D space. Stoffelen (1998) set out to make cross sections through this surface in order to observe whether the measured backscatter triplets would indeed follow a manifold and whether the manifold was well described by CMOD2. This led to a true revolution in scatterometry, as described below. The expected coherence in the measurement data was found and indeed the backscatter triplets are arranged in close proximity to a conical surface, as depicted in Fig. 5. The major axis of the ‘cone’ corresponds to variations in wind speed, whereas the minor axes represent changes in wind direction, confirming a basic sensitivity to the near-surface wind vector over the world’s oceans (Stoffelen, 1997a, 1998). The actual shape of the manifold has been characterised to high precision and the GMF has been improved accordingly (Stoffelen, 1998). The resulting C-band GMF, called CMOD5.N (Hersbach et al., 2007), is also used for Synthetic Aperture Radar (SAR; Portabella, 2002) and ASCAT wind retrieval (Stoffelen & Anderson, 1997a). The measured triplets are spread across the ideal conical manifold and this spread has been characterised in detail by Stoffelen & Anderson (1997a). Later, Portabella & Stoffelen (2006) modelled this spread in the ERS data and found that at high winds it is mainly determined by radar (speckle) noise, but at low winds by local wind variability. Since the fore, mid- and aft beams sample a WVC slightly differently, due to their different sampling sequences and footprint shapes, variable winds may cause the mean wind in the spatially integrated radar footprint over a WVC to be slightly different for each of the

Figure 5. Depiction of the CMOD5.N GMF manifold at the outer ESCAT swath in measurement space spanned by the backscatter measurements of the fore, aft and mid antennas (red, green and blue axes, respectively). The colours indicate the wind direction (red 0°, green 120°, blue 240°, all with reference to the mid-beam). The GMF is split at wind speeds of 10 m s–1, 20 m s–1 and 30 m s–1, respectively, to reveal its internal structure. (Vogelzang & Stoffelen, 2012)

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three beams. This effect is particularly relevant for low winds, as these show the highest relative variation in surface backscatter. The inconsistency in the spatial collocation of fore, mid- and aft beams and the resulting backscatter uncertainty due to local (sub-WVC) wind variability has been called ‘geophysical noise’ by Portabella & Stoffelen (2006). In the cross sections (as in Fig. 6) occasional triplets are seen that lie almost in the middle of the cone (i.e. along its major axis) and rather far away from the wind GMF manifold as measured by the nominal noise parameters. These triplets do not correspond to good-quality winds and are rejected after wind retrieval by a Quality Control (QC) step. For ERS and ASCAT, about 0.5% of data over the open ocean (Portabella et al., 2011) could be rejected as being unlikely wind triplets. The same QC methodology has been applied to the NASA Scatterometer (NSCAT; Figa & Stoffelen, 2000) and SeaWinds instruments at the Ku-band with great success (Portabella & Stoffelen, 2001, 2002). At this wavelength about 5% of open-ocean WVCs are rejected by the QC procedure, most often due to rain clouds. It was also noted that in cross sections across the cone (Fig. 6, left panels) the measurements appear to be triangular in shape. Stoffelen & Anderson (1997a) realised that prior knowledge of this particular shape could be used in the wind retrieval in order to avoid irregularities (attractors) in the retrieved wind directions. They found a rather simple transformation of the backscatter measurement space that results in a circular manifold. A circular manifold has constant prior probability of each wind direction and is straightforward in the wind retrieval. Later, Stoffelen & Portabella (2006) noted that the wind retrieval in the transformed measurement space with a circular GMF manifold is regular since it results in a wind vector sensitivity that is rather smooth and constant. However, while the transformation works well for ESCAT-type scatterometers, constant wind vector sensitivity cannot be obtained for rotating pencil-beam scatterometers. So, due to rain and measurement geometry rotating pencil- beam Ku-band scatterometers are difficult to handle, but the experiences with ESCAT resulted in unprecedented-quality retrievals for QuikScat and OceanSat-2

Figure 6. Cross sections through the manifold in the ESCAT measurement space (see Fig. 5) across the manifold (left panels) and along its main axis (right panels). (AMS, Stoffelen & Anderson, 1997a)

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Table 1. Wind component error standard Dataset ε (m s–1) ν deviations with respect to the (different) Buoy ECMWF Scatterometer scales resolved by the different ASCAT-12.5 1.21 1.23 1.54 1.55 0.69 0.82 scatterometer wind products as obtained by triple collocation. The lower the buoy ASCAT-25 1.24 1.30 1.42 1.45 0.65 0.74 error, the better the scatterometer product SeaWinds − KNMI 1.40 1.44 1.19 1.27 0.79 0.63 resolves the buoy scales, and thus the SeaWinds − NOAA 1.39 1.41 1.20 1.30 1.20 1.04 higher the resolution. (AGU, Vogelzang et al., 2011)

(Stoffelen et al., 2013; Vogelzang et al., 2011). Table 1 provides evidence of the high accuracy of the scatterometer products based on ESCAT heritage. The final advance in the geophysical interpretation of the ESCAT backscatter data was its use to model sea ice and to discriminate between water and sea ice surfaces. Just like wind over water, sea ice has a very particular signature in the measurement space. Sea ice surfaces are generally isotropic with varying roughness. Indeed, sea ice points lie on a line in measurement space, where the coordinate along the line depicts sea ice roughness (De Haan & Stoffelen, 2001). Subsequently, Bayesian methods were used to compute the probability of water in the WVC (Belmonte Rivas & Stoffelen, 2011). This method, first developed for ESCAT, is now also in use for the QuikScat and the OceanSat-2 scatterometers. In summary, the advances inspired by the ESCAT measurement space revolutionised not only the ESCAT wind retrieval methodology, but also those of all other wind scatterometers.

2.3 ESCAT Wind Quality

Besides the advances inspired by the ESCAT measurement space, the ERS era brought other innovations as well. The first was ocean calibration by NWP model wind fields (Stoffelen, 1998), which is still applied today to obtain interbeam backscatter calibration and the highest-quality ASCAT winds (Verspeek et al., 2012). Another statistical assessment showing the high quality and consistency of the ESCAT and ASCAT instruments and wind retrievals was provided by Hersbach (2008); the plot in Fig. 7 was produced after calibrating both ESCAT and ASCAT data to the ECMWF model. Another innovation was in triple wind collocation (Stoffelen, 1998), which is used today for ASCAT wind quality assessment (see Table 1), but also in several other Earth observation disciplines, such as quality assessments of soil moisture, sea surface temperature, sea ice drift and altimeter wave height data. Together, NWP Ocean Calibration (NOC) and triple collocation essentially tie the scatterometer winds to the global buoy wind network and at the same time provide statistical evidence of biases in the NWP model (e.g. ECMWF). NOC and triple collocation are performed routinely to assess the stability of scatterometer winds over time (see www.knmi.nl/scatterometer). Since the global moored buoy wind network is considered as an absolute reference, the combination of triple collocation and NOC may also be used to intercalibrate scatterometers, in particular ESCAT and ASCAT, with the objective of obtaining decadal scatterometer wind time series. These procedures could thus be applied to obtain a high-quality Fundamental Climate Data record for the ESCAT data. The excellent statistical verification of both ESCAT and ASCAT winds was corroborated by the inspection of wind maps such as the one shown in Fig. 1 (see www.knmi.nl/scatterometer). The spatially smooth wind vectors from the High-Resolution Limited Area Model, shown in blue and purple, depict a cold northerly flow on the left, adjacent to a warmer southerly flow on the right (the amount of purple increases with wind speed). The scatterometer winds from ERS‑2 at around 12:00 GMT on 24 November 1999 in the Norwegian Sea (indicated in red) show much more structure and detail. Note that the grey-

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Figure 7. Joint distribution of collocated ESCAT and ASCAT winds with a Pearson correlation of 98.9% and standard deviation of differences of 0.77 m s–1. Blue dots: average ERS‑2 wind speeds in a 1 m s−1 ASCAT wind speed bin; red dots: average ASCAT wind speeds in an ERS‑2 bin. (From Hersbach, 2008)

shaded Meteosat IR image is coherent with the scatterometer winds, with wind convergence patterns lining up well with cloud patterns, revealing details of the local meteorological conditions. Since C-band scatterometers are much less affected by rain than Ku-band scatterometers, the representation of deep tropical convection will be much improved. However, there may still be issues of concern due to effects of splash, i.e. rain droplets roughening the sea surface, or wind downbursts. The latter are due to air cooled by melting and evaporating precipitation, thus increasing its mass density and falling to the sea surface, where the air is deflected in the horizontal direction, often in a circular outflow pattern. The horizontal extent of downburst patterns is generally much larger than that of the associated rain patterns. Figure 8 shows a representative example of moored buoy winds in the tropics in the presence of rain. Wind shifts as large as 10 m s–1 are often associated with local rain. However, convective areas also show wind shifts when no rain is measured at the buoy. This is most likely because the horizontal extent of downburst areas is larger than those of rain areas. ASCAT winds are variable in convective areas, but prove a reasonable representation of the local buoy winds, given the rather high wind variance in the spatial representation difference of buoy and ASCAT. Triple collocation shows that the ECMWF model winds are clearly further away from the buoy winds, as expected, given the rather smooth wind fields in such convective areas (Portabella et al., 2011). It may be clear that convective processes in the tropics have a rather large effect on the surface winds and air−sea interaction processes, but also on vertical exchange in the atmosphere and tropical circulation. It remains a challenge today to assimilate the full spatial detail as measured by a scatterometer into NWP models. Therefore, process studies using scatterometer data will remain necessary in order to exploit scatterometer data to the full.

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Figure 8. Wind speed (left axis) and rain rate (right axis) as functions of time at a rainy buoy location as explained in the legend. Time is centred around an ASCAT overpass (black dot) where the reference ECMWF forecast winds have base time at –25 h. (Portabella et al., 2011)

3. Benefits of the ERS Scatterometers 3.1 Wind

Retrieved ERS scatterometer winds have proven to be very useful for the forecasting of dynamic weather (Le Meur et al., 1998; Isaksen & Stoffelen, 2000) using the 4D-Var data assimilation system. This is mainly due to the capability of the 4D-Var system to assimilate winds with dual ambiguity (Stoffelen & Anderson, 1997b) and to propagate the ESCAT surface information vertically (Isaksen & Janssen, 2004). Increased coverage, such as from tandem ERS‑1/ ERS‑2 measurements, clearly improve the forecasts of extreme events (e.g. Stoffelen & Beukering, 1997; Le Meur et al., 1998); see Fig. 9. Severe storms that hit Europe often originate over the North Atlantic, where sparse meteorological observations are available. Consequently, the initial stages of severe storms are often poorly analysed and their development poorly predicted (e.g. ESA, 1999). As a result, occasional devastating ocean or coastal wind and wave conditions remain a major challenge for NWP. In addition, NWP data assimilation systems are cycled over observation windows, e.g. of 6 h, with cutoff times in the range of 2–6 h. Therefore, particularly in case of fast weather developments, which often have large societal impacts, the timely use of satellite observations is complicated. Scatterometer data are therefore used for nowcasting applications such as hurricane warnings over sea (Sienkiewicz et al., 2010) for marine traffic or offshore activities. Moreover, the near-surface wind conditions drive the ocean circulation, which in turn plays a major role in the climate system and in marine ecosystems (e.g. fisheries). Scatterometer winds have proven to be very relevant in driving ocean circulation and air–sea interactions (Chelton et al., 2004; Tokmakian, 2005; Blanke et al., 2005; Liu & Xie, 2006), which in turn play a major role in the climate system and marine biology (Moore & Renfrew, 2005). An overview of the Ocean Vector Wind observation requirements, capabilities and applications is provided in Bourassa et al. (2009).

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Figure 9. Mean sea level pressure fields from the ECMWF model valid at 12:00 UTC on 6 September 1995. Top left: Five-day forecast using the 4D-Var data assimilation system without ERS scatterometer data. Bottom left: Forecast using the 4D-Var data system with scatterometer data. Top right: Analysed field for verification of the forecasts showing an excellent tropical cyclone strength forecast with ESCAT winds at the 5-day range. (Le Meur et al., 1998)

3.2 Land Surface Monitoring

Because ESCAT was designed for wind monitoring, it was initially not clear if and how it might be useful for monitoring of the land surface. One particular concern was that the spatial resolution of ESCAT is simply too coarse to be of value for land applications due to the high heterogeneity of land surfaces. Nevertheless, the global systematic coverage achieved by ESCAT attracted several research groups who began to investigate the capabilities of ESCAT for mapping of vegetation (Frison & Mougin, 1996), soil moisture (Wagner et al., 1999b), freeze/thawing (Wismann, 2000) and snow (Drinkwater et al., 2001). One of the scientific challenges faced by all initial land surface studies was the strong dependency of the backscattering coefficient on the incidence angle, which varies from 18° for the mid-beam in the near-range, to 59° for the fore and aft beams in the far range. Therefore, the backscattering coefficient changes significantly over the image swath, and from acquisition to acquisition, making the interpretation of the images, or time series, elusive without a prior correction or normalisation of the backscatter data. Thus many of the initial studies addressed this dependency by fitting a linear function to the ESCAT backscatter measurements collected over a long period such as a month (Mougin et al., 1995; Schmullius, 1997), following a procedure previously developed for the SASS scatterometer by Kennett & Li (1989). This allowed studies of the relationship between the ESCAT measurements and global land cover, and seasonal land surface dynamics due to vegetation phenology or freeze/thaw processes in high-latitude areas. Nevertheless, this monthly fitting procedure suppresses short-term signal fluctuations caused, for example, by changes in soil moisture, short-term freeze/thaw events, or changes in snow morphology, rendering a more physically based interpretation of the ESCAT data impossible.

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To improve our capabilities to analyse ESCAT data over land, more advanced modelling approaches have been developed in which the ESCAT signal can be decomposed into its different backscatter components. One such approach is the method developed by Wagner et al. (1999b), where in one of the first processing steps the incidence angle dependency of σ0 is determined for each land surface pixel and for each day of the year by comparing the quasi-simultaneous mid- and aft/fore beam backscatter measurements. This knowledge of the incidence angle behaviour then not only allows extrapolation of the backscatter measurements to a given reference angle (40°), but also separating the backscatter contributions due to seasonal vegetation growth and decay from the shorter-term soil moisture fluctuations (Wagner et al., 1999a). Furthermore, several semi-empirical backscatter models typically composed of bare soil backscatter- and volume scattering formulations for simulating backscatter of vegetation- and snow-covered surfaces have been developed (Magagi & Kerr, 1997; Pulliainen et al., 1998; Wen & Su, 2003; Woodhouse & Hoekman, 2000; Zribi et al., 2008). These semi-empirical models can be used to improve our theoretical process understanding and, at a more practical level, the simultaneous retrieval of soil moisture, vegetation and other land surface parameters (e.g. roughness) using iterative least-squares matching procedures. These models have been implemented over selected world regions with varying degrees of success, but not yet on a global scale. These research efforts led to the derivation of the first global long-term (1992−2000) soil moisture database using ESCAT data from both ERS‑1 and ERS‑2 (Scipal et al., 2002; Wagner et al., 2003; see Fig. 10). This dataset was released in 2002 by the Vienna University of Technology (TU Wien) and freely shared with the scientific community. Fortunately, the dataset raised considerable interest and several validation studies carried out by independent research

Figure 10. Mean monthly soil moisture derived from ESCAT backscatter data (1992−2000) for January, April, July and October. Brown tones indicate dry conditions (wilting level), blue tones indicate wet conditions (field capacity). Tropical forest, desert regions with strong azimuth effects, and areas affected by snow and frost have been masked out.

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teams quickly demonstrated the high quality of the data (Dirmeyer et al., 2004; Drusch et al., 2004; Pellarin et al., 2006). This was unexpected because active C-band measurements had not been considered to be well suited for the retrieval of soil moisture. This was mainly because, first, active measurements are more sensitive to surface roughness compared with passive microwave measurements and, second, the C-band (λ = 3.8−7.5 cm) is less able to penetrate vegetation and the soil than the L-band (λ = 15−30 cm) (Wagner et al., 2007). Exactly for these reasons, passive long-wavelength measurement concepts have been selected for ESA’s Soil Moisture and Ocean Salinity (SMOS) mission and NASA’s Soil Moisture Active Passive (SMAP) mission – the first two satellite missions developed specifically for the retrieval of soil moisture – utilising passive sensors operating at the L-band (Entekhabi et al., 2004; Kerr, 2007). Nevertheless, the positive results from the validation studies, and the increasing use of the ESCAT soil moisture database in such diverse applications as hydrology (Parajka et al., 2006; Scipal et al., 2005), Numerical Weather Prediction (Scipal et al., 2007; Zhao et al., 2006), agronomy (De Wit & van Diepen, 2007) and climate monitoring (Künzer et al., 2009) led to the recommendation to build up a global near-realtime soil moisture service for ASCAT. This operational service was developed by Eumetsat in cooperation with TU Wien, and went fully operational in December 2008 (Bartalis et al., 2007; Wagner et al., 2010). In retrospect, it can be said that this unexpected success was possible because of ESCAT’s unique characteristics:

—— the multi-incidence angle viewing capability, which allows the separation of vegetation and soil moisture effects; —— the high temporal sampling rate, which allows researchers to exploit the advantages of change detection; and —— the excellent radiometric accuracy, which results in a suitable signal-to-noise ratio for the task of soil moisture retrieval.

Now that the ERS‑2 mission has come to a successful end, there is an opportunity to reprocess the complete ESCAT data archive to 25 km to create a Fundamental Climate Data Record (FCDR) that will provide crucial inputs to any effort dealing with the creation of an Essential Climate Variable (ECV) record on soil moisture (Wagner et al., 2009). The first efforts in this direction are already underway and it is planned to release an improved 25-km resolution ESCAT soil moisture dataset in 2013. Furthermore, by merging ESCAT and ASCAT soil moisture data with a suite of soil moisture products derived from passive microwave sensors (SMMR, SSM/I, TMI, AMSR-E) it will be possible to create a 30+ year soil moisture ECV data record (Dorigo et al., 2010; Liu et al., 2011). Such a first soil moisture ECV dataset, where ESCAT is essential for characterising the climate in the 1990s, is also planned for release in 2013.

3.3 e Sea Ic and Drift

The extent of sea ice over the polar oceans is a critical parameter for understanding and forecasting ocean circulation and climate change. No satisfactory conventional observing system is available and sea ice is difficult to observe using satellite optical sensors because of the low level of illumination and the frequent cloud cover in polar regions. Microwave instruments and in particular satellite-borne radars, such as the ESCAT scatterometers, are of great interest because of their large temporal and spatial coverage and their all-weather measurement capability. The development of specific data analysis methods for these radars led to the detection of sea ice and to estimates of its extent, drift and age (see Fig. 11). A routine ESCAT processing has been developed at the ERS Processing and

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Figure 11. Images of the backscatter coefficient over the Arctic region, observed at an incidence angle of 40° during October 1995 (ERS‑1, left) and October 1996 (ERS‑2, right). Dark blue: unprocessed areas; grey: open water. The highest values of the backscatter coefficient are observed for multi-year ice (ice that has survived at least one summer), shown in green or yellow. This pair of images shows how the sea ice situation can differ from year to year. In 1996, new and multi-year ice covered the coastal zones of Siberia where large areas of open water were apparent in the 1995 image. (CERSAT, 2011) Archiving Facility of the French Research Institute for Exploitation of the Sea (CERSAT/IFREMER) to produce fields of sea ice age, drift and extent, with polar coverage, on a weekly and monthly basis. Over polar oceans, values of backscatter measurements depend on the dielectric properties of the observed materials − sea water, first-year ice and multi-year ice − on their relative concentrations and on surface topography. As sea ice ages, its dielectric properties, linked to volume and surface scattering, as well as its surface roughness are modified. Sea ice characteristics are derived from backscatter levels at prescribed incidence angles (40°), and from backscatter variations with incidence angle. In summer, surface melting of sea ice changes the backscatter signature and renders interpretation of the data more difficult.

4. The Future 4.1 C-band Fan Beam Scatterometer Heritage

ESCAT was followed by ASCAT, a clear heritage instrument, building on the success of the ESCAT instrument and ground processing. The Eumetsat Polar System programme lists the Second Generation ASCAT as a high- priority instrument. Specific requirements include slightly increased spatial resolution, extended swath, radiometric stability with reference to ASCAT, and the addition of horizontal polarisation. The C-band was chosen because it provides good sensitivity, all-weather capability and continuity of the ESCAT and ASCAT series. Rotating pencil beam concepts were discarded due to poorer simulated performance, while rotating fan beam concepts were considered to be technically too complex, since ASCAT-SG would share a satellite platform with several other Earth-viewing microwave instruments (Lin et al., 2012). The addition of horizontal polarisation could potentially result in an improved capability to sense hurricane winds, since the vertical emit and receive polarisation of ESCAT and ASCAT saturates at around 40 m s–1. Since Eumetsat, together with many international sister agencies, is involved in building a global constellation of ocean vector wind missions (CEOS VC) and obtaining extreme winds is a prerequisite for these sister agencies, the requirement for measuring extreme winds becomes more important. Horizontal polarisation in addition to vertical polarisation, however, requires an extended-capability antenna, which may be best accommodated on the mid-beams that are fixed on the body of the satellite platform. When emitting and receiving horizontal polarisation (HH) with the mid-beam, the additional sensitivity to hurricane winds would be limited as the geophysical dependency of HH is only favourable for extreme winds at incidence angles above 35° (see Fig. 12). Therefore, ASCAT-SG incidence angles from 20° to 35° would not profit much from the extended HH capability.

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Figure 12. C-band horizontal polarisation backscatter as a function of wind speed. The solid lines represent HH at varying incidence angles, while the red dotted line represents anticipated VH (see text). For extreme winds the sensitivity to wind speed appears to be most favourable for VH backscatter measurements.

Another, more convenient, option would be to use the vertically emitted pulses and use the dual-polarisation mid-beam antennas to receive both the vertical (VV) and horizontal polarisations (VH). Recently, Vachon & Wolfe (2011) reported on the Radarsat SAR, which has cross-polarisation or VH capability, a linear dependency of VH backscatter on wind speed. No significant dependency on azimuth or incidence angle was found. Preliminary verification of the Radarsat SAR VH backscatter dependency in tropical hurricanes confirmed a linear dependency at extreme winds as well, but with somewhat reduced slope. NOAA’s aircraft campaigns and Eumetsat’s Satellite Application Facility for Numerical Weather Prediction (NWP SAF) performance assessments are planned to verify the VH geophysical dependency at extreme winds. Cross-polarisation capability would furthermore improve vegetation determination and, as a consequence, soil moisture index retrieval. Advantages for sea ice detection are also anticipated. Following the successful ERS mission at the C-band and successful NASA missions at the Ku-band, several agencies plan to develop scatterometer systems combining both wavelengths. In this way, resolution enhancements may be combined with the C‑band all-weather capability.

4.2 Other Wind Resources

Besides using scatterometers, components of the wind field can be measured with SAR (e.g. Portabella et al., 2002) or passive radar measurements (WindSat, 2011; SSMIS, 2011). As a scatterometer, a SAR also measures the radar backscatter but at higher resolution (down to ~100 m) and in only one azimuth direction. The GMF derived for ESCAT is often used for C-band SAR wind retrieval. The latter is a problem since it is not possible to resolve both wind direction and speed at a matching spatial resolution without prior information, while the sensitivities to wind speed and direction (in m s–1 per dB) are comparable (underdetermined problem). For passive radar measurements a similar underdetermination applies, but here the wind direction sensitivity of the measurements is so small that wind speed can be retrieved with sufficient accuracy. However, a more significant problem is that the microwave measurement sensitivity to rain, cloud liquid water and humidity has to be

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appropriately accounted for by using multiple-frequency measurements. This limits the performance of passive radar winds in dynamic weather conditions.

4.3 Doppler Scatterometer

Scatterometer winds are measured relative to the ocean, whereas buoy and NWP model winds are provided with reference to a fixed Earth reference. Comparisons of these datasets therefore require that ocean currents are known. Direct current measurements would greatly improve ocean modelling, particularly in the tropics. ESA recently launched a project that aims to assess the potential of scatterometer instruments for sea surface current retrieval in addition to surface wind vector estimation. Besides new conceptual ideas, the project will provide recommendations on signal processing techniques and GMF development for surface current retrieval from the Doppler estimates. Since the wind forces a net motion on the ocean waves, called the Stokes drift, the Doppler capability will also provide wind information, which may be combined with backscatter data.

5. Conclusions

C -band scatterometer data are now widely used for hurricane prediction, Numerical Weather Prediction, marine nowcasting, oceanography, hydrology and studies of the cryosphere, providing societal and economic benefits in areas such as marine safety, offshore activities, ship routing, wind energy and climate change monitoring. The ESCAT research mission anticipated such a wide range of applications, with for example the near-realtime availability of the scatterometer winds. The launch of ERS‑1 inspired the development of novel analytical techniques using the measurement space of ESCAT, which confirmed the expected internal consistency of the ESCAT measurements and the low noise, but also provided improved characterisation of the sensitivity to wind speed and direction, resulting in improved GMFs. Moreover, the visualisation of the ESCAT measurement space provided insights into the nonlinear wind retrieval problem, which has subsequently been improved. It has also aided the discrimination of sea ice and water and has provided important insights into the wind retrieval quality control. The ESCAT wind Cal/Val led to the further development of the triple collocation methodology that is now widely used in Earth observation. The lessons learnt from ESCAT were readily applied to the NASA scatterometer missions at the Ku-band and provided similar benefits, leading to improved rain screening and wind datasets. ESCAT has also been successfully applied in soil moisture, sea ice and snow characterisation, leading to operational applications. The ESCAT heritage has been important for the development of ASCAT on MetOp for the Eumetsat Polar System. The EPS Second Generation ASCAT is now being designed, where the ESCAT C-band static fan beam concept is being maintained due to its great success. This may well lead to over 40 years of C-band scatterometer data, which will be a tremendous resource for climate applications to study wind climatologies, air–sea interactions, atmospheric processes, etc. The demand for the unique ESCAT wind, soil moisture, snow and sea ice data will therefore remain high in the years to come, and continued ESA data and processing services would provide great benefit to society.

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A cknowledgements

The NWP SAF scatterometer wind processors for SeaWinds (SDP) and ASCAT (AWDP) are freeware, and can be obtained upon registration at www.nwpsaf.org. Access to near-realtime scatterometer wind products can be obtained upon registration at www.osi-saf.org. The NWP SAF and OSI SAF are sponsored by Eumetsat. TU Wien would like to acknowledge the support of the Austrian Science Fund (FWF), the Austrian Space Application Programme (ASAP) and ESA for developing the first ESCAT soil moisture database. The support through the enhanced-resolution ERS‑2 Scatterometer Soil Moisture Product project funded by ESA is also gratefully acknowledged.

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Eumetsat Meteorological Satellite Conf., September 2010, Cordoba, Spain. www.eumetsat.int/Home/Main/AboutEUMETSAT/Publications/ ConferenceandWorkshopProceedings/2010/groups/cps/documents/document/ pdf_conf_p57_s2_06_lin_v.pdf Liu, W.T. & Xie, X. (2006). Measuring ocean surface wind from space. In J. Gower (Ed), Remote Sensing of the Marine Environment, Manual of remote Sensing. 3rd edn, vol. 6, American Society for Photogrammetry and Remote Sensing, pp.149–178. Liu, Y.Y., Parinussa, R.M., Dorigo, W.A., de Jeu, R.A.M., Wagner, W., van Dijk, A., McCabe, F.M. & Evans, J.P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrol. Earth Syst. Sci. 15 (2), 425-436. Long, A.E. (1986). Towards a C-band radar echo model for the ERS‑1 scatterometer. 3rd Int. Coll. Spectral Signatures in Remote Sensing, Les Arc, France. ESA SP-247, European Space Agency, Noordwijk, the Netherlands, pp.29–34. Magagi, R.D. & Kerr, Y.H. (1997). Retrieval of soil moisture and vegetation characteristics by use of ERS‑1 wind scatterometer over arid and semi-arid areas. J. Hydrol. 188–189, 361–384. Moore, G.W. & Renfrew, I.A. (2005). Tip jets and barrier winds: a QuikSCAT climatology of high wind speed events around Greenland, J. Climate 18, 3713– 3725. Moore, R.J. & Pierson, W.J. (1967). Microwave determination of winds at sea. Proc. IEEE 67, 1504–1521. Mougin, E., Lopes, A., Frison, P.L. & Proisy, C. (1995). Preliminary analysis of ERS‑1 wind scatterometer data over land surfaces. Int. J. Rem. Sens. 16(2), 391–398. Offiler, D. (1987). ERS‑1 Wind Retrieval Algorithms. UK Met Office, MET O 19, Branch Memorandum, No. 86. Parajka, J., Naeimi, V., Blöschl, G., Wagner, W., Merz, R. & Scipal, K. (2006). Assimilating scatterometer soil moisture data into conceptual hydrologic models at the regional scale. Hydrol. Earth Syst. Sci. 10, 353–368. Pellarin, T., Calvet, J.-C. & Wagner, W. (2006). Evaluation of ERS scatterometer soil moisture products over a half-degree region in southwestern France. Geophys. Res. Lett. 33(17), L17401. Peteherych, S., Wurtele, M.G., Woiceshyn, P.M., Boggs, D.H. & Atlas, R. (1984). First global analysis of SEASAT scatterometer winds and potential for meteorological research. NASA Conf. Publ. CP2303, pp.575–585. Portabella, M. (2002). Wind Field Retrieval from Satellite Radar Systems. Thesis, University of Barcelona, Spain. www.knmi.nl/publications/fulltexts/phd_thesis.pdf Portabella, M. & Stoffelen, A. (2001). Rain detection and quality control of sea winds. J. Atm. Ocean Techn. 18(7), 1171–1183. Portabella, M. & Stoffelen, A. (2002). A comparison of KNMI quality control and JPL rain flag for seawinds. Can. J. Rem. Sens. 28(3), 424–430. Portabella, M. & Stoffelen, A. (2006). Scatterometer backscatter uncertainty due to wind variability. IEEE Trans. Geosci. Rem. Sens. 44(11), 3356–3362. doi:10.1109/ TGRS.2006.877952 Portabella, M., Stoffelen, A. & Johannessen, J.A (2002). Toward an optimal inversion method for SAR wind retrieval. J. Geophys. Res. 107(C8), 1–13. Portabella, M., Stoffelen, A., Verhoef, A. & Lin, W. (2011). Rain effects on ASCAT retrieved winds: towards an improved quality control, IEEE Trans. Geosci. Rem. Sens. 50(7), 2495–2506. Pulliainen, J.T., Manninen, T. & Hallikainen, M.T. (1998). Application of ERS‑1 wind scatterometer data to soil frost and soil moisture monitoring in boreal forest zone. IEEE Trans. Geosci. Rem. Sens. 36(3), 849–863. Schmullius, C.C. (1997). Monitoring Siberian forests and agriculture with the ERS‑1 wind scatterometer. IEEE Trans. Geosci. Rem. Sens. 35(5), 1363–1366.

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Scipal, K., Drusch, M., Hasenauer, S., Wagner, W. & Jann, A. (2007). Towards the assimilation of scatterometer derived soil moisture in the ECMWF numerical weather prediction system. Joint 2007 Eumetsat Meteorological Satellite Conf. and 15th Satellite Meteorology & Oceanography Conf. of the American Meteorological Society, Amsterdam, the Netherlands. Scipal, K., Scheffler, C. & Wagner, W. (2005). Soil moisture–runoff relation at the catchment scale as observed with coarse resolution microwave remote sensing. Hydrol. Earth Syst. Sci. 9(3), 173–183. Scipal, K., Wagner, W., Trommler, M. & Naumann, K. (2002). The Global Soil Moisture Archive 1992–2000 from ERS scatterometer data: First results. Proc. IGARSS 2002. Sienkiewicz, J., Brennan, M.J., Knabb, R., Chang, P.S., Cobb, H., Jelenak, Z.J., Ahmad, K.A., Soisuvarn, S., Kosier, D. & Bancroft, G. (2010). Impact of the loss of QuikSCAT on NOAA MWS marine warning and forecast operations. 1st Int. Ocean Vector Winds Science Team Meeting, Barcelona, Spain. SSMIS (2011). www.ncdc.noaa.gov/oa/rsad/ssmi/swath/index.html Stoffelen, A. (1998). Scatterometry, PhD Thesis, Utrecht University, the Netherlands. http://igitur-archive.library.uu.nl/dissertations/01840669/inhoud.htm Stoffelen, A. & Anderson, D. (1993). Wind retrieval and ERS‑1 scatterometer radar backscatter measurements. Adv. Space Res. 13(5), (5)53–(5)60. Stoffelen, A. & Anderson, D. (1997a). Scatterometer data interpretation: measurement space and inversion. J. Atm. Ocean Techn. 14(6), 1298–1313. Stoffelen, A. & Anderson, D. (1997b). Ambiguity removal and assimilation of scatterometer data. Q. J. R. Meteorol. Soc. 123, 491–518. Stoffelen, A. & van Beukering, P. (1997). Implementation of Improved ERS Scatterometer Data Processing and its Impact on HIRLAM Short Range Weather Forecasts. HIRLAM Technical Report No. 31, KNMI, the Netherlands. Stoffelen, A. & Cats, G. (1991). The impact of SeaSat-A scatterometer data on high- resolution analyses and forecasts: The development of the QEII storm. Mon. Wea. Rev. 119, 2794–2802. Stoffelen, A., & Portabella, M. (2006). On Bayesian scatterometer wind inversion, IEEE Trans. Geosci. Rem. Sens. 44(6), 1523–1533. doi:10.1109/TGRS.2005.862502 Stoffelen, A. & Verhoef, A. (2011). Validation of Oceansat-2 scatterometer data, Proc. Eumetsat Satellite Conf., Oslo, Norway, www.eumetsat.int/Home/Main/ AboutEUMETSAT/Publications/ConferenceandWorkshopProceedings/2011/ groups/cps/documents/document/pdf_conf_p59_s5_06_stoffele_v.pdf Stoffelen, A., Verhoef, A., Verspeek, J., Vogelzang, J., Driesenaar, T., Risheng, Y., Payan, C., De Chiara, G., Cotton, J., Bentamy, A., Portabella, M. & Marseille, G.J. (2013). Research and Development in Europe on global application of the OceanSat-2 Scatterometer winds, Document extern project: 2013, NWP SAF report number: NWPSAF-KN-TR-022 OSI SAF report no. SAF/OSI/CDOP2/ KNMI/TEC/RP/196, KNMI, De Bilt, the Netherlands. www.knmi.nl/publications/ fulltexts/oceansat_cal_val_report_final_copy1.pdf Tokmakian, R. (2005). An ocean model’s response to scatterometer winds. Ocean Modeling 9, 89–103. Vachon, P. & Wolfe, J. (2011). C-band cross-polarization wind speed retrieval. IEEE Trans. Geosci. Rem. Sens. Lett. 8(3), 456–459. doi: 10.1109/LGRS.2010.2085417. Verspeek, J., Stoffelen, A. Verhoef, A. & Portabella, M. (20112. Improved ASCAT wind retrieval using NWP ocean calibration. IEEE Trans. Geosci. Rem. Sens. 50(7), 2488–2494. doi: 10.1109/TGRS.2011.2180730 Vogelzang, J., Stoffelen, A., Verhoef, A. & Figa-Saldaña, J. (2011). On the quality of high-resolution scatterometer winds. J. Geophys. Res. 116, C10033. doi:10.1029/2010JC006640. Vogelzang, J. & Stoffelen, A. (2012). Scatterometer wind vector products for application in meteorology and oceanography. J. Sea Res. special issue on Physics of Sea and Ocean, 74, 16–25.

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Wagner, W., Bartalis, Z., Naeimi, V., Park, S.-E., Figa-Saldana, J. & Bonekamp, H. (2010). Status of the METOP ASCAT soil moisture product. Proc. IEEE Geoscience and Remote Sensing Symp., IGARSS 2010, Honolulu, USA, pp.276–279. Wagner, W., Blöschl, G., Pampaloni, P., Calvet, J.-C., Bizzarri, B., Wigneron, J.-P. & Kerr, Y. (2007). Operational readiness of microwave remote sensing of soil moisture for hydrologic applications. Nordic Hydrol. 38(1), 1–20. Wagner, W., de Jeu, R. & van Oevelen, P. (2009). Towards multi-source global soil moisture datasets for unravelling climate change impacts on water resources. Proc. 33rd Int. Symp. on Remote Sensing of Environment (ISRSE). Joint Research Centre of the European Commission, Stresa, Italy, p.3. Wagner, W., Lemoine, G., Borgeaud, M. & Rott, H. (1999a). A study of vegetation cover effects on ERS scatterometer data. IEEE Trans. Geosci. Rem. Sens. 37(2), 938–948. Wagner, W., Noll, J., Borgeaud, M. & Rott, H. (1999b). Monitoring soil moisture over the Canadian Prairies with the ERS scatterometer. IEEE Trans. Geosci. Rem. Sens. 37(1), 206–216. Wagner, W., Scipal, K., Pathe, C., Gerten, D., Lucht, W. & Rudolf, B. (2003). Evaluation of the agreement between the first global remotely sensed soil moisture data with model and precipitation data. J. Geophys. Res. D: Atmospheres 108(D19), Art. No. 4611. Wen, J. & Su, Z.B. (2003). A time series based method for estimating relative soil moisture with ERS wind scatterometer data. Geophys. Res. Lett. 30(7), 1397. WindSat (2011). www.nrl.navy.mil/WindSat/ Wismann, V. (2000). Monitoring of seasonal thawing in Siberia with ERS scatterometer data. IEEE Trans. Geosci. Rem. Sens. 38(4 II), 1804–1809. Woodhouse, I.H. & Hoekman, D.H. (2000). Determining land-surface parameters from the ERS wind scatterometer. IEEE Trans. Geosci. Rem. Sens. 38(1), 126–140. Zhao, D., Su, B. & Zhao, M. (2006). Soil moisture retrieval from satellite images and its application to heavy rainfall simulation in eastern China. Adv. Atmos. Sci. 23(2), 299–316. Zribi, M., André, C. & Decharme, B. (2008). A method for soil moisture estimation in Western Africa based on the ERS scatterometer. IEEE Trans. Geosci. Rem. Sens. 46(2), 438–448.

164 →→TheES R SAR Wave Mode:→ A Breakthrough in Global Ocean Wave Observations

SAR Wave Mode

TheAR → eRS S Wave Mode: A Breakthrough in Global Ocean Wave Observations

K. Hasselmann1, B. Chapron2, L. Aouf3, F. Ardhuin4, F. Collard5, G. Engen6, S. Hasselmann7, P. Heimbach8, P. Janssen9, H. Johnsen10, H. E. Krogstad11, S. Lehner12, J.-G. Li13, X.-M. Li14, W. Rosenthal15, J. Schulz-Stellenfleth16

1 Max Planck Institute of Meteorology, 20146 Hamburg, Germany. [email protected] 2 IFREMER, 29280 Plouzané, France. [email protected] 3 Météo-France, Division Marine et Océanographie, 31100 Toulouse, France. [email protected] 4 IFREMER, 29280 Plouzané, France. [email protected] 5 OceanDataLab, 29280 Plouzané, France. [email protected] 6 Norut, P.O. Box 6434, 9294 Tromsø, Norway. [email protected] 7 Max Planck Institute of Meteorology, 20146 Hamburg, Germany. [email protected] 8 Massachusetts Institute of Technology, Cambridge, MA 02139, USA. [email protected] 9 ECMWF, Marine Aspects Section, Shinfield Park, Reading, RG2 9AX, UK. [email protected] 10 Norut, PO Box 6434, 9294 Tromsø, Norway. [email protected] 11 Norwegian University of Science and Technology, 7491 Trondheim, Norway. [email protected] 12 German Aerospace Center (DLR), Remote Sensing Technology Institute, Oberpfaffenhofen, 82234 Wessling, Germany. [email protected] 13 Met Office, Exeter, Devon, EX1 3PB, UK. [email protected] 14 German Aerospace Center (DLR), Remote Sensing Technology Institute, Oberpfaffenhofen, 82234 Wessling, Germany. [email protected] 15 Helmholtz-Zentrum Geesthacht, 21502 Geesthacht, Germany. [email protected] 16 Helmholtz-Zentrum Geesthacht, 21502 Geesthacht, Germany. Johannes.Schulz‑[email protected]

1. Introduction

The first European Remote Sensing satellite, ERS‑1, launched on 17 August 1991, was conceived in the 1980s, at a time of growing scientific and public awareness of the need to better understand, monitor and manage the Earth system. The dangers of environmental degradation, the alarming increase in the rate of loss of species, and the threat of irreversible climate change had emphasised the need to treat Earth as an integrated system. Satellites provided a unique opportunity to obtain the necessary global, continuous datasets. And an ocean satellite would clearly be needed as a key component of an integrated satellite Earth observation system in order to monitor the oceans, which cover more than two-thirds of Earth’s surface, and the ocean−atmosphere interface. To achieve a 24-hour, all-weather global capability, it was decided that the main instrument on ERS‑1 would be a multi-component Active Microwave Instrument (AMI), consisting of a scatterometer, a radar altimeter and a Synthetic Aperture Radar (SAR). Since they can see through clouds and are independent of sunlight, microwave systems have the great advantage of being able to operate under all conditions. The proof of concept of satellite-based microwave measurements of the ocean had been provided by the NASA−NOAA satellite Seasat, launched in August 1978. Although Seasat unfortunately failed through a power short-circuit after only 100 days of operation, initial analyses had demonstrated that microwaves, reflected or backscattered from the sea

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surface, could provide valuable data on sea surface heights, sea surface winds, wave heights and many other important features. In particular, it had been shown that the SAR could reveal detailed structural information on a wide variety of ocean phenomena. The SAR is an advanced radar imaging instrument that achieves high resolution not only in the range direction, through the standard radar technique of measuring the travel time of short microwave pulses, but also in the along-track (azimuthal) direction, by exploiting the Doppler shift induced by the satellite motion. The achievable azimuthal resolution is then of the order of the antenna length, as opposed to the inverse dependency on the antenna length for a conventional radar. The ocean surface features imaged by the Seasat radar included the propagation directions and wavelengths of surface waves and internal waves, current-induced surface signatures of bottom topography, and the distribution of oil slicks, eddies and fronts. Seasat not only provided a major technical impetus for an improved follow- on European satellite ERS‑1, but also created a strong motivation for European scientists to engage in satellite remote sensing of the oceans. The collaboration with American colleagues had demonstrated to European scientists the exciting opportunities that all-weather global satellite measurements offered for studies of the ocean, the coupled ocean−atmosphere system and the cryosphere. Particularly influential in this respect was John Apel (1930−2001), a long-standing advocate of ocean remote sensing and a key player in the Seasat project. One of the many important applications of ERS‑1 (1991–2000) and its successors ERS‑2 (1995−2011) and Envisat (2002−12) was in the field of ocean waves. The launch of ERS‑1 came at an opportune time for ocean wave research. In the three decades prior to the launch of ERS‑1, detailed theoretical studies and extensive field programmes had led to major advances in our understanding of the dynamics and propagation of wind-driven ocean waves. Numerical wave models, such as the WAM model (WAMDI, 1988), had been developed to compute the space–time evolution of the two-dimensional ocean wave spectrum, for given wind field forcing, as a function of period and propagation direction (for a comprehensive summary of ocean wave research and modelling, see Komen et al., 1994). What were needed now to drive the models were good surface wind data, and, to initialise and validate the models, good wave data. The combined modes of the Active Microwave Instrument of ERS‑1 were able, in principle, to provide all the data needed: surface winds, from the scatterometer and Radar Altimeter; mean wave heights, also from the Radar Altimeter; and the full two-dimensional surface wave spectrum, from the SAR.

2. The SAR Wave Mode

The SAR, as the most sophisticated of the AMI systems, provided potentially the most detailed ocean wave information. To reduce the so-called clutter noise, four single-look SAR images, with a theoretical resolution of 4 m, were averaged to produce multi-look images of about 20 × 16 m resolution. The SAR instrument produced millions of images of the ocean surface at this extremely high spatial resolution. But it also presented two major challenges: achieving global coverage and retrieving calibrated two-dimensional ocean wave spectra from the SAR images. The difficulty in achieving global coverage was that, in continuous operation, the available data generation rate of the SAR was far too large for onboard storage of the data and later transmission to a ground receiving station, after the satellite came in sight of one of the relatively few ground stations. The problem was resolved by introducing two operating modes for the SAR: a standard broad-swath mode, which could be switched on when

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Figure 1. ERS‑1 orbits and footprints of the nadir-looking Radar Altimeter and the full-swath SAR of the AMI. The wave-mode SAR was switched on briefly every 200 km along the satellite track, producing a 5 × 10 km SAR imagette (top right). (From Lehner et al., 2000; DLR) Bottom right: Example of the standard SAR wave mode product delivered by ESA. (Brooker, 1995) the satellite was in sight of a ground station, to which the SAR data could then be transmitted directly, and a global SAR wave mode, designed with a periodic on/off operation cycle. The SAR wave mode was activated only for a short period every 200 km along the orbit, producing 5 × 10 km rectangular ‘imagettes’ (see Fig. 1, top right). (In Envisat, which succeeded ERS‑1/ERS‑2, the separation between imagettes was reduced to 100 km). The wave mode data were sufficiently reduced relative to the full-swath data that they could be stored aboard the satellite until the data could be transmitted to a ground station in sight of the satellite. Although small, the imagettes, were nevertheless sufficiently large compared with the wavelengths of ocean waves to retrieve a meaningful statistical ocean wave spectrum. They were also sufficiently closely spaced to be compatible with the spatial resolution of current numerical global ocean wave prediction models, as applied, for example, in the daily global wave forecasts of the European Centre for Medium Range Weather Forecasts (ECMWF). The SAR wave mode imagettes were obtained at a mean incidence angle of 23°, within the low-incidence-angle region of the broader scatterometer swath (see Kerbaol et al., 1998, for a detailed description and analysis). Together with the Radar Altimeter wave heights measured directly below the satellite orbit and the winds retrieved from the scatterometer, these data provided a valuable and unique synergistic dataset that could be used both as inputs to numerical wave prediction models and as data for model validation.

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The second basic problem in the SAR imaging of ocean waves was the interpretation of the images. Seasat had clearly demonstrated that the SAR could see ocean waves (see Johannessen et al., this volume, for similar full- swath examples for ERS). But what was the relation between the waves seen in the image and the real waves in the ocean? Since the standard SAR image processing assumed that the Doppler was produced only by the satellite motion, the imaging theory could not be directly applied to random time- varying surfaces such as the sea surface. In particular, doubts had been expressed that SAR images could be used for quantitative measurements of ocean waves due to the orbital motions of the long waves that transport the short Bragg waves (in the 20 cm wavelength range) responsible for the microwave backscatter. The additional Doppler shift induced by the long- wave orbital velocities leads to a misplacement of the inferred position of the backscattered surface element, resulting in a distortion of the inferred wave spectrum. In one of the first analyses of these effects, Alpers & Rufenach (1979) pointed out that, depending on the wave steepness and propagation directions, some wave components would thereby be enhanced, while others would be smeared out. A first convincing demonstration that, despite these concerns, SAR wave image data could nevertheless provide useful information on wave spectra was provided by the analysis of the full wave dataset from a Seasat orbit (Fig. 2, left). The inferred centre of high winds that produced the swell agreed well with the wind field reconstructed from weather data (Fig. 2, right). Yet this successful reconstruction of the swell sources from SAR wave data glossed over some of difficulties emphasised by Alpers & Rufenach (1979). Since the Seasat SAR wave data were uncalibrated, only the wavelengths and propagation directions of the wave retrievals were used, not the wave amplitudes. Moreover, the swell fields were well dispersed, corresponding to

Figure 2. Left: Principal propagation directions of swell derived from the SAR wave images of the SEASAT orbit 757, acquired at 22:47 UTC on 18 August 1978, with the position of the inferred extratropical cyclone. (From Lehner, 1984) Right: Contour plot of surface wave heights (in metres) derived from a later reanalysis of the ECMWF data (ERA-40) at 18:00 UTC on 17 August 1978.

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narrow directional spectra. In this case, the different weightings assigned by the SAR to different propagation directions produced no significant distortions. Finally, the long swell waves were propagating in the near-range direction (i.e. orthogonal to the satellite flight direction) and had low steepness. In this case, the distortions induced by the wave orbital motions were small and, if anything, enhanced the imaging. ERS SAR data are now widely used to track waves emanating from storm regions, using both the SAR wave mode and full-swath data (see below). The SAR enables the routine reconstruction of the high-wind source region of the swell and decay rates of the swell as it propagates over many thousands of kilometres, using the basic spectral wave propagation relations first applied by Barber & Ursell (1948) to infer the locations and times of the distant storms that produced the swell. The SAR wave retrieval methods have meanwhile become more sophisticated, yielding well calibrated wave spectra. However, care must still be taken to allow for the distortions induced by the nonlinearity of the imaging mechanism, as discussed in the next section.

3. Retrieval of Ocean Wave Spectra from SAR Images

In anticipation of the difficulties of retrieving quantitative wave spectral data from SAR images not only in individual case studies, but also for the more general application of operational wave prediction, an extensive field programme, the Marine Remote Sensing Experiment (MARSEN), was carried out in 1979. The experiment, involving numerous research aircraft, ships, wave measurement stations, etc., from Europe and the US, was originally planned as a Seasat underflight project. However, when Seasat failed in 1978, it was rescheduled as a field programme in support of the planned European successor to Seasat. MARSEN yielded invaluable insights. Among other results, it led to an extensive multi-author collective analysis of the theory of the SAR imaging of ocean waves (Hasselmann et al., 1985). This provided the basis for the design of the ERS‑1 SAR wave mode instrument and the later retrieval of ocean wave spectra from the wave mode imagettes. Figure 3 illustrates the impact of velocity bunching on the SAR imaging of ocean waves. For a small azimuthal component of the wave steepness, the velocity bunching can be represented by a linear transfer function. This can be simply added to the two other linear transfer functions characterising the radar imaging of ocean waves: the hydrodynamic and tilt modulation of the short backscattering Bragg waves (in the centimetre range) by the imaged long ocean waves (in the range 10−1000 m). The hydrodynamic and tilt transfer functions apply equally to real and synthetic aperture radars and are reasonably well understood. However, the velocity bunching mechanism rapidly becomes nonlinear when the wave steepness increases and the displacement direction, which is in the satellite flight direction, is parallel to the propagation direction of the waves. When these two effects are superimposed, in particular for short steep waves travelling parallel to the flight (azimuthal) direction, the wave image can become completely smeared out (see lower part of Fig. 3). Thus, in order to retrieve quantitative ocean wave spectra from SAR image spectra, two developments were necessary: a theory of the nonlinear mapping of an ocean wave spectrum into a SAR image spectrum, and a method for inverting the nonlinear mapping relation to infer the wave spectrum from the measured SAR wave spectrum. Both problems were successfully solved, the first rigorously, the second more approximately, leading to different inversion schemes (Hasselmann & Hasselmann, 1991; Krogstad et al., 1994; Engen et al., 1994; Hasselmann et al., 1996; Mastenbroek & de Valk, 2000; Chapron et al., 2001; Schulz-Stellenfleth et al., 2005; Collard et al., 2009).

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Figure 3. Velocity bunching: Impact of wave orbital motions on the SAR-imaged position of a backscattering surface element of a wave field. The vertical axis refers to the average wave steepness. For a given steepness, the imaged position of an element on the sea surface is given by the intersection of the associated ray with the horizontal line corresponding to the given wave steepness. p and n indicate the orbital motions of water particles toward and away from the SAR platform. (Courtesy of X.-M. Li, after Hasselmann et al., 1985)

Figure 4. Contour plots of observed and computed 2D wavenumber spectra.

The original inversion method of Hasselmann & Hasselmann (1991) is illustrated in Fig. 4. It is based on a maximum likelihood matching of the first- guess (prior) information available from a wave model and the data provided by the SAR wave image spectrum. From the first-guess wave spectrum, the forward transform is applied first to compute the associated SAR wave image spectrum. This will generally differ from the observed SAR wave image spectrum. One then constructs a maximum likelihood resultant wave spectrum and an associated SAR wave image spectrum by linearly combining the two (generally inconsistent) sets of information. The final maximum likelihood (posterior) wave spectrum depends on the weights assigned to the two separate sets of information. It is then possible to effectively disregard the regions of the SAR image spectrum in which the wave components are strongly distorted by the velocity bunching mechanism, the unreliable information being replaced by the predicted first-guess spectrum. The method necessarily depends on the subjective assessment of the reliability of the first-guess wave spectrum, which is derived from integrated information on winds and waves over a larger area around the location of the retrieval, compared with the reliability of the SAR wave spectral image (which may well have problems apart from the distortions induced by velocity bunching). The first generation of retrieval algorithms yielded a wave spectrum corresponding to a frozen image of the sea surface. They were thus unable to resolve the 180° directional ambiguity of the wave propagation directions of

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a frozen image without additional information, such as from the first-guess wave model prediction. However, the required information was, in principle, available in the multi-look SAR data itself. Instead of simply averaging four successive looks (in order to reduce the clutter noise), it was possible to determine small changes between successive looks due to the propagation of the waves. Technically, the directional ambiguity was removed by computing not only the spectrum for the averaged looks, but also the complex cross- spectrum between successive looks (Vachon & Raney, 1991; Vachon & West; 1992). The analytic nonlinear ocean SAR transform based on cross-spectra and the corresponding inversion was published by Engen & Johnsen (1995). Following a proof-of-concept using reanalysed ERS‑2 data, cross-spectra were incorporated in the standard ESA SAR wave mode product for the following satellite Envisat (Johnsen et al., 1998; Lehner et al., 2000). The original maximum-likelihood method of Hasselmann & Hasselmann (1991) (Fig. 4) was generalised accordingly, such as by ECMWF (Abdalla et al., 2004) and in the PARSA retrieval algorithm of Schulz-Stellenfleth et al. (2005), to derive two-dimensional wave spectra free from directional ambiguities. The need to augment SAR wave image data using independent wave data from other sources – preferably from a state-of-the-art wave model used for operational global wave prediction – nevertheless remains an inherent limitation of SAR wave spectral data in all cases in which the azimuthal spectral cutoff due to velocity bunching cannot be neglected. Attempts to circumvent this limitation have used two approaches. The first approach is simply to limit the retrieval at the outset to the spectral range not seriously affected by the azimuthal cutoff. There exists a broad range of interesting problems, in particular regarding the propagation of swell (as illustrated in Fig. 2 and further examples discussed below), for which this approximation is quite acceptable. The second approach is to substitute the detailed spectral information provided by a state-of-the-art wave spectral model by auxiliary information obtained, for example, from scatterometer- derived surface winds or Radar Altimeter wave heights. This leads to the difficulty, however, that the missing azimuthal cutoff data are of a specific spectral structure, whereas the substitute data necessarily represent spectral integrals that are unable to resolve the missing spectral information. Thus, in summary, the various proposed SAR wave spectrum retrieval algorithms can be classified with respect to two characteristics. First, whether they are first-generation retrievals (‘1’), based only on the standard image variance spectrum, or second-generation retrievals (‘2’), using inter-look cross-spectral data. Second, whether they use state-of-the-art wave model predictions as first-guess input data (M), are limited to swell-type spectral data (S), in which the spectral components affected by the azimuthal cutoff can be neglected, or, finally, use auxiliary data instead of a first-guess model to augment the spectral region affected by the cutoff (A). An example of retrieval type 1S is the swell study of Lehner presented in Fig. 2; examples of the application of the other retrieval types will be discussed in subsequent sections. Parallel to these different development strands, all retrieval algorithms have benefited from continuous technical improvements. These include improved formulations of the imaging transfer functions, special retrieval algorithms for selected integrated spectral properties, analysis of image pixel statistics, and investigations of the signal-to-noise and image contrast problems associated with the inter-look cross-spectral analysis method used to remove the directional ambiguity (Schulz-Stellenfleth et al., 2005).

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4. Comparison of Retrieved SAR Wave Spectra with Operational Wave Model Predictions A comprehensive comparison of three years of ocean wave image spectra retrieved from ERS‑1 SAR with the wave spectra predicted by the ECMWF operational global wave model was undertaken by Heimbach et al. (1998); see Fig. 5. This provided the first quantitative assessment of the quality of the ECMWF wave forecasts over a continuous period, covering all seasons and on a global scale. A similar study on a regional scale, using the Norwegian operational wave prediction model, was undertaken by Breivik et al. (1998). In both studies a type 1M retrieval algorithm was applied. The availability of a first-guess wave field was important for the retrieval of the wind sea spectrum (Fig. 5, left panels), but not critical for the swell components (right panels), which consisted largely of well dispersed wave systems, in which the velocity bunching could be linearised. The retrieval algorithm was upgraded in this analysis by subdividing the wave spectrum into a number of separate wave systems, which were then inverted individually (Hasselmann et al., 1996). In a complementary study, Bauer & Heimbach (1999) compared SAR-retrieved significant wave heights (using the same retrieval algorithm) against those obtained independently from the altimeters on ERS‑1 and Topex/Poseidon. As in

Figure 5. Seasonally and ocean-basin averaged polar diagrams of wave heights and directions (defined analogously to the standard wind vector polar diagrams of operational meteorology). Left: Wind sea spectra. Right: swell. Solid lines: ERS‑1 SAR wave mode data; dashed lines: ECMWF-WAM model predictions. (From Heimbach et al., 1998)

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other applications of type 1M retrieval algorithms using first-guess wave spectra from a wave model, the directional ambiguity presented no serious limitation, as this could be resolved by the first-guess data from the wave model. The overall agreement between the SAR-retrieved and predicted wave data as well as with independent wave measurements was clearly very encouraging. At this stage, no attempt had been made to assimilate the retrieved SAR wave data in the wave model. Thus, the investigation was a test of the mutual consistency of the wave model prediction, driven by winds produced by the standard ECMWF weather analysis, and the wave spectra retrieved from the SAR. Despite the good overall agreement, systematic seasonally and regionally dependent deviations between the model and retrieved polar wave height diagrams are clearly visible. At that time, a tendency was revealed for the ECMWF model to predict too high wind seas and too low swell, indicating that both the wind input and the swell dissipation source terms in the wave model needed to be modified. This has led to successive improvements in the wave model. With the later model updates and the assimilation of SAR wave spectral data and Radar Altimeter wave heights, the ECMWF operational wave prediction model has now reached a level of maturity (see Figs 6–9) that the current

Figure 6. Global comparison of inverted Envisat/ASAR Level 1b data and WAM model results for significant wave heights, 2009 (Abdalla et al., 2010). (Peter Janssen, ECMWF)

Figure 7. Global comparison of inverted Envisat/ASAR Level 1b data and WAM model results for mean wave period, 2009 (Abdalla et al., 2010). (Peter Janssen, ECMWF)

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Figure 8. Global comparison of inverted Envisat/ASAR Level 1b data and WAM model results for mean directional spread, 2009 (Abdalla et al., 2010). (Peter Janssen, ECMWF)

Figure 9. Impact of various assimilation setups (ERS‑2 RA and SAR) on the bias and rms error of a one-dimensional wave spectrum, verified against in-situ buoy observations, 1−29 May 2001. (Abdalla et al., 2004)

operational SAR wave data assimilation scheme has only a minor impact on the skill of the prediction (Fig. 9). However, this level of maturity could not have been reached without the two-dimensional wave spectra provided as verification data by the global SAR wave mode system. The above comparisons of retrieved and predicted wave spectra were still based on a first-generation type 1M retrieval algorithm, in which the directional ambiguity of the SAR image spectra had not been removed. Figures 10 and 11 show similar comparisons using the type 2M PARSA retrieval algorithm. Figure 10 illustrates that in this case it is meaningful to compare not only the directional spread (as in Fig. 8), but also the mean direction itself. The impact of combining model and retrieved fields in the simultaneous determination of both the retrieved and predicted wave spectra is illustrated in Fig. 11, which shows comparisons of retrieved and predicted wave heights for two models,

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Figure 10. Comparisons of the PARSA mean frequency (left) and mean wave direction (right) with those of the ECMWF reanalysis wave model. (Li et al., 2010)

Figure 11. Comparisons of the PARSA significant wave heights with the results of the ECMWF reanalysis wave model (left) and the DWD forecast wave model (right). (Li et al., 2010)

ECMWF and the German Weather Service (DWD), both of which are based on the same basic physics. The examples differ in two respects, however. First, as input first-guess spectra, the same predicted ECMWF spectra were used in both cases. Thus in the DWD case, the input data came from another model. Second, in the ECMWF prediction, in contrast with the DWD prediction, earlier SAR spectra had been

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assimilated in initialising the wave spectrum for the subsequent wave model integration. Thus, it is not surprising that the deviations between the predicted and retrieved wave spectra are almost 50% smaller for the ECMWF model than for the DWD model. Attention must clearly be given to the relative weights assigned to the predicted first-guess spectrum and the SAR image spectrum both in the maximum likelihood retrieval algorithm and in the assimilation scheme when comparing predictions and observations. We return later to the important question of the advantages and disadvantages of combining prediction, retrieval and assimilation in a single operation, but first we present results in which no first-guess input wave spectra are used.

5.AR S Retrievals of Swell

In addition to the retrieval of the full wave spectrum, valuable information on the dynamics of ocean waves can be obtained from investigations of swell waves emanating from high-wind source regions. These are particularly suitable for global monitoring with SAR, as they propagate over large oceanic distances. Furthermore, if not too near the source of origin, they normally have sufficiently small steepness that the nonlinearities induced by velocity bunching can be neglected. Thus retrieval algorithms of type 1S or 2S can be used, independent of first-guess model predictions. Following the early Seasat example of Lehner (1984; Fig. 2), Holt et al. (1998) reported similar results for ERS‑1, in which swell waves emanating from an intense storm were tracked over a period of several days. Comparisons of SAR and buoy data indicated that SAR-derived peak wavelength and direction measurements could be used reliably to predict arrival times and propagation direction over large distances. A classic field experiment conducted by Snodgrass et al. (1966), who studied swell propagation along a great circle, and its attenuation during its crossing of the Pacific, was repeated by Heimbach & Hasselmann (2000). This time the authors used ERS‑1 SAR retrievals over a 10-day period (Fig. 12, top right panel), together with spectra from ECMWF’s WAM model collocated in time and space (top left panel) to assess the simulated swell properties. The retrieved and simulated spectra were traced back to their region of generation by a major storm off New Zealand (Fig. 12, bottom panels). A detailed comparison of the travel times of simulated and retrieved swells (in terms of their implied group velocities) revealed an underestimation of ECMWF’s wind speed used to drive the wave model, a result that was confirmed independently by scatterometer data. After 2002, the availability of operational complex SAR wave mode spectra from the Advanced SAR (ASAR) on Envisat stimulated the wider use of type 2S wave spectral retrieval algorithms, in which the directional ambiguity had been removed and the nonlinearities of velocity bunching (beyond the linear response approximation) were simply ignored. Since a first-guess wave spectrum, as in a type 2M algorithm, was now no longer needed to remove the directional ambiguity, but only to supply the information lost through the azimuthal cutoff by velocity bunching, the latter was simply ignored by restricting the application to spectra, such as swell, in which these effects were small. Extensive comparisons of buoy data and the Envisat/ASAR wave mode Level 2 products (based on a mixed type 2S–2A retrieval algorithm; see Johnsen et al., 2006) were also carried out by Li & Holt (2009) using the UK operational wave model to interpolate between the locations and times of SAR and buoy measurements (Fig. 13). Figure 14 shows the comparison of model and Envisat/ASAR wave mode Level 2 products wave height at the Christmas Island (CI) buoy site in different spectral bins, i.e. wave periods. The agreement was satisfactory except for a median range of typical wind sea wave periods (10−16 s), in which the SAR retrieved wave height in this case was 25% higher than the buoy data.

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Figure 12. Simulated swell (top left) and SAR wave mode (SWM) retrieved swell (top right) propagating across the Pacific over a 10-day period. In the bottom panels, both simulated and observed swells are traced back to their regions of generation. (From Heimbach & Hasselmann, 2000)

Similar comparisons and cross-validations of the operational wave model against the ASAR wave mode Level 2 products have also been carried out by Météo-France. The Météo-France spectral retrieval is based on a partitioning and optimal interpolation algorithm applied to individual wave systems. The assimilation of one year (September 2005 to August 2006) of ASAR Level 2 wave products in the wave model WAM cycle 4 (Aouf et al., 2008) reduced significantly the rms error of the significant wave height. The impact of the assimilation is strongest in the tropics, where the sea is dominated by swell, and the weakest component of the wave model. Compared with the assimilation of Radar Altimeter wave heights, the impact of the assimilation of ASAR Level 2 wave spectra is most pronounced for longer-range forecasts from 12 to 60 h (Fig. 15). Figure 16 shows a further example of the application of a type 2S retrieval algorithm, in this case to investigate the decay properties of long swells with periods of 13–18 s (Arduin et al., 2009). These wave components were and still are the largest source of error in global numerical wave models. The spectra from individual imagettes are sufficiently accurate to infer the dissipation rates of swell systems. Moreover, the large-scale consistency of the swell fields can be exploited to reach even more accurate estimates of characteristic wave parameters, by averaging wave properties in space and time. Ardhuin et al. (2009) found that the dissipation rates of swells were surprisingly nonlinear, with a stronger dissipation of steeper swells, and no clear dependence on the wave period or wind speeds (Fig. 16). This nonlinearity is consistent with the estimated strong dissipation rates for very steep swells derived by Högström et al. (2009).

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Figure 13. ASAR-derived versus buoy swell partition heights after bias correction, for the years 2004−8. The solid line joins the median values from SAR observations in each 0.1 m class of buoy-measured heights. (From Collard et al., 2009)

Figure 14. Comparison of model and Envisat ASAR wave heights at the CI buoy site in four spectral bins. (From Li & Holt, 2009)

Building on this observation, Ardhuin et al. (2010) used an analogy with the bottom boundary layer to parameterise nonlinear swell dissipation. The parameterisation is now used operationally by Météo-France and the Argentine Navy, and is being implemented by NOAA’s National Weather Service and the National Centers for Environmental Prediction (NCEP) in the United States (Tolman et al., 2011).

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Figure 15. Variation in the assimilation index over the forecast period compared with Jason-1 wave heights. The red line indicates the assimilation of Radar Altimeter RA-2 wave heights only, while the green line refers to the assimilation of both RA-2 and ASAR Level 2 wave spectra. The assimilation index is the reduction of the rms error relative to the reference run without assimilation. (From Aouf et al., 2008)

Figure 16. Swell dissipation for 22 swell events in the Pacific: estimated linear attenuation coefficient as a function of the swell significant slope (ratio of the swell significant wave height and peak wavelength, s = 4 Hs/L), taken 4000 km from the storm centre, for a variety of peak swell periods (colours). (Ardhuin et al., 2009)

6. Globally Integrated versus Local Retrieval Algorithms It is undisputed that second-generation retrievals, which use the complex information of the image cross-spectra to remove the directional propagation ambiguity, are inherently superior to first-generation retrievals using only SAR image variance spectra. However, this advantage is not pronounced in the combinations 1M and 2M, in which the first-guess input wave spectra from a state-of-the-art wave model can provide the necessary directional information (see, for example, Fig. 9, based on a type 1M retrieval algorithm). The main advantage of second-generation retrievals comes to bear either in retrievals of type 2S, applied to swell-type spectra in which the azimuthal cutoff due to velocity bunching is insignificant (Figs 13−16), or in retrievals of type 1A and 2A, where the information lost through the azimuthal cutoff is

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replaced by auxiliary data from sources other than a wave model. But even in retrievals of type 1S or 2S, the directional ambiguity is often not a serious problem, if the propagation directions (as in the examples presented above) can be clearly inferred from the overall geometry. The main issue in the choice of retrieval algorithm therefore arises when the azimuthal cutoff becomes important, namely for steep, high-wind seas. The question is whether it is best in this case to use a first-guess wave spectrum provided by a state-of-the-art wave model (type 1M and 2M retrievals), or to try to substitute for the missing azimuthal cutoff information using other data (type 1A and 2A retrievals), such as from a radar altimeter, a scatterometer (although this instrument was no longer available on Envisat) or from wave buoys. The answer depends on whether the goal is to provide the best possible estimate of the wave spectrum, or to improve the wave model. If the goal is to provide the best possible estimates of the wave spectrum for operational predictions or hindcasting studies, the answer is that one should clearly attempt to make optimal use of all available data in a global data assimilation scheme, including not only state-of-the-art wave models, but also state-of-the-art operational weather forecasting models, and all forms of observational data, including from satellites and conventional observing systems. As an example, Figs 17 and 18 (Li et al., 2010) show the impact of assimilating first-guess model data on the prediction of a complex sea state in an intense storm region. It is clear that without a reasonably realistic first- guess input, it would have been impossible to derive the complex spectra shown on the right in Fig. 18 from the ESA WVW wave mode products shown in the middle column, which exhibit a pronounced azimuthal cutoff. But it is equally evident that the first guess for this complex storm system exhibits systematic errors that could not have been corrected without the information from the SAR. Thus a retrieval of the wave spectrum from the complex SAR image spectrum requires simultaneous information from both the SAR and the wave model. On the other hand, if the goal is to test and improve the model, one should clearly strive to use independent data that have not been used in the model prediction. Unfortunately, this is feasible for SAR wave data only if one restricts the investigation, as in the examples of Figs 13 and 16 above, to type 1S and 2S retrievals, in which the nonlinear azimuthal cutoff region is excluded. In the case of strong wind sea spectra, as in the example just shown, the important spectral information lost beyond the azimuthal cutoff cannot be provided by model-independent, spectrally integrated observations. For this reason, most of the various proposed type 1A and 2A retrieval algorithms − e.g. Mastenbroek & de Valk (2000), CWAVE (Schulz-Stellenfleth et al., 2007), CWAVE_ENV (Li et al., 2011) − were not developed to test and improve state-of-the-art wave models, but rather simply to retrieve characteristic wave parameters. Although these retrieval algorithms cannot therefore compete with type 1M and 2M state-of-the-art retrieval algorithms for detailed spectral wave measurements, or with type 1S and 2S algorithms for testing and improving state-of-the-art wave models, they nevertheless represent a valuable complement to these more sophisticated retrieval algorithms in providing generally accessible, simple tools for useful exploratory studies. As an example, Fig. 19 shows the validation of the operational wave model of the German Weather Service (DWD) against a combination of Envisat RA-2 data and ASAR-retrieved wave heights using the CWAVE_ENV algorithm. In the long term, the SAR wave mode data could be seen as but one component of a highly complex satellite. The satellite, in turn, is only conceived as one component of an integrated Earth observation system, consisting of several satellites and an extensive ground-based conventional observing system. To make full use of this extensive global dataset, we need to develop sophisticated integrated models that also treat Earth as a complex coupled

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Figure 17. Locations of predicted and observed wave spectra at stations A, B and C within and south of an intense cyclone in the Pacific on 12 April 2009. (Li et al., 2010)

Figure 18. ECMWF predicted spectra (left), ESA SAR wave mode spectra (WVW) (centre) and PARSA retrieved spectra (right) for the three stations A (top), B (middle) and C (bottom) shown in Fig. 17. (Li et al., 2010)

system. In particular, the ocean wave field should be treated as an integrated component of the global weather system. The long-term goal should therefore be to assimilate the SAR wave data together with all other relevant sources of data (radar altimeter wave heights and winds, standard meteorological data, ocean buoy data, etc.) within a state-of-the-art integrated global atmospheric and ocean wave forecasting system. Within this general framework, a future ERS−Envisat−Sentinel integrated assimilation system should be extended to include also ocean currents (see section 8).

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Figure 19. Validation of the DWD forecast wave model using simultaneous wave measurements of the Envisat/ASAR and RA-2. Centre: The forecast wave field at 0:00 UTC on 20 January 2007 (background) and significant wave heights (SWH) retrieved from the ASAR wave mode data using the CWAVE_ENV algorithm (right track), and the collocated RA-2 SWH data (left track), both acquired between 23:39 and 23:51 UTC on 19 January 2007. The right and left panels show the model predicted SWH compared with the ASAR and RA-2 retrievals, respectively. (Courtesy of X.-M. Li)

In summary, the 20 years of SAR wave mode data provided by ERS‑1 (1991−2000), ERS‑2 (1995–2011) and Envisat (2002−2012) have had a major impact on ocean wave research and operational numerical wave prediction. The satellites produced invaluable data on the two-dimensional ocean wave spectrum that no other instrument was able to provide at a comparable level of spectral resolution and global, continuous coverage. The dataset has been indispensable for improving and validating the global wave model WAM (WAMDI, 1988), which is now applied in over 200 research and operational forecasting centres worldwide, as well as for improving similar wave prediction models operated by the French, UK and US weather services. Implemented together with the radar altimeter wave heights in the data assimilation mode, the wave mode SAR data have significantly improved, and can be expected to further improve, the quality of wave prediction.

7. Ongoing and Future Research

Apart from their invaluable contribution to the determination of global ocean wave spectra, SAR wave mode data also have other applications that are the subject of current and planned future research. These include investigations of extreme sea states in intense storms, studies of individual exceptional wave states, such as freak (monster) waves, and the extension of numerical wave models in the general context of Earth system modelling, including the improved representation of wave–current interactions, air–sea fluxes, and second-order spectral quantities related to infra-gravity and microseism generation.

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7.1 Extreme Sea States

Both tropical cyclones, which are associated with the most intense winds and air–sea fluxes, and extratropical cyclones, which produce most of the highest sea states, are difficult to observe directly due to sampling and signal saturation issues. Yet, these storms define the design criteria for most ocean engineered structures and coastal defences. Satellites provide a unique opportunity for overcoming these sampling problems. For instance, the largest significant wave height recorded by a satellite altimeter reached 20.1 m when storm Quirin crossed the North Atlantic in February 2011. A joint analysis of the remotely sensed winds and waves, including Envisat/ASAR data, confirmed the existence of very long waves, with peak periods reaching 25 s, supported by in situ buoy measurements and the analysis of seismic noise at land-based stations (Hanafin et al., 2012). To understand the dynamics of such extreme wave systems, it is important to observe both the conditions within the high-wind region itself and the long swell components emanating from the storm, which allow conclusions to be drawn on the processes within the storm itself (see examples presented in Figs 14–16). Data for both regimes can be provided by the global SAR wave mode observations. A careful combination with seismic noise data should yield further insights into the evolution and statistics of these extreme events (Grevemeyer, 2000; Aster et al., 2008; Ebeling & Stein 2011). The evolution of the wave spectrum within an intense storm region is governed by four processes: wave propagation, the energy transfer due to resonant nonlinear wave–wave interactions, wind generation and dissipation by white capping and turbulence (Komen et al., 1996; Janssen, 2008). Of these, the first two are completely understood theoretically from first principles and are free from empirical constants, while the last two are represented in wave models by empirical expressions tuned against extensive observed wave growth data. The most important process for the structure of the wave spectrum is the nonlinear energy transfer, which redistributes the energy within the spectrum through resonant interactions between wave quadruplets and is responsible for the formation of a sharp spectral peak that gradually shifts to lower frequencies (longer wavelengths) in a growing wind sea. Unfortunately, although the nonlinear energy transfer is well understood theoretically, and has been computed numerically in a number of case studies, the exact transfer expression is represented by a three-dimensional that is impossible to compute exactly for each frequency, direction and spacetime point of a numerical wave model. Most models therefore use a strongly simplified expression, the Discrete Interaction Approximation (DIA) based on a single dominant wave quadruplet (Hasselmann et al., 1985) that reproduces the principle properties of a growing wind sea in a uniform wind field, but is known to exhibit serious deficiencies under more complex wind conditions. With the advances in computing power since the DIA was first introduced, a number of improved versions of DIA including more basic interaction quadruplets have now been proposed (e.g. van Vledder, 2001; Tolman, 2004, 2009, 2011; Ueno & Kohno, 2004; Tamura et al., 2008, 2010). The data derivable from the ESA SAR wave mode using type 2M retrieval algorithms provide excellent new opportunities for testing the impact of these suggested improvements, in combination with the assumed wind input dissipation source functions, under realistic extreme wind conditions (as illustrated by the example in Figs 17 and 18). Nonlinear interactions, wind action and dissipation processes can still be important a few storm diameters outside an active generating area. They lead to a redistribution of the wave energy leaving the storm (Snodgrass et al., 1966), which can then be detected in the swell propagating away from the storm over very large distances. The swell systems can be readily observed with the SAR using simpler type 2S retrieval algorithms. Apart from avoiding

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the complexities of 2M retrievals associated with the admixture of predicted and observed data, these measurements have the advantage that the swell systems can be tracked over much larger areas of the ocean. As well as reconstructing the location and time of the storm, the measurements can then provide valuable indirect information on the processes active within and in the vicinity of the storm area. Preliminary investigations (Delpey et al., 2010) have found, for example, that swell can be detected with the SAR wave mode propagating away from the storm in directions perpendicular to the principal swell propagation direction. It is not yet clear whether this is controlled by the variability of the wind fields in a storm, by the variable directional spectral shapes in the storm, or by directional spreading through nonlinear wave–wave interactions. A combination of storm-related swell data using type 2S SAR retrievals and type 2M retrievals for the storm area itself could yield important new insights into the nature of extreme sea states in severe storms.

7.2 Freak Waves

The occurrence of extreme individual waves, called freak or monster waves, has become a major concern because of the rapid increase in global trade and the concurrent growth in global shipping using ever larger container vessels. It has been estimated that in recent years an average of about one super-container ship carrying many thousands of containers has been lost per month. Although the cause of the loss is often unknown, it is suspected that in many cases it was due to an unexpected encounter with an individual monster wave (Fig. 20). Although long attributed to the narrative lore of seamen, the reality of monster waves has now been clearly established through many well documented reports. While they are obviously difficult to interpret directly due to the intrinsic nonlinear imaging property of the SAR instrument, unusual wave formations have also been identified in SAR wave mode images, opening up the possibility of obtaining information on the probability of the occurrence of freak waves using the global observing capability of the SAR wave mode. However, to relate SAR data to freak waves, three major problems must first be resolved: What is the physical structure of a freak wave? Given the physical structure of a freak wave, how is this very nonlinear, rapidly moving individual surface feature mapped into a SAR image? How can one invert the mapping to recover the original freak wave from the SAR image? Regarding the first question, the simplest hypothesis is that a freak wave can be described to first order as the straightforward linear superposition of

Figure 20. Monster wave encountered by the NOAA ship Discoverer in the Bering Sea, 1979. (Commander Richard Behn, NOAA Corps)

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the individual wave components of a random sea state. Freak waves would then obey Gaussian statistics. However, the available evidence suggests that observed monster waves can be considerably higher than can be reasonably expected from Gaussian statistics, and must thus therefore represent a basically nonlinear phenomenon. Various nonlinear models based on the Benjamin−Feir wave−wave interaction instability mechanism (Janssen, 2003) or on wave–current interactions have been proposed, but it is likely that a number of different nonlinear interaction configurations can produce freak waves (Dysthe et al., 2008). Given the structure of a freak wave, including the orbital velocity field and its interaction with the short backscattering Bragg waves superimposed on the large-scale orbital velocity field, the mapping into a SAR image is in principle well defined (although for ERS SAR wave mode incident angles of only 23°, the standard Bragg backscattering theory must presumably be augmented for steep range-travelling waves by a specular reflection model). However, the computation of the resultant image is computationally demanding. Unfortunately, the statistical closure methods applied in the fully nonlinear forward-mapping algorithm of Hasselmann & Hasselmann (1991) and Krogstad (1992) for wave spectra cannot be simply carried over to the case of individual wave representations. However, an approximation using a linearised Fourier transform relation for individual images, in which a filter is applied to the azimuthal cutoff region (corresponding to a type 2S algorithm, but applied now to the image itself, not to the image spectrum), has been proposed by Schulz-Stellenfleth & Lehner (2004). Figure 21 shows an example of the sea surface elevation field (B) derived from the calibrated ERS‑2 wave mode data (A) using this algorithm. The algorithm has been applied to two years (September 1998 to November 2000) of reprocessed ERS‑2 SAR wave mode data to derive a global map of maximum (SAR estimated) single wave heights (Fig. 22). Although not applicable for quantitative assessments of individual wave heights, due to the neglect of the nonlinear distortions induced by the SAR imaging system, this provides nevertheless a useful tool for arriving at first estimates of the statistics of exceptional individual wave occurrences as seen by the SAR. Another application of SAR-derived wave images is the investigation of wave groupiness (Borge et al., 2004; Niedermeier et al., 2005). Figure 23 shows an example of pronounced wave groupiness occurring in the vicinity of the Ekofisk oil platform in the North Sea. The third and final challenge is to invert the freak wave → SAR image mapping relation in order to recover the properties of the original monster wave from the SAR freak wave image. This will depend, of course, on the freak wave model and will presumably involve some form of parametric inversion method. Without progress in addressing the first two problems, however, predictions on advances on this question must remain speculative. Even after a complete three-stage freak wave model → SAR freak wave image → SAR image inversion scheme has been developed, one then faces the further difficulty that the probability of simultaneously observing a freak wave both in reality and in a SAR wave image in order to validate the model is for all practical purposes negligible. However, there is an interesting exception: the monster waves that occur at a few locations famous for their exceptional surfing conditions (Fig. 24). These huge surf waves are produced by refraction at subsurface reefs and are generated under well defined, reproducible and measurable wave conditions. Although representing a special type of monster wave, they may provide an ideal dataset for testing theories of the mapping of monster waves into SAR images, as well as for developing the associated inversion algorithms. Under less extreme conditions, coastal wave transformation due to complex bathymetry changes can also be used to interpret and better understand SAR observations involving wave focusing and refraction effects (e.g. Collard et al., 2005).

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Figure 21. SAR ocean wave retrieval in the spatial domain. A: A 5 × 10 km normalised ERS‑2 SAR wave mode imagette acquired at 48.45°S, 10.33°E on 27 August 1996. B: The retrieved sea surface elevation field. (From Schulz-Stellenfleth & Lehner, 2004)

Figure 22. Estimated maximum individual wave heights inferred from ERS‑2 SAR wave mode data acquired between September 1998 and November 2000. (Koenig et al., 2007; DLR)

In summary, most SAR wave observations have been motivated by the desire to improve our understanding and prediction of wave spectra, with less attention paid to the imaging of the individual wave fields themselves. However, the representation of individual waves can yield important information not only on freak waves, but also on other phenomena, such as wave grouping, the transformation of waves in the surf zone, and non-Bragg microwave returns from fast scatterers or reflectors, for example, from white caps or steep waves.

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Figure 23. Analysis of wave groupiness in the area of the Ekofisk oil platform in the North Sea. (From Borge et al. 2004)

Figure 24. A monster wave off Hawaii. Such surf waves pose no immediate danger to shipping, but are ideal for testing theories of the mapping of monster waves into SAR wave images, together with their associated inversion algorithms. (EPA)

7.3 Wave–Current Interactions

Ocean waves propagating through strong, variable currents can undergo dramatic changes in wave height and steepness. In a steady shear current, wave rays are bent, the bending being proportional, in the approximation of geometric optics, to the ratio of the vertical component of the current vorticity to the wave group velocity. Thus refraction effects become important whenever surface gravity waves propagate through major ocean currents exhibiting high gradients. Particularly critical are frontal zones, which can lead to wave

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reflections, trapping and waveguide propagation of the trapped wave within the current. The resulting wave field can become very complex, with wave systems following different paths, producing crossing seas and caustics. The resultant strong spatial variations in wave energy can seriously endanger navigation. The effects have been observed in numerous SAR images; Liu et al. (1994), for example, carried out an empirical analysis of wave ray refraction patterns inferred from an ERS‑1 SAR image over an ocean eddy. Most of these data have not been supported by synchronous measurements of surface currents. Today, however, routine ocean circulation models are available that assimilate multi-satellite altimeter sea surface height data, thereby better characterising the mesoscale upper-ocean dynamics and enabling the incorporation of wave refraction effects in wave models. Wave–current investigations using SAR observations can furthermore be enriched through an emerging analysis capability to infer ocean surface velocities directly from the Doppler information of the SAR data themselves (Chapron et al., 2005; Johannessen et al., 2008). As an example, Figs 25 and 26 illustrate the deflection of waves by large surface current gradients in the Agulhas current off the coast of South Africa, detected using the Envisat wide-swath SAR data. The currents (Fig. 25) were determined using the SAR Doppler information, while the wave spectra (Fig. 26) were obtained using a 1S retrieval algorithm (2S retrieval algorithms free from directional ambiguities were not directly available for the full-swath SAR data, as ESA provides the required complex image spectra only for the SAR wave mode imagette data). The incoming swell propagation rays are seen to be strongly deflected by the large current gradients, producing crossing-swell systems from an initially uniform incoming swell system within only 200 km. The problem of wave–current interactions is closely related to that of wave–wave interactions, and both can be invoked to explain the generation of spacetime caustics and anomalous wave systems, including freak waves as an extreme case. In general, waves are scattered and amplified when propagating in an inhomogeneous random medium, which can represent a nonuniform, slowly varying current, as in the above example, or the low-frequency, low- wavenumber random currents that are continuously generated in a random sea state by quadratic difference interactions between different wave components. From this perspective, the Benjamin–Feir instability invoked to explain the generation of extreme waves (Janssen, 2003) can be placed within the general theory of unstable wave–wave interactions (Hasselmann, 1967) in a form analogous to the wave–current interaction mechanism considered above. Finally, quadratic wave–wave interactions between wave components of nearly the same frequency propagating in opposite directions generate microseisms (Longuet-Higgins, 1950; Hasselmann, 1963). Precisely measured microseismic records can be used to determine the arrival times of different swell components. Using geometric optics, the source can be determined with high accuracy by triangulation from several seismic stations (Fig. 27; Husson et al., 2012). Waves propagating in opposite directions with the same frequency occur only infrequently in the open ocean and are usually an indication of an unusual storm event, such as an intense tropical or mid-latitude cyclone, or possibly effects of multiple wave–current refractions. As recently demonstrated (Ardhuin et al., 2010), numerical model of microseism generation by random ocean waves, including ocean wave reflections, are becoming increasingly realistic. Yet, the development of microseism observations into a reliable early warning system and/or the full exploitation of available 50 years of seismic data archives still require more detailed knowledge of the directional properties of ocean wave spectra than is presently achievable using the energy transfer parameterisations (as discussed above) applied in current state-of-the- art wave prediction models. The extensive global directional wave information made available by the two-dimensional ERS−Envisat wave mode data should again prove invaluable in investigating this attractive option.

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Figure 25. Envisat/ASAR-derived currents and wave observations in the Agulhas current (left), confirmed by the altimetry-derived surface flow field (right). A southwesterly swell system interacts first with the Agulhas current, flowing toward the west, and subsequently with the Agulhas return current, flowing and meandering towards the east. The principal swell propagation directions (left) are displayed as background below the sketched ASAR Doppler-derived current pattern (full curves). (CLS)

Figure 26. Wave spectra for three imagettes corresponding to the three locations shown in Fig. 25: A (left), B (middle) and C (right). The 300 m swell system is strongly influenced by the large surface current gradients, and the two swell patterns of comparable energy eventually cross. (CLS)

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Figure 27. Location of an expected storm source from independent SAR wave mode products and seismic noise measurements. For each seismic station, a 1000-km wide blue disc has been plotted with a radius equal to the distance to the storm source, as estimated from the differential arrival time of the swell at the seismic station. The different discs intersect southwest of New Zealand, in agreement with the storm location given by the SAR (the red dot is the location at which backward-propagated SAR measurements converge). (From Husson et al., 2012)

8. Conclusions and Outlook

The sequence of ESA satellites ERS‑1, ERS‑2 and Envisat deployed over the last 20 years has had an immeasurable impact on oceanography, meteorology and climate research. We have focused in this chapter on the dynamics and prediction of ocean waves, in particular on the information provided by the ERS SAR wave mode component of the Active Microwave Instrument (AMI). However, since ocean waves are generated by the wind, interact with ocean currents, generate microseisms, affect coastal and offshore structures, and influence shipping and navigation, an improved understanding and prediction of ocean waves is important for many other aspects of the Earth system. Thus, the ERS SAR wave mode, designed to provide global measurements of the two- dimensional ocean wave spectrum, must be seen as an integral component of a global Earth observation scheme, consisting not only of the full complement of ERS instruments, but also of many other satellites providing data on ocean currents, surface winds and other oceanic and atmospheric variables, including also the extensive system of conventional in situ measurements. The impressive series of publications based on the ERS SAR wave mode instrument (of which only a selected subset has been cited here) illustrates the complexity of the problems that need to be addressed in order to exploit the full potential of the instrument. The ERS satellite was conceived in the late 1970s, when the oceanic community had just developed realistic numerical models of the two-dimensional ocean wave spectrum for operational global

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wave prediction and eagerly welcomed the opportunity to feed these models with appropriate wave spectral data from a globally observing satellite. However, although Seasat had already demonstrated that a SAR could image ocean waves, it soon became apparent that the imaging process was highly complex. For a quantitative interpretation of the ERS SAR wave images, detailed models of the electromagnetic return from a random, moving surface, including, in particular, the interactions between short backscattering Bragg waves in the decimetre range and the imaged long waves in the 100– 1000 m range were needed. These were soon developed. Methods were then needed to invert the mapping relation, and retrieve the two-dimensional wave spectra from the satellite image spectra. This task was also achieved. However, because the general forward-mapping relation was nonlinear, the inverse mapping relation involved the unavoidable loss of some information: wavenumbers beyond an azimuthal high wavenumber cutoff could not be resolved. Thus, for a satisfactory general retrieval algorithm, the missing spectral information needed to be supplied from another source. In practice, the only available source was a two-dimensional spectral wave model, as all conventional measurement systems (other than wave buoys at a few isolated locations) are unable to resolve the specific spectral bands of interest. Thus retrieval algorithms in the general nonlinear case necessarily require an input first-guess wave spectrum from a wave model. This results, however, in an unavoidable intertwining of three separate aspects of the ERS SAR wave mode data: the retrieval of ocean wave spectra from the SAR data, the prediction of ocean wave spectra, and the assimilation of observational data in wave prediction models. The last two aspects imply that the application of the SAR wave mode data becomes coupled also to the problem of weather prediction, involving in addition the measurement and assimilation of atmospheric data. The separability of the three problems is feasible only for swell systems, outside an active wave generation region, in which the nonlinearities of the SAR imaging mechanism become negligible. The application of SAR wave mode data in this intertwined mode, together with improvements in weather prediction and associated atmospheric data assimilation methods, has led to the continuous improvement of global wave prediction models to a level at which, for most state-of-the-art wave models today, no systematic deviations between observed and modelled wave spectra, beyond the expected variability of observations, is found in the overall statistics of the comparisons of model predictions and SAR observations. However, significant deviations can still be found in individual cases, such as in intense cyclonic storms. Apart from the difficulties of an accurate prediction of wind fields in such extreme situations, it is well known from theoretical studies that the numerical approximations of the important source function describing the nonlinear energy transfer exhibit serious shortcomings in most wave models in complex wind seas. The SAR wave mode data provide an excellent opportunity for revisiting this problem and significantly improving wave models in these important cases. A related problem in which the application of SAR wave mode data could lead to significant progress is in the occurrence of individual extreme wave formations known as freak or monster waves. Although monster waves occur infrequently, they represent a serious danger for the rapidly expanding global shipping industry, causing significant losses or damage to container ships and supercarrier vessels. Global SAR wave mode observations of individual extreme waves could provide a valuable statistical database for determining the frequency and conditions of occurrence of these poorly understood wave formations. However, further work is needed on the nonlinear SAR imaging of individual waves before a quantitative determination of the statistics of real freak waves rather than freak SAR wave images can be provided. Another process that leads to the formation of extreme wave conditions is the interaction between waves and currents, an important problem that is also

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ripe for a more detailed exploration using SAR wave mode (and in this case also full-swath SAR data). Strong current gradients can result in a focusing of waves, producing abnormal wave conditions. Previously regarded as largely unpredictable, the simultaneous measurements of both wave spectra and currents using SAR wave imaging, the SAR Doppler for current measurements, and currents computed from radar altimeter sea level measurements, now enable predictions of the anomalous wave spectra produced by wave–current interactions. Finally, interesting data on the location of unusual systems of ocean waves travelling in opposite directions can be derived from triangulations of microseism measurements at different stations. Microseisms of twice the wave frequency are generated by quadratic interactions between ocean waves travelling in opposite directions in the open ocean, caused by moving cyclonic storm systems, or in the vicinity of coasts through wave reflections at steep shorelines. Important indirect information on processes within a storm can also be derived from observations of swell propagating away from a distant storm. These and other examples presented in this chapter demonstrate that despite the considerable advances in our understanding and prediction of ocean waves achieved through the ERS SAR wave mode data, many interesting problems that could be successfully addressed using this instrument still await a solution. It is therefore very encouraging that there are plans to continue the ERS-1/ ERS-2–Envisat satellite system with the launch in 2013 of Sentinel-1, the first in a series of next-generation satellites, as an important component of the Global Monitoring for Environment and Security (GMES) system. Sentinel-1 will again carry a SAR wave mode instrument with enhanced capabilities (20 × 20 km imagettes every 100 km). The continuation of this satellite series is important not only for the upgrading of the present ocean observing system and the resolution of the open problems mentioned, but also for the creation of a long- term time series in support of climate change monitoring.

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→→Satellite Oceanography from the ERS Synthetic Aperture Radar and Radar Altimeter: → A Brief Review

SAR & Altimeter

Satellite Oceanography from the→ erS Synthetic Aperture→ radar and→ radar Altimeter:→ a→ Brief Review

J.A. Johannessen1, B. Chapron2, W. Alpers3, F. Collard2, P. Cipollini4, A. Liu5, J. Horstmann6, J.C.B. da Silva7, M. Portabella8, I.S. Robinson9, B. Holt10, C. Wackerman11, P. Vachon12

1 Nansen Environmental and Remote Sensing Center, Bergen, Norway 2 L’Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Plouzane, France 3 Center for Marine and Atmospheric Sciences, Institute of Oceanography, University of Hamburg, Hamburg, Germany 4 National Oceanography Centre, Southampton, UK 5 Department of Ocean Science and Engineering Zhejiang University, Hangzhou, China 6 NATO Centre for Maritime Research and Experimentation (CMRE), Viale San Bartolomeo, La Spezia, Italy 7 Department of Geosciences, Environment and Spatial Planning, University of Porto, Porto, Portugal. 8 Institut de Ciències del Mar (ICM−CSIC), Pg. Marítim de la Barceloneta, Barcelona, Spain 9 Ocean & Earth Science, University of Southampton, National Oceanography Centre, Southampton, UK. 10 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA 11 General Dynamics Advanced Information Systems, Ypsilanti, MI, USA 12 Radar Applications and Space Technologies, Defence R&D Canada, Ottawa, Canada

1. Introduction

The cloud-independent, all-weather, day-and-night active microwave Synthetic Aperture Radar (SAR) and Radar Altimeter (RA) operated continuously on ERS‑1 from July 1991 to March 2000 and on ERS‑2 from October 1995 to July 2011. In addition, the two ERS satellites also operated the active microwave C-band scatterometer (see Stoffelen & Wagner, this volume). The side-looking C-band (5.6 GHz) SAR was operated to obtain two-dimensional spectra of ocean surface waves to reveal ultra-high-resolution surface wind field patterns, to identify numerous oceanic features, as well as to detect oil spills, ship and detailed sea ice conditions and deformation. The nadir-pointing Ku-band (13.8 GHz) RA aimed to measure sea surface height, significant wave heights and wind speed, and thanks to the choice of orbit, it also covered high latitudes for sea ice signature observations including freeboard heights. Table 1 summarises the applications of the ERS‑1/ERS‑2 SAR and RA data in the field of oceanography. The SAR surface wave retrievals, in particular the SAR wave mode capabilities and the altimeter mean sea level retrievals, are addressed by Hasselmann et al. and Andersen et al., respectively, elsewhere in this volume. A comprehensive overview of the key early scientific findings based on ERS data is presented in the special issue the Journal of Geophysical Research: Oceans on advances in oceanography and sea ice research using ERS observations (Johannessen et al., 1998).

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2. Surface Currents from SAR

The ERS SAR operated in both Wave Mode (WM) and Image Mode (IM) and achieved a high spatial resolution of 26 m in range (across-track) and between 6 m and 30 m in azimuth (along-track). As a first approximation, the side- looking ERS SAR formed an image from the Bragg resonance between the transmitted vertically (V) polarised radar pulses and the short gravity ocean waves (Wright, 1978; Valenzuela, 1978; Ulaby et al. 1982; Elachi, 1987). These short gravity waves, including small-scale breakers, form in response to the local wind stress and are further modulated by the orbital motion of the longer waves, by intense wave breaking at longer waves, and by the surface current and/or Sea Surface Temperature (SST) gradients. The ERS SAR provided unique observations of near-surface winds, waves and surface current features.

2.1 Current Shear, Convergence/Divergence

Surface density fronts are ubiquitous features of the upper ocean dynamics. The surface signatures of meandering fronts and eddies can be quite spectacular and have been regularly observed and documented in SAR images. Wave–current interactions, the suppression of short wind waves by natural films, and the varying wind fields resulting from atmospheric boundary layer changes across an oceanic temperature front all contribute to the radar image manifestations of such mesoscale features. A clear example of the frontal features associated with the mesoscale circulation patterns expressed in SAR images is shown in Fig. 1. In Fig. 1 the changes in surface roughness detected by the SAR apparently correspond to changes in the near-coincident SST field imaged by NOAA’s Advanced Very-High-Resolution Radiometer (AVHRR; Johannessen et al., 1996a). In the latter, the structure of the SST field with the curvilinear temperature fronts represents a mesoscale variability of 10−50 km, characteristic of the unstable Norwegian coastal current (Johannessen et al., 1989). The configuration and orientation of the frontal features contained in the radar image are in good qualitative agreement with those in the AVHRR image, where the sharp radar cross-section gradients are strongly collocated with the SST gradients. Other comparable image expressions using near-coincident ERS‑1/ERS‑2 SAR and AVHRR acquisitions of surface frontal features were documented by Nilsson & Tildesley (1995) for the East Australian current, and by Mitnik et al. (2005) for the southwestern Okhotsk Sea. In order to interpret the observed surface roughness changes, numerous further investigations have built on the SAR internal wave imaging capability (see section 2.3).

Table 1. Overview of the oceanographic applications of ERS‑1/ERS‑2 SAR and RA data.

SAR (image mode) RA Surface currents Mesoscale fronts and eddies via Surface geostrophic currents along fronts divergence−convergence and eddies Internal waves Planetary waves Shallow-water bathymetry Range Doppler surface velocity Oil spill, natural film and ship detection Distinct contrast in radar cross-section Wind field High-resolution wind speed and in some Along-track wind speed cases also wind direction Atmospheric boundary layer processes Sea level Mean Sea Surface Heights relative to a reference surface (ellipsoid or geoid)

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Figure 1. Left: The Norwegian coastal current front in a NOAA/AVHRR 1-km resolution image of the SST field (white, 14°C; dark blue, 12°C; land is masked with green) acquired at 14:20 UTC on 3 October 1992. Right: ERS SAR 100-m resolution image of the surface roughness field acquired at 21:35 UTC (after Johannessen et al., 1996a). Both images cover approximately the same 100 × 300 km region. (© ESA/NERSC)

Kudryavtsev et al. (2005) and Johannessen et al. (2005) used a forward-imaging simulation model to demonstrate that geostrophic surface current convergence and divergence along the frontal boundaries are the principal source of strong modulation of the intermediate short-scale surface roughness that, in turn, contributes to the image contrast. Mesoscale to submesoscale eddies were often examined with ERS‑1/ERS‑2 SAR in conjunction with SST data. These eddies are most often detected in coastal zones and are largely cyclonic in a rotational sense (DiGiacomo & Holt, 2001; Johannessen et al., 1996; Nilsson & Tildesley, 1995; Liu et al., 1994). They are identified in the SAR images by a series of dark (reduced backscatter), narrow, curvilinear bands arranged within the eddy into a spiral appearance, as shown in Fig. 2. The dark bands are composed of surfactants brought together by the complicated flow patterns within the eddies. They appear within a limited range of wind speeds (3−7 m s−1) and are likely short lived. There are probably several forms of generation mechanisms related to current shear and frontal convergence, and are often associated with coastal topography. The importance of meso- to submesoscale eddies is related

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Figure 2. ERS‑1 SAR image of the Santa Monica Bay/Santa Monica Basin, California, at 1834 UTC on 19 December 1994. The labels A−F indicate the locations of submesoscale cyclonic eddies. (From DiGiacomo & Holt, 2001)

to biological pumping of nutrients into the upper ocean and their role as significant components of dissipation within the ocean circulation energy regime.

2.2 Internal Waves

Internal Solitary Waves (ISWs) are thought to play an important role in global mixing in the upper layers of the ocean (Shroyer et al., 2010), and they contribute considerably to shelf dynamics in terms of vertical heat fluxes and ocean shelf mass transport. Internal tides and ISWs have also been shown to interact with upwelling jets, driving intensified turbulence and dissipation (Avicola et al., 2007; Kurapov et al., 2010). From SAR imaging principles, imaging of internal waves in a stratified ocean is then connected with the interaction between the internal wave-induced surface current fields and wind-driven ocean surface waves (Alpers, 1985; Apel et al., 1985). In a surface current convergence zone, small gravity waves are squeezed together and the surface is roughened compared with the surroundings. In contrast, in the surface current divergence zone, small gravity waves are dispersed. The signature of a rough surface in a SAR image is correspondingly bright, while a smooth surface appears dark. The interaction of internal waves, the induced surface current with wind waves, and the resultant SAR image intensity variations are illustrated schematically in Fig. 3. When the thickness of the mixed layer is thinner than that of the bottom layer (e.g. H1 < H2), only depression waves can originate. In contrast, for cases when the bottom layer H1 > H2, only elevation waves evolve, as illustrated in Fig. 3 (Liu, 1998). In deep water with a shallow mixed layer, the nonlinear internal solitons are therefore typically depression waves. The depression internal wave packet can be identified as a bright band immediately followed by a dark band. Conversely, an elevation internal wave packet can

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Figure 3. Schematic diagram of interaction of internal waves, induced surface current with wind waves, and the resultant SAR image intensity variations. (After Liu, 1998)

be identified as a dark band immediately followed by a bright band. Thus the observation frequency of internal waves depends on the induced surface current strain rate, sea state, wind speed and environmental conditions. In the open ocean with deep water and a shallow mixed layer, the nonlinear internal solitons are generally mode-one depression waves, while elevation waves can exist in shallower water near to the shore in cases when the bottom layer becomes thinner than the mixed layer. The Strait of Gibraltar and the Strait of Messina were among the first sites of internal wave generation that was investigated using ERS‑1/ERS‑2 SAR images (Alpers et al., 2008), although the first SAR image showing the generation of internal waves in the Strait of Messina was captured in 1978 by the SAR onboard the American Seasat satellite (Alpers & Salusti, 1983). In the Strait of Gibraltar the SAR images reveal that internal waves only propagate eastwards into the Mediterranean Sea (Fig. 4, left). This is due to the complex topography of the sill region with which the predominantly semi-diurnal tide interacts as shown in model calculations (Brandt et al., 1996). In contrast, the ERS‑1/ERS‑2 SAR images acquired over the Strait of Messina show internal waves propagating northwards as well as southwards from the sill, although internal waves propagating northwards are much less frequent (Brandt et al., 1997, 1999). In most ocean regions, the internal wave generation processes are repeated with tidal periodicity. With a large enough dataset covering different phases of the complete (semi-diurnal) tidal cycle, it is therefore possible to investigate and reconstruct the propagation ‘history’ of waves generated by a given source (e.g. Moum et al., 2003; New & da Silva, 2002; Klimak et al., 2006; da Silva & Helfrich, 2008; da Silva et al., 2007, 2009, 2011). The largest internal waves (peak to trough values of more than 60 m) are encountered in the Andaman Sea (Fig. 4, right), between the Malay Peninsula and the Andaman and Nicobar Islands. These internal waves are associated with strong roughness bands (ripplings) that can even be observed from ships. This was described in a book published in 1861 by Maury (quoted in Osborne & Burch, 1980): ‘The ripplings are seen in calm weather approaching from a distance and in the night their noise is heard over considerable time before they arrive. They beat against the sides of the ship with great violence, and pass on, the spray sometimes coming on deck.’

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Figure 4. Left: ERS‑1 SAR image acquired on 20 January 1994 revealing the surface manifestation of an internal soliton close to its generation area in the centre of the Strait of Gibraltar and an internal wave packet east of the strait generated during the previous tidal cycle. Right: ERS‑2 SAR strip acquired over the Andaman Sea on 11 February 1997 showing a surface manifestation of internal solitary waves. The land area visible at the bottom is the island of Sumatra. The image area is 100 × 300 km. The smaller circular pattern in the upper third of the SAR image is the surface signature of a secondary internal wave generated by the interaction of the first internal solitary wave with the Dreadnought underwater bank, 241 m deep. (Vlasenko & Alpers, 2005)

2.3 Shallow Water Bathymetry

Knowledge of sea bottom bathymetry and corresponding depths is of vital importance to ship routing in shallow water, for fisheries and for offshore operations. While traditional bathymetric surveys are time and cost-consuming, the imaging capabilities with SAR have demonstrated a promising capability to retrieve bathymetric maps over large areas at relatively low cost, provided the average water depth does not exceed 20–30 m. Under such conditions, bottom topographic features may then become visible on radar images of the sea surface if there is a current (usually tidal current) flowing over these features. They cause local perturbations to the surface current, which in turn modulates the sea surface roughness, hence making it visible in the SAR images. Figure 5 shows a typical ERS SAR image acquired over a shallow tidal area around Shanghai off the east coast of China. A theory relating the modulation patterns visible in such SAR images to the underwater bottom topography was developed by Alpers & Hennings (1984) using weak hydrodynamic interaction theory. Although this theory describes the modulation qualitatively quite well, it cannot be used to invert SAR image intensity modulation patterns into bathymetric maps. When used in combination with acoustic sounding data the SAR images are of great value in generating bathymetric maps. Off the coast of the Netherlands large-scale topographic structures are regularly imaged under favourable moderate (3–7 m s−1) wind conditions. These, in turn, are used to validate and initiate morphodynamic models that are developed to forecast changes in bottom shapes linked with sediment transport, river deposition and coastal erosion. The results show that by using the SAR information in combination

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Figure 5. ERS‑1 SAR image of the Xinchuan Gang shoals off the east coast of China north of Shanghai. Parts of the area become dry during ebb tide (dark areas off the coast).

with ship-based depth soundings the number of ship tracks can be reduced by a factor of 3–5, while still maintaining an rms error of the depth map of less than 30 cm (Hasselmann et al., 1997), which is the accuracy required by the Dutch Ministry of Transport and Public Works (Rijkswaterstaat). This conclusion has been corroborated by the results of depth assessments in other areas. It demonstrates that the use of SAR imaging can lead to substantial reductions in the cost of traditional sounding campaigns. Capitalising on these findings, the Dutch company ARGOSS has developed a system, called the Bathymetry Assessment System (BAS), which is commercially available (see Alpers et al., 2004).

2.4 Wave–Current Interactions

Ocean waves propagating through strong, variable currents can undergo dramatic changes in wave height and steepness. In a steady shear current, wave rays are bent, the bending being proportional, in the approximation of geometric optics, to the ratio of the vertical component of the current vorticity to the wave group velocity (see Fig. 6). For more information on wave-current interactions, see section 7.3 in the chapter by Hasselmann, et al., page 190.

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Figure 6. ERS‑2 SAR image of the Agulhas current from 12 February 2010. Significant wave heights are given by the colour code. Local swell systems can be detected over this image and multiple wave rays derived. The white arrows correspond to the length (100–500 m) and propagation direction of the main detected swell system over a given subimage tile. Using the local direction angles, the reconstructed propagation rays (red and green) have been reconstructed and clearly exhibit refraction effects, with increasing bending corresponding to stronger shear current areas. (CLS)

2.5 Range Doppler Surface Velocity

The SAR-received signal in amplitude and phase can be used to infer the first- order moment of the Doppler distribution, called the Doppler centroid shift. The reflected signals over the illuminated ocean scene in the cross-range direction systematically exhibit different phase shifts compared to the nominal expected phase history associated with a motion-free scene such as obtained over land. To first order, this mean shift is directly connected to an overall radar line-of- sight mean velocity relating to the dominant roughness scales contributing to the backscatter power. These scales are predominantly affected by the wind speed and direction, orbital wave motion and underlying surface current. After correctly taking into account the local platform orbital geometry and kinematics as well as Earth’s rotation, the measured mean Doppler correlates well with the local ocean surface motion as well as roughness (see Fig. 7). To achieve this with adequate accuracy, a very precise orbit is needed as well as instrument pointing. The wind direction in this case is offshore (e.g. the well-known Tramontane wind in the Gulf of Lion, France), and the Doppler centroid shift over the ocean is consequently positive, implying that the scatterers move towards the antenna as expected for this descending track. Based on this innovative method, presented in the Marine SAR Analysis and Interpretation System (MARSAIS) toolkit using ERS SAR data (Chapron et al., 2005), a new Envisat wide-swath single-look complex product provided the opportunity to derive potentially highly accurate surface current estimates over the entire ASAR 400 km swath in the coastal zones as well as open oceans.

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Figure 7. Left: Complex ERS SAR image of the Gulf of Lion, France. Centre: Doppler centroid anomaly from the complex image. Right: Doppler anomaly along the blue profile in the centre image. A transition between land and ocean, with positive Doppler over the ocean is clearly depicted.

Figure 8. Mean radial surface speed derived from ascending Envisat/ASAR wide-swath mode acquisitions for the Gulf Stream (left) and the Agulhas current (right). The colour bars indicate the velocity (in m s−1) revealing opposite flow directions for the two current systems. (CLS)

An example of this is shown in Fig. 8, using an average of more than 600 ascending acquisitions of the Gulf Stream and the Agulhas current (Johannessen et al., 2008). As noted, the maximum mean radial Doppler velocity reaches about 1.5 m s−1, with a mean current width of the order of about 100 km. In the Agulhas current a distinct sign shift in the range Doppler velocities, followed by the gradual manifestation of moderately weaker (~0.75 m s−1) and broader eastward velocities, are manifested in the semi-permanent meandering of the Agulhas return current (Boebel et al., 2003). These robust Doppler velocity signatures are striking. Although the Doppler velocity is not a direct surface current measurement, it inevitably demonstrates that the use of Doppler observations, first discovered in the ERS SAR data, can help to derive new and innovative estimates of mesoscale dynamics associated with intense surface current regimes. For more details, visit http://soprano.cls.fr

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Figure 9. Spatial distribution of oil pollution in the waters around Singapore derived from ERS SAR images. (CRISP, Singapore)

3. Oil Spills, Natural Films and Ship Detection

Imaging radars are very suitable for monitoring oil spills notably in regions of intense shipping traffic and in offshore areas of extensive oil and gas production (Pedersen et al., 1996). Oil and natural slicks (film) dampen the capillary short gravity waves. In turn, the radar cross-section is lowered and the imaged area appears dark. For most users, the basic problem is how to distinguish between man-made oil spills from natural films and other ‘lookalike’ features. For the practical determination of slick type (whether natural or man-made) and origin using a single-frequency and polarisation SAR, empirical and conceptual and interactive algorithms, employing wind history and drift models, appear to be most useful (Espedal et al., 1998). On 19 August 1996 the beaches of Singapore were found polluted with oil. Using an ERS SAR image, investigators were able to find the ship responsible, and the ship owner ended up in court, where he was convicted and fined US$0.6 million. Building on repeat acquisitions, ERS SAR images have also been used to monitor areas with intense shipping traffic and frequent oil spills, either accidental or deliberate. The map in Fig. 9 shows the number of oil slicks per 10 000 m2 derived from quick-look ERS SAR images downloaded and processed by the Singapore receiving station.

4. High-Resolution Wind Field and Boundary Layer→ Retrievals The/ERS ERS‑1 ‑2 SARs used the same radar frequency (C-band) and polarisation (VV) as the scatterometers on these platforms. Hence, it was rapidly postulated that the CMOD Geophysical Model Function (GMF) relationship between wind and radar backscatter could also be used for the SAR. Given the very high spatial resolution of the SAR instruments, this opened the way for the retrieval of very high-resolution wind (~1 km) fields. However, with just one SAR antenna the near-surface wind field cannot be simply partitioned into the contributions from the wind speed and the direction. Several algorithms developed during the late 1990s tackled this deficiency with satisfactory results, at least under good imaging conditions (Etling & Brown, 1993; Johannessen et al., 1996b; Chapron et al., 1995; Wackerman et al., 1996; Vachon & Dobson, 1996; Fetterer

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et al., 1998; Kerbaol et al., 1998; Lehner et al. 1998; Wackerman et al., 2003; Horstmann et al., 2000, 2004; Horstmann & Koch, 2005).

4.1d Win Retrieval

4.1.1d Win Direction Retrieval

Besides signals of coastal shadowing effects, the SAR wind direction retrievals are based on the possibility of detecting linear features, such as those associated with wind streaks and atmospheric boundary layer rolls, at scales larger than typically 800 m. The determination of the orientation of these linear features can be carried out either in the spectral domain or in the spatial domain and resulted in high-resolution wind direction estimations ranging from 1 km to 10 km. The wind directions, being perpendicular to the orientation of the linear features, are then determined with an accuracy of about 15−20° but with a directional ambiguity of 180° (Monaldo et al., 2003; Drobinski & Foster, 2003; Koch, 2004; Sikora et al., 2006). This ambiguity can, however, be removed if wind shadowing is manifested in the image, which is often the case in the vicinity of coastal regions. In other cases where there are no manifestations of linear features in the image, the wind directional information can be derived from weather maps, atmospheric models or in situ measurements. A very nice documentation of the wind retrieval capabilities of SAR is presented in Beal & Pichel (2000). Attempts have also been made to explore in more detail the along-azimuth complex SAR signals (Engen & Johnsen, 1995). The proposed methodology is based on the processing of single-look-complex SAR data to estimate the decorrelation time and phase from the cross-spectra between different SAR looks. Such a methodology is closely related to the Doppler centroid method described above, and can be used to help to remove the wind ambiguity more directly as demonstrated by Mouche et al. (2012).

4.1.2d Win Speed Retrieval

Given the wind direction, the wind speed retrieval then relies on the global empirically based model function developed for the ERS scatterometer for fully developed seas (Stoffelen et al., 1997a,b; Quilfen et al., 1998). This model function relates the Normalised Radar Cross Section (NRCS) to the local near- surface wind speed u, wind direction versus antenna look direction Φ, and the incidence angle θ. The general form of the function is:

v()i v0 -A^ ihu^1 + B^ u,ihcos U + C^ u,ihcos 2Uh (1) where A, B, C and ν are coefficients that generally depend on radar frequency and polarisation. In the case of the models available for the C-band VV polarisation, these coefficients were determined empirically by evaluating ERS‑1 scatterometer data and independent wind fields from models (e.g. Numerical Weather Prediction (NWP) models, ECMWF) or wind-measuring buoys (Monaldo et al., 2001) (for more details, see Stoffelen & Wagner, this volume). In coastal regions this model function should be applied with care as the wind can be fetch-limited, implying that the seas may not be fully developed. In an attempt to avoid the inherent inversion problem for SAR-based wind vector retrievals, Portabella et al. (2002) proposed combining the SAR- observed σ0 and/or wind streaks, with other background information from NWP models and/or buoy data. Assuming Gaussian errors, and that both the observation and the background errors are uncorrelated, the general Bayesian

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approach (Lorenc, 1986) was then applied to SAR observations. This method allows retrievals of an optimum wind vector from the best combination of SAR and other wind information, and has shown promising results, although deficiencies remain in cases of abrupt shifts in wind direction associated with wind fronts (Portabella et al., 2002). The methodology is now being extended to include the SAR Doppler information (Mouche et al., 2012) in order to help constrain front locations.

4.1.3 Extreme Wind Events

Under extreme environmental conditions, the air–sea exchanges are thought to represent the main kinetic energy sources necessary to maintain the deep ocean stratification and to strengthen ocean stirring processes. Thanks to satellite-based observations, including SAR, extreme weather events such as tropical cyclones, hurricanes and explosive mid-latitude storms and polar lows are now more often reported and analysed (Katsaros et al., 2000; Quilfen et al., 1998). These measurements are critical for short-term forecasting, but also offer a better means to question the role of extreme conditions for the state of the ocean at local and global scales and how they affect ocean circulation and ocean heat transport. In particular, the imaging SAR-based observations provide high-resolution to delineate the fine-scale structures associated with extreme wind conditions. The early observations of these extreme wind fields associated with hurricanes and polar lows spurred the systematic collection of all possible acquisitions during the hurricane seasons.

4.2 Expressions of Atmospheric Boundary Layer Phenomena

Mesoscale and microscale atmospheric phenomena in the Marine Atmospheric Boundary Layer (MABL) become visible on SAR images because they are associated with variations in the wind stress at the sea surface (Beal et al., 1997; Elfouhaily, 1997; Thompson & Beal, 2000). The wind stress depends on wind speed and direction at the sea surface, possibly on the relative surface motion, but also strongly on the stability of the air–sea interface, which is a function of the temperature difference between the water and air (Keller et al., 1989), and on the coverage of the sea surface with surface-active material that attenuates the short surface capillary gravity waves. Variations in the wind stress disturb the short surface waves and thus give rise to ‘imprints’ on the sea surface that become visible in a radar image as variations in the SAR image intensity. A collection of ERS SAR images acquired over the tropical and subtropical oceans showing imprints of atmospheric phenomena on the sea surface can be viewed at the ESA website earth.esa.int/applications/ERS‑SARtropical or www.ifm.zmaw.de/~ERS‑sar. Some of these MABL phenomena are discussed in the following sections.

4.2.1 Katabatic Winds

Katabatic winds are cold winds that blow down a sloping terrain (‘gravity flow’) and, at a coast, out over the adjacent sea surface. These winds are generated because during the evening and at night, air near the surface cools faster over land than over the sea, resulting in a downhill flow of cold air. A large number of ERS SAR images show sea surface signatures of katabatic wind fields in various coastal regions adjacent to mountainous areas. This is shown in Fig. 10 for the katabatic winds blowing down from 1800 m high mountains through a broad valley into the Gulf of Gioia Tauro in the Tyrrhenian Sea. The roughness

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Figure 10. ERS‑1 SAR image acquired at 21:14 UTC on 8 September 1992, over the Strait of Messina and adjacent sea areas. Over the Tyrrhenian Sea (northern section) the sea surface signatures of katabatic wind fields are visible. Note the mushroom-like pattern caused by katabatic winds blowing through a broad valley on the Calabrian coast onto the sea.

pattern has the form of a mushroom. The wind field has been simulated using a non-hydrostatic mesoscale atmospheric model and then compared with the wind field derived from the ERS‑1 SAR image using the C-band wind scatterometer model CMOD4 (Alpers et al., 1998). The comparison shows that the atmospheric model reproduces quite well the mushroom-like form of the wind field pattern, while the wind speed is somewhat lower than the one inferred from the SAR image.

4.2.2A tmospheric Gravity Waves and Boundary Layer Rolls

Atmospheric Gravity Waves (AGWs) exist in the presence of layered atmospheres, and occur as quasi-periodic waves, solitary waves or undulating bores. They are often generated behind mountain ranges, in which case they are called lee waves. In the steady state lee waves are stationary with respect to the terrain feature, but they are propagating relative to the mean air flow above the surface. Lee waves are very common in visible remote sensing imagery where they manifest themselves as wave-like cloud patterns. However, they are also manifested in SAR images since they are associated with varying near- surface wind speeds and thus varying short-scale surface roughness. Examples of SAR observations of atmospheric waves have been reported by Vachon et al. (1995), Alpers & Stilke (1996) and Li et al. (2004). Expressions of atmospheric gravity waves can usually be separated from expressions of atmospheric boundary layer rolls or wind streaks (Vachon & Dobson, 1996). The latter provide a reliable means for wind direction retrieval via determination of the orientation of the linear features (e.g. imprint of the rolls).

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Figure 11. ERS‑1 SAR image of atmospheric waves acquired on 30 May 1995 near the .

Figure 11 shows an ERS‑1 SAR image of atmospheric waves acquired on 30 May 1995 near the south of Kamchatka Peninsula in far eastern Russia. The mountain range along the peninsula is clearly visible. It looks like two atmospheric wave packets coming out of two sections (northwest and north−south) of the mountain range. However, the two wave packets are well connected with an indentation showing at the intersection area. Based on the study by Li et al. (2004), this wave pattern is most likely the upstream atmospheric waves generated by flow impeded by the mountain range on the Kamchatka Peninsula.

4.2.3 Coastal Wind Fronts

In coastal seas bordered by mountain ranges the sea surface signatures of wind fronts are often expressed in ERS SAR images. They can be caused by barrier jets, katabatic winds or by synoptic-scale winds impinging on a coastal mountain range causing recirculation of airflow, such as seen in the ERS SAR image off the east coast of Taiwan (Fig. 12, left) (Alpers et al., 2007). This distinct SAR image expression has been assessed in comparison with model calculations obtained by a high-resolution atmospheric (MM5) model, as seen in Fig. 12 (right). The observed fontal line is indeed suggested to be of atmospheric origin and seems to be generated by the collisions of the airflows moving in opposing directions: one is associated with a weak easterly synoptic- scale wind blowing against the high coastal mountain range on the east coast of Taiwan, and the other with a local offshore wind. At the convergence zone, air is forced to move upwards, which often gives rise to the formation of precipitating cloud bands parallel to the coast. This has been confirmed by the complementary

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Figure 12. Left: ERS‑2 SAR image acquired on 18 January 1999 showing a distinct wind front over the east coast of Taiwan. Right: Schematic plot of the airflow showing the recirculation of air causing the formation of the atmospheric frontal line parallel to the coast. The two vertical lines with the horizontal red arrows denote the offshore components of the wind vector at the shoreline and east of the convergence line.

Japanese Geostationary Meteorological Satellite, the American satellite and rain-rate maps from ground-based weather radars (Alpers et al., 2010).

5. Currents from RA

The Radar Altimeters on ERS‑1/ERS‑2 measured the transit time and backscatter power of the transmitted pulses. The transit time is proportional to the satellite’s altitude above the ocean and sea ice surfaces, provided that adequate corrections for time delays are applied. To ensure reliable atmospheric and ionospheric corrections of the RA signals and accurate knowledge of their position in space, Microwave Radiometer (MWR) and Precise Range and Range-Rate Equipment (PRARE) were operated simultaneously to measure the integrated atmospheric water vapour column and cloud liquid water content as well as particle density in the ionosphere. Over ocean surfaces the measured range is accurate to about 5 cm at an along-track resolution of about 5 km, with the largest contribution to the uncertainty arising from the sea state bias (Gaspar & Florens, 1998). Higher-range accuracy is then achieved by detailed analysis of the received signal resulting from averaging a large number of echoes at the expense of reduced spatial resolution. The magnitude and shape of the returned echoes also contain information about the characteristics of the reflecting surface, from which it is possible to retrieve geophysical parameters such as significant wave heights, wind speeds and the freeboard height and location of the edges of sea ice. Measurements of the sea surface height have been routinely obtained from satellite altimeter missions in the last 20 years. Today the annual mean sea surface (MSS) height derived from altimetry is known with millimeter accuracy (e.g. Cazenave et al., 2009) in the open ocean. As most changes in ocean surface currents (on timescales of a few days or more) result in a geostrophic balance, the sea surface height gradients derived from the ERS Radar Altimeters were employed to investigate temporal and spatial changes in the large basin-scale circulation patterns and mesoscale ocean dynamics. At seasonal and inter- annual time scales the RA also detects phenomena such as the westward- propagating Rossby waves and El Niño−Southern Oscillation that are manifest in the sea surface topography anomalies (see also Andersen et al., this volume).

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5.1 Mesoscale Fronts and Eddies

By measuring the departure of the sea surface height from its long-term mean level at that location, the Sea Surface Height Anomaly (SSHA) obtained from the ERS altimeters was used to detect the presence of frontal boundaries mesoscale eddies. From the along-track gradients of the SSHA the cross-track geostrophic velocity was then calculated at a spatial resolution of about 10 km. This estimates the time-varying part of the surface velocity field, relative to a mean circulation, except in a zonal band of 1–2° of the equator where the Coriolis parameter is zero and the geostrophic assumption does not apply. It is not possible to optimise the sampling of any single satellite mission to observe all oceanic processes and regions, in particular at the mesoscale of 10–100 km. Since 1992 there have been at least two and sometimes as many as four altimeters in orbit at the same time. When the orbits of these instruments are harmonised the along-track data from each of them can be interpolated to a common grid and thus achieve a much higher-quality product (Le Traon et al., 1998). For the combination of Topex/Poseidon and ERS, for example, the sea level mean mesoscale mapping error was reduced by a factor of 4 and the standard deviation reduced by a factor of 5 (Le Traon & Dibarboure, 1999) compared with those for Topex/Poseidon only. Gridded maps of SSHA data provide details of the streamlines of the geostrophic flow anomalies depicted along contours of constant height and directed with higher values of the SSHA to the right/left in the northern/ southern hemisphere, respectively. The time-variable part of the surface current field can thus be derived and used to study and monitor changes in the location and strength of current fronts and eddies. Such a time sequence of altimeter-derived SSHAs over several years yields valuable information about the relationships between eddies on different spatial scales and their dependence on the strength of surface currents as they vary over the seasons and years. By merging such altimeter mission data, the mean and eddy kinetic flow behaviours have also been characterised and investigated for the Gulf Stream and its extension into the North Atlantic, and for the Kuroshio, Malvinas and Agulhas currents (e.g. Le Traon et al., 1998; Ducet et al., 2000; Okkonen et al., 2001; Fu, 2007).

5.2 Westward Propagating Planetary Waves

In most regions the ocean surface height variability at the meso- to large scale (i.e. at wavelengths greater than a few tens of km) is dominated by features that propagate towards the west (a meridional, i.e. north−south propagation component, may be present, but it is normally smaller than the westward velocity). These features are planetary waves, also known as Rossby waves, and nonlinear, rotating eddies. Following the seminal paper by Chelton & Schlax (1996), many studies of large-scale westward propagation based on satellite altimetry in the 1990s have interpreted the observed features in terms of planetary waves (for a review, see Fu & Chelton, 2001). Although most of those papers were based on data from the Topex/Poseidon mission, it was nevertheless soon demonstrated that the ERS altimeters could resolve the waves similarly well (Cipollini et al., 1997a). Moreover, accurate infrared data from the ERS‑1/ERS‑2 Along-Track Scanning Radiometer (ATSR) instruments provided the first evidence of the presence of these features in the sea surface temperatures (Cipollini et al., 1997b), which were finally shown by Hill et al. (2000) to be nearly ubiquitous. The satellite observations allow estimations of the characteristics of propagating features, such as westward speed, amplitude and wavelength. It was immediately found that westward-propagating features travel faster than predicted by the classical (‘linear’) theory for Rossby waves. This finding

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in turn stimulated a number of efforts to extend the theoretical framework to more representative cases, for instance by including a mean flow (Killworth et al., 1997) and realistic bottom topography (Killworth & Blundell, 2004, 2005). Such theoretical advances have greatly reduced the discrepancies between predicted and observed speeds. Figure 13 (left) demonstrates how clear the signature of westward- propagating features is in the ERS RA dataset showing a longitude/time section (at 28°S, 50°− 80°E in the Indian Ocean) of Sea Surface Height Anomaly data from the Radar Altimeter on ERS‑2, covering a time span of more than eight years. The diagonal stripes in the plot (diagonal alignments of positive and negative anomalies) are the westward propagating features, i.e. eddies and/or Rossby waves. The wavenumber/frequency spectrum of the data (Fig. 13, right) shows a ridge of energy broadly aligned with the line at the propagation speed predicted by the extended theory for planetary waves (black dashed line), while it is clear that the dispersion curve from the old ‘linear’ theory (magenta curve) could not explain the observed spectrum.

Figure 13. Left: Longitude/time plot of SSHAs (in m) at 28°S in the Indian Ocean, from the ERS‑2 RA. Right: Wavenumber/ frequency spectrum of the plot on the left. The diagram has been flipped horizontally so that westward-propagating signals are in the bottom left quadrant. The magenta curve is the approximate (flat bottom and no background flow) dispersion relationship for planetary waves with a Rossby radius of deformation of 42.9 km, the typical value in the area (Chelton et al., 1998). The black dashed line corresponds to a speed of 4.7 cm s−1, which is the median speed from the extended theory of Killworth et al. (1997) over the area. (Data extracted from the RA Database System (RADS) archive: http://rads.tudelft.nl)

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The advent of optimally interpolated maps of SSHA from the merging of a 10-day orbital repeat mission (first Topex/Poseidon, and then Jason-1 and -2) with data from the ERS satellites (and then Envisat) finally made it possible to detect and analyse westward-propagating features with significantly increased spatial resolution (Chelton et al., 2007), and thus allowed the characterisation of these features at scales of the order of 100−500 km. In these higher-resolution data it is apparent that a large part of the variability at extratropical latitudes takes the form of westward-propagating, eddy-like features, nonlinear and with spatial scales close to the lower limit for planetary waves (Chelton et al., 2011). As a consequence, many of the smaller-scale features seen in Fig. 13 (left) can be interpreted as nonlinear eddies.

6. Summary and Outlook

Since 1991 the ERS active microwave sensors on ERS‑1/ERS‑2, notably the SAR and Radar Altimeters, and their successors, have clearly contributed to advances in the study, monitoring and understanding of the upper ocean. By 2011 these two sensors together had contributed to more than 1000 publications in peer-reviewed journals. Key products today include merged global ocean surface topography using the data available from various altimeter missions (www.aviso.oceanobs.com) and merging of data related to the sea state from various instruments and platforms (www.globwave.org) with particular efforts to provide consistent tracking of travelling swell systems and crossing sea statistical occurrences, as well as the emerging range Doppler velocity products at both Level 2 and Level 3 (http://soprano.cls.fr/L3/fireworks.html). Accordingly, analyses are now under way to characterise more precisely the variability in the global ocean at scales of tens to hundreds of kilometres. Also routinely assimilated by numerous forecasting systems, these efforts serve both research and applications such as those associated with marine services established under the global monitoring for environment and security (www.gmes.info) and presently routinely operated in MyOcean (www.myocean.eu.org). The ERS missions, extended with the Envisat mission, have therefore been invaluable in the preparations for the launch and routine availability of Sentinel‑1 SAR and Sentinel‑3 altimeter data with tentative launch in October 2013 (Sentinel‑1) and November 2014 (Sentinel‑3). Building on existing processing methods and retrieval algorithms developed for and applied to the ERS and Envisat SAR and altimeter data, combined with available in situ data and numerical simulations, it is anticipated that the Sentinel‑1 and Sentinel‑3 missions will further advance our capability to monitor, quantify and describe the high-resolution upper ocean dynamical state. The Sentinel missions will be able to capitalise on the valuable archive of satellite active microwave SAR and Radar Altimeter data acquired over 25 years, with global coverage, excellent quality and easy and user-friendly data access. The next generation of Sentinel active microwave data will stimulate new and more research, increase the range of applications and broaden and strengthen marine services with subsequent societal benefits. In particular, the next decade of regular SAR and altimeter observations should lead to:

—— advances in quantitative understanding of upper ocean dynamics at the meso- to sub-mesoscale, including frontal zone instabilities, eddy generation and decay, and the importance of convergence and divergence; —— regular high-resolution information on surface waves and surface currents, as well as their interactions, which is invaluable for oil spill monitoring as well as managing responses to emergencies and search and rescue operations; —— intensified studies and greater understanding of planetary waves and their role in the zonal transport of heat across ocean basins;

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—— strengthening the consistent use of SAR and altimeters in synergy with high- resolution optical sensors; and —— better scientific and operational use of merged high-resolution SAR imaging sensor data and nadir-profiling altimeters taking into account new approaches for measuring surface velocities and topography directly from satellites such as NASA’s Surface Water and Ocean Topography (SWOT) mission and ESA’s Wavemill instrument mission.

A cknowledgements

The advances in satellite oceanography based on the ERS Synthetic Aperture Radar and Radar Altimeter observations over the last 20 years have been possible thanks to the involvement and efforts of a large range of scientists, engineers and technicians with support from national and international funding agencies, as well as the steady supply of data and scientific support studies initiated by ESA as well as other space agencies.

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224 →→New Views of Earth using → ERS Satellite Altimetry

Altimeter

New Views of Earth using ERS Satellite Altimetry

O.B. Andersen1, P. Knudsen1, P. Berry2, R. Smith2, F. Rémy3, T. Flament3, S. Calmant3, P. Cipollini4, S. Laxon5, A. Shephard6, D. McAdoo7, R. Scharroo7, W. Smith7, D. Sandwell8, P. Schaeffer9, J. Bamber10, C.K. Shum11, Y. Yi11, J. Benveniste12

1 Danish (DTU Space), Copenhagen, Denmark 2 De Montfort University, Leicester, UK 3 Laboratoire d’Etudes en Géophysique et Océanographie Spatiales, Toulouse, France 4 National Oceanography Centre, Southampton, UK 5 University College London, UK 6 University of Leeds, UK 7 National Oceanic and Atmospheric Administration (NOAA), Washington, DC, USA 8 Scripps Institute, La Jolla, CA, USA 9 Collecte de Localisation Satellite (CLS), Toulouse, France 10 University of Bristol, UK 11 Ohio State University, Columbus, OH, USA 12 ESA Centre for Earth Observation (ESRIN), Frascati, Rome, Italy

1. Introduction

The first European Remote Sensing satellite, ERS‑1, launched in 1991, carried a comprehensive payload including an imaging Synthetic Aperture Radar (SAR) designed to map the cryosphere, a radar altimeter to measure the height of the ocean and other powerful instruments to measure ocean surface temperatures and winds at sea. Satellite altimetry works conceptually as follows. The satellite transmits a short pulse of microwave radiation with known power towards the sea surface, where it interacts with the surface and part of the signal returns to the altimeter, where the travel time is accurately measured. The determination of the height of the surface from the altimeter range measurement involves a number of corrections to account for the behaviour of the radar pulse through the atmosphere (Fu & Cazenave, 2001). In the case of altimetry over the ocean, corrections for the sea state and other geophysical signals are also applied. (Andersen & Scharroo, 2011) Some of the most spectacular results of the ERS‑1 mission, such as the ice sheet topography maps, the global marine gravity field or the global bathymetry map, were obtained when the satellite was operating in a number of ambitious mission phases:

—F— irst ice phase, from December 1991 to March 1992, optimised for Arctic ice experiments characterised by a 3-day repeat cycle with a ground track spacing of roughly 930 km at the equator. —F— irst multidisciplinary phase, from April 1992 to December 1993, with a 35‑day repeat cycle with a ground track spacing of around 80 km. —S— econd ice phase, from January 1994 to March 1994, with features identical to those of the first ice phase. —— The geodetic phase, from April 1994 to March 1995, in which the satellite performed interleaved 168-day repeat track cycles resulting in a final ground track spacing of 8 km. —S— econd multidisciplinary phase, from April 1995 until the end of the mission on 10 March 2000.

The ERS‑1 168-day interleaved geodetic phase mission was proposed and designed by the ERS‑1 international science team members, led by Guy

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Duchossois and Richard Francis. However, the geodetic phase was planned to last only until the launch of the ERS‑2 satellite in April 1995, giving only 1.5 cycles of 168-day repeat observations. The decision to continue the geodetic phase and to complete the second 168-day repeat, resulting in a ground- track spacing of 8 km, was not made until the end of 1994. Shortly before the December 1994 American Geophysical Union (AGU) meeting there was a launch failure at Arianespace. During the meeting Anny Cazenave, Dave Sandwell and Richard Francis drafted a letter to ESA, which was supported by many scientists at the meeting. In the letter they asked that, if the launch of ERS‑2 was delayed, then perhaps the second 168-day phase could be extended to completion before resuming the 35-day operations. This was finally approved by ESA and the scientific community had two complete 168-day cycles before ERS‑1 was returned to its multidisciplinary phase. With two cycles/phases of ERS‑1 Geodetic Mission (GM) data, the spatial density of these data now equalled that of the Geodetic Mission data from the older mission, which had been classified by the US Navy. This may have convinced the US Navy to release all the Geosat GM data in July 1995, providing a unique opportunity to cross-validate the two datasets and to create in particular global marine gravity fields of unprecedented accuracy, much higher than could have been achieved using just one satellite. ERS‑2 was launched in 1995 to replace the older ERS‑1, and was operated in a 35‑day repeat multidisciplinary phase throughout the mission. By the time of the launch of ERS‑2, the original ERS‑1 was still in very good health and went on to remain in operation until 1999, providing the first tandem mission altimetry phase valuable to construct climate profiles. ERS‑2 remained in operation until 2011 and was one of the longest Earth observation satellites missions ever flown.

2. Altimeter Data Improvement

Initially, the ERS‑1 altimeter provided sea surface height observations that were noisier than the data from the NASA/CNES Ocean Topography Experiment (Topex) satellite launched in 1992. There were several reasons for this, including the satellite’s Sun-synchronous orbit and higher inclination. Consequently, many post-launch improvements were made throughout the mission, which improved the data quality significantly, and the applicability of the ERS‑1 mission for climate studies.

2.1 ERs→retracking

Due to the unique nature and spatial coverage of the Geodetic Mission data, much effort was devoted to improving this important dataset. Maus (1998) and subsequent authors had shown that the noise in retracked range data can be reduced significantly by imposing the constraint that the Significant Wave Heights (SWHs) must be correlated from waveform to waveform along the track (Fig. 1). This greatly improved the quality of the ERS‑1 GM dataset. Another way to improve the value of the ERS‑1 GM dataset was achieved by increasing the amount of data that could be made available from the mission. This was done by using multiple tolerant retrackers adapted to local conditions (Fig. 2). This additional step allowed the recovery of an additional 6–10% more data for shallow water and polar regions. In particular, in polar regions, only a few echoes are ocean-like in shape and so most fail to be retracked by a standard Brown waveform model. Previously, these echoes had been rejected by the space agencies (i.e. ESA), but they could be recovered using more tolerant retrackers.

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Figure 1. Profiles of along-track sea surface slope for six repeat cycles crossing the South Pacific Ocean in a region of generally high SWHs. Top: Profiles derived from the onboard tracker available in the waveform data record (rms = 8.23 µrad). Middle: Profiles derived using a weighted least- squares 3-parameter retracking algorithm (rms = 6.45 µrad). Bottom: Profiles derived from a 1‑parameter retracking algorithm constrained by smoothing the rise-time and amplitude parameters (rms = 4.01 µrad). (Smith & Sandwell, 2005)

2.2 ERs→orbit and Range Correction Improvements

The radial orbit had long been a major source of error in ERS‑1 altimetry, which Figure 2. Altimetric height observations was made worse by having only satellite laser ranging for precise tracking and in ice-covered regions east of Greenland. having to rely on insufficiently accurate general-purpose gravity field models. Top: The number of data points that can However, it was realised that altimeter crossovers can be used very effectively be retrieved using standard ESA retracked as additional tracking data to laser ranging. The ERS‑1/ERS‑2 tandem mission 1 Hz data. Bottom: The amount of 1 Hz data even provided the unique possibility to determine simultaneously the orbits of that can be retrieved using more tolerant two similar satellites flying in the same orbit. Altimeter crossovers between the retrackers. (Andersen et al., 2009) two satellites then linked the two orbits in a common reference frame. Tailoring the Joint Gravity Model 3 (JGM-3) was another way to reduce orbit errors. The resulting Delft Gravity Model (DGM-E04) reduced this part of the orbit error by a factor of 2, and performed even better with respect to the ESA-provided orbits, and also the recent Earth Geopotential Model (EGM96). The ERS‑1 and ERS‑2 orbits for the entire tandem mission were computed and studied in detail, and the orbit errors due to the gravity field and non- conservative forces are identified. The analyses systematically show that the orbits computed with JGM-3 had a radial rms orbit accuracy of 7 cm, while those of DGM-E04 were 5 cm (Fig. 3). In 2003, ESA funded the Reprocessing of Altimeter Products for ERS (REAPER) project to perform homogeneous and full reprocessing of the ERS‑1 and ERS‑2 orbit files from the period July 1991 to June 2003 using up-to-date standards that were subsequently made available to users. During the ERS era, the space agencies established the Radar Altimetry Data System (RADS) archive for storing, cross-validating and distributing high- level, consistent altimetry data from all available satellites as an extension to the data distribution system.

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Figure 3. Locally averaged ERS−Topex dual satellite crossover height differences for the ERS‑1/ ERS-2 tandem mission. The four panels show the most common ERS orbit solutions: (a) GFZ PGM055, (b) DTU JGM-3, (c) EGM96 and (d) DGM-E04. (Scharroo et al,, 1998)

The ERS‑1 Altimeter (Phases A–G) Value-Added GDR Data Product (Berwin, 2001), was jointly developed by Ohio State University, the JPL Physical Oceanography Distributed Active Archive Center (JPL PO.DAAC) and the University of Texas Center for Space Research. Improved orbits, media and ocean tide corrections have been derived and applied to the data products (Shum et al., 1993; Anzenhofer et al., 1999; Urban et al., 2001; Ogle, 2003).

2.3 Coastal Altimetry

Among the many contributions that the ERS satellites have made to altimetric science, one of the highlights has been the establishment and development of coastal altimetry. Although open-ocean altimetry is a mature technique that is now routinely used in operational oceanography, until recently altimetric measurements of sea level and significant wave heights in the coastal zone were flagged as bad and discarded for technical reasons. These included the degradation of the waveforms owing to the presence of land in the altimetric footprint, and the inadequacy of some corrections for geophysical and atmospheric effects such as the path delay induced by atmospheric water vapour. In recent years several research groups have attempted to overcome such problems, and have given rise to the new field of coastal altimetry (Vignudelli et al., 2011). The idea that meaningful measurements in coastal zones can be retrieved is not new. Deng et al. (2002), for instance, demonstrated, using ERS‑2 data, that the statistics of the altimetric signal are encouraging up to just a few kilometres from the coast. This is illustrated in Fig. 4, where the shape conforms to the open-ocean model, and the standard deviation drops quickly with increasing distance from the coast. The seeds of this exciting new development for altimetry were sown in the 1990s with a number of studies based on ERS data. The first attempt to retrieve nearshore altimetric data by custom processing was made by Manzella et al. (1997), who recomputed the wet tropospheric correction for ERS‑1 altimeter data over the Corsica channel by recalibrating the model correction with the closest available radiometric estimate from the onboard ERS Microwave Radiometer, EMR-1. This was a precursor to the Dynamically Linked Model, a technique that is still used today (Obligis et al., 2011). Manzella et al. (1997) also recovered measurements that had been flagged as bad because the nadir backscatter was high due to a smooth sea surface, using another technique that has now been adopted to improve the recovery rate of altimetric estimates in coastal regions.

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Figure 4. Mean power of the waveform (left) and the standard deviation of the mean waveform (right) for ERS‑2 in five 2-km-wide bands from the Australian coast. (Deng et al., 2002)

An extensive study of coastal altimetry, taking into account all the issues in the reprocessing, was carried out by Anzenhofer et al. (1999). They described the generation of coastal altimetry data and were the first to analyse in detail the various retracking algorithms and their implementation. They showed some examples based on the intermediate ERS waveform data, and concluded with some recommendations for better (local) tidal modelling, careful screening of the data, improvement of the wet tropospheric correction and retracking, which still represent the main areas in which coastal altimetry research is concentrating its efforts (Vignudelli et al., 2011). This pioneering work with ERS altimetry data prompted ESA and CNES to launch two projects, COASTALT and PISTACH, to study and further exploit coastal altimetry data.

3. Mean Sea Surface Mapping

The Mean Sea Surface (MSS) is an important parameter in geodesy and physical oceanography. It is the time-averaged physical height of the ocean surface. In principle, a complete separation of the ocean mean and variable part requires uninterrupted infinite sampling in both time and space. The challenge in MSS mapping is to achieve the most accurate filtering of the sea surface variability over a limited time span and simultaneously obtain the highest spatial resolution. This is normally achieved by combining data from the highly accurate Exact Repeat Mission (ERM) with that from the ERS‑1 non-repeating Geodetic Mission and the older (1985) Geosat Geodetic Mission (Fig. 5). Since 1996, several determinations of the Mean Sea Surface have been made at the Collecte de Localisation Satellite (CLS) in Toulouse. For these surfaces data from the two 168-day interleaved ERS‑1 Geodetic Mission cycles were extremely important because they provided the spatial resolution of the finest geophysical structures at the shortest wavelengths with a track-to- track distance of 8 km at the equator and a sample of 7 km along tracks. As a complement, data from the Exact Repeat Mission of ERS‑1, ERS‑2 and Envisat were used to calculate mean profiles over a long period, from 1992 to the

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Figure 5. An early Mean Sea Surface derived from three years of satellite altimetry data from Topex and ERS‑1. (Yi, 1995)

Figure 6. Mean Sea Surface models CLS-SHOM 1998 (left) (Schaeffer et al., 1998) and CNES-CLS11 (right) (Schaeffer et al., 2012).

present. The first European MSS was calculated at CLS in 1998, with funding from the Hydrographic and Oceanographic Service of the French Navy (SHOM). In 2001 the CLS01 MSS was computed and in 2011, 20 years after the launch of ERS‑1, CLS determined the CNES-CLS11 MSS using 16 years of altimetry data from Topex/Poseidon/Jason-1 and, respectively, 14 years of data from ERS/Envisat, 7 years of data from the Geosat Follow-On (GFO), 3 years of data from Topex/ Poseidon interlaced and the two 168-day non-repeat ERS‑1 subcycles (Fig. 6). MSS models were also developed at the Danish National Space Center (DNSC/DTU). The DTU10 Mean Sea Surface is one of the most recent geometric descriptions of the averaged height of the ocean surface derived from a combination of 17 years of altimetry data from eight satellites covering the period 1993–2004 (Fig. 7). This is a truly global MSS derived at 1 min resolution, and includes all of the Arctic Ocean by including laser altimetry from Icesat and a sophisticated extension towards the North Pole using the geoid.

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Figure 7. The differences between the Mean Sea Surface models CNES-CLS11 and DTU10, of the order of 1–3 cm rms, clearly demonstrate the high level of consistency. Locally (at high latitudes, in areas of strong variability) the differences can exceed 10 cm, particularly in the Arctic Ocean where the CNES-CLS11 surface coverage is limited. These differences provide good indicators of the consistency and relative accuracy of the MSS models derived at various institutions. (Andersen & Knudsen, 2009)

4. Gravity and Tectonics

The sea surface mimics the geoid, which is an equipotential surface of Earth’s gravity field. Matter is not evenly distributed on or below the seafloor (or further within the crust and mantle), causing variations in the pull of gravity that produce tiny variations in ocean surface height. By accurately mapping these variations, the process can be inverted and the pull of gravity can be deduced from observations of sea surface heights (Fig. 8). Today, several global marine gravity fields at 1 min resolution are available for free download from the internet. These include the NTU fields (Hwang et al., 2000), the Sandwell & Smith fields (Sandwell & Smith, 1997), the KMS02 fields (Andersen & Knudsen, 1998), the GSFC fields (Wang, 2001), and the CLS- SHOM fields (Hernandez & Schaeffer, 2000); see Fig. 9. The derivation of a marine gravity field from satellite altimetry over permanently ice-covered regions of the Arctic Ocean has provided much new geophysical information about the structure and development of the Arctic seafloor. Because of its remote location and permanent ice cover, the Arctic Ocean, from a tectonic point of view, remains the most poorly understood ocean basin. A gravity field has been derived with data from the ERS‑1 radar altimeter, including permanently ice-covered regions. The gravity field shown in Fig. 10 clearly delineates sections of the Arctic Basin margin along with the tips of the

Figure 8. One of the first examples of regional gravity field mapping in the North Atlantic between Norway, Iceland and Greenland, using ERS‑1 radar altimeter data from the 35‑day multidisciplinary phase. Left: Map showing known geological features; right: gravity anomalies given in mGal. (Knudsen et al., 1992)

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Figure 9. Three ERS‑1 GM derived global marine gravity fields. Top: DNSC08 global free air gravity anomalies (Andersen et al., 2009); middle: the Sandwell & Smith field v18.2; bottom: the CLS-SHOM1999 gravity anomaly field.

Lomonosov and Arctic mid-ocean ridges. Several important tectonic features of the Amerasia Basin are clearly expressed in this gravity field. These include the Mendeleev Ridge; the Northwind Ridge; details of the Chukchi Borderland; and a north–south trending, linear feature in the middle of the Canada Basin that apparently represents an extinct spreading centre that ‘died’ in the Mesozoic era. A corresponding marine gravity field for the Antarctic region from ERS‑1 altimetry (Fig. 11) reveals the southernmost traces of fracture zones (FZs), e.g. the

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Pahemo and Endeavour FZs in the southern Amundsen Sea, all the way to their intersection with the west Antarctic continent (see McAdoo & Laxon, 1997). These traces of fracture zones place constraints on the early history (65–80 million years ago) of seafloor spreading between the Antarctic and the New Zealand micro- continent and support the existence of a palaeo-tectonic plate, the Bellingshausen. For tectonics, the gravity field indicates a strong anomaly within the Ross Sea that may constrain models of recent ice sheet history in the Ross embayment. It has been suggested that one-half of this gravity low could be attributed to the effect of the loading of the crust by the weight of a much larger sheet during the early Holocene, 10 000–20 000 years ago. It has been proposed that such an ice sheet may have covered the Ross Sea during that period. The rheology of the oceanic lithosphere in this area is dominated by the inner distribution of temperature, the lithosphere cooling slowly from the ridge where it is formed to the old abyssal basins. Owing to the global coverage offered by satellite altimetry, the equivalent elastic thickness of the oceanic lithosphere has been investigated on a global scale (Calmant & Baudry, 1996) and regional anomalies of the thermal structure of the oceanic lithosphere Figure 10. The regional altimetric gravity in the Central Pacific have been reported (Calmant et al., 2002). Recently, a field for the Arctic Ocean derived from derivation of retracked gravity anomalies over smaller inland water bodies like ERS‑1 radar altimetry. This is one of the the Great Lakes was presented by Kingdon et al. (2008). first examples of gravity determined under the ice-covered Arctic Ocean. (Laxon & McAdoo, 1994) 5. Bathymetry

A detailed knowledge of the topography of Earth’s surface is fundamental for understanding most physical processes. On land, weather and climate are controlled by topography on scales ranging from large continental landmasses to small mountain valleys. Since the land is shaped by tectonics, erosion and sedimentation, an understanding of topography is essential for any geological investigation. In the oceans, a detailed knowledge of bathymetry is also essential for understanding physical oceanography, biology and marine geology. Currents and tides are steered by the overall shapes of the ocean basins as well as by smaller sharp ocean ridges and seamounts. Recent reports suggest that the interaction of tides and currents with the rugged seafloor mix the ocean to provide global overturning. Sea life is abundant where rapid changes in ocean depth deflect nutrient-rich water upwards towards the surface. Because erosion and sedimentation rates are low in the deep oceans, detailed bathymetry also reveals mantle convection patterns, plate boundaries, the cooling/subsidence of the oceanic lithosphere, oceanic plateaus and the distribution of volcanoes (Fig. 12). Figure 11. The first complete altimetric Because it is not possible to map the topography of the ocean basins directly gravity field for the entire Southern from space, most seafloor mapping is a tedious process that has been carried Ocean derived from ERS‑1 radar altimetry, out over a 40-year period by research vessels equipped with echo sounders. So including ice-covered seas and extending far, only a few percent of the oceans have been surveyed at 200 m resolution. to the southernmost ocean limit of 79°S, It has been estimated that 125–200 ship-years of survey time will be needed i.e. excluding the Ross and Ronne−Filchner to map the deep oceans, costing several billion dollars. Mapping shallow seas ice shelves (McAdoo & Laxon, 1997; Laxon would take even more time and funding. Therefore, the geodetic missions & McAdoo, 1998) The southern limits of such as those of Geosat and ERS‑1 included high-resolution mapping of the ERS, Icesat and Cryosat-2 altimetry are oceanic domain that has been of primary importance in seafloor mapping and shown by the green, blue and red circles, seamount detection (Baudry & Calmant, 1996). respectively, overlying the grey Antarctic In the wavelength band 10–160 km, variations in gravity anomalies are continent. highly correlated with seafloor topography and thus, in principle, can be used to recover topography in an inversion process (Baudry & Calmant, 1991; Calmant & Baudry, 1996; Calmant et al., 2002; Andersen & Knudsen, 2009). For longer and shorter wavelengths, isostatic compensation and noise are limiting factors.

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Figure 12. Two examples of global bathymetric models derived from satellite altimetry. Top: Sandwell & Smith v7.1; bottom: DNSC08.

6.ae Se Ic 6.1 Arctic Sea Ice Thickness

ERS satellite altimetry has been instrumental in determinining the mean thickness of Arctic ice and its variability between 65°N to 82°N over the period 1993–2001. The ERS data reveal a high-frequency inter-annual variability in mean Arctic ice thickness that is dominated by changes in the amount of summer melt, rather than by changes in circulation. To deduce the ice thickness from ice elevation, the source of the echoes scattered from snow- covered sea ice must be determined. Laboratory experiments show that, under dry cold snow conditions, a normal-incidence 13.4‑GHz radar reflection from snow-covered sea ice originates at the snow–ice interface. The ERS radar altimeter measurements of ice elevation therefore provide the level of the snow–ice interface above the water level, that is, the ice freeboard. The ice freeboard measurements are subsequently converted to thickness and mass (Fig. 13).

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Figure 13. Left: Average winter Arctic sea ice thickness from October 1993 to March 2001 from ERS altimeter measurements of the ice freeboard. Right: Comparison of satellite altimeter- and submarine-derived ice thicknesses in the Beaufort Sea during the 1990s. Submarine thicknesses are shown for each of the 50-km segments gathered during four missions during the 1990s. Altimeter thickness estimates are generated from observations within 15 days and 100 km of the submarine draught sections. The submarine thicknesses exclude thin ice (0.5 m) and open water, because (owing to difficulties in discriminating thin ice from open water) progressively more altimeter data are excluded once the ice thickness falls below 1 m. The error bars show uncertainties in altimeter thickness due to measurement errors and snow depth variability.

6.2 Antarctic Sea Ice Loss

By combining ERS measurements and the results of a coupled ice–ocean model, Shepherd et al. (2010) were able to provide the first estimates of changes in the quantity of ice floating in the global oceans and their consequent contribution to changes in sea level. Their results show that rapid losses of Arctic sea ice and small Antarctic ice shelves are partially offset by thickening of Antarctic sea ice and large Antarctic ice shelves (Fig. 14). Altogether, 746 ± 127 km3 yr–1 of floating ice was lost between 1994 and 2004, a value that exceeds considerably the reduction in grounded ice over the same period. Although the losses are equivalent to a small (49 ± 8 mm yr−1) rise in mean sea level, there may be large regional variations in the degree of ocean freshening and mixing.

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Figure 14. Average rate of rate of change in the thickness of Antarctic ice shelves, 1994–2008, determined from ERS and Envisat radar altimetry and a model of accumulation fluctuations. (Shepherd et al., 2010)

7.e Ic Sheet Elevation Models

Ice sheet research was one of the key targets for the ERS‑1 programme. Compared with other satellite and field data, ERS‑1 allowed significant advances, particularly in polar ice sheet monitoring. The key factors were the improved spatial coverage and improved latitudinal coverage compared with previous satellites. Improved microwave sensor technology also made a major contribution. Finally, the ERS‑1 altimeter was designed with a specific mode for mapping ice sheet topography and investigating volume changes. The main physical processes (climatic and dynamic) that act on an ice sheet induce particular signatures on its free surface. From the small scales to the larger ones, topography contains important information about local anomalies or general trends in behaviour (Rémy et al., 1999). Accurate knowledge of the topography of an ice sheet also provides an initial condition on which to base analyses of its future evolution. At the large scale, the surface is quasi- parabolic and is governed by ice viscosity and by flow boundary conditions (Fig. 15). The topography is mostly controlled by the distance from the coast (Vaughan & Bamber, 1998), except where basal conditions, such as sliding, affect the topography in the upslope direction. The large-scale topography controls the flow direction and its mapping allows the calculation of balance velocities. At largest scale, networks of anomalies of the surface topography perpendicular to the steepest slope direction can be pointed out. They are due to boundary flow conditions of outlet glaciers that are propagated from the coast up to the dome (Rémy & Minster, 1997). They indicate drainage patterns, upstream glacier positions or the flowline directions, but also the role of the outlet flow conditions on the whole shape. At the 10 km scale, topographic signatures of the flat ice shelf make it possible to delimit adjacent glaciers and to visualise lateral stress effects (Fig. 16). ‘En echelon‘ structures with a tilted orientation of 45° with respect

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Figure 15. Complete elevation map of the Greenland ice sheet derived mainly from ERS‑1 altimetry data. Left: The topographic relief has been spatially filtered to highlight the shorter-wavelength features in the ice sheet and to illustrate ice sheet dynamics. (Bamber et al., 1998). Right: Topographic relief map of the Antarctic ice sheet (Bamber et al., 2009).

Figure 16. High-resolution map of Antarctic ice sheet topography from the ERS‑1 Geodetic Mission. The elevation reaches 4000 m. Note the ice shelves surrounding most of the continent, surface undulations, flat areas reflecting subglacial lakes and elongated scars due to hydrological networks. (Rémy et al., 1999)

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to the flow direction have been found but have not yet been explained. In the same way, undulations at the 10 km scale wavelength with a metric scale amplitude seem to be the main ice sheet surface features. They have been attributed to ice flow above an irregular bedrock. Up to now, they have been modelled as symmetrical dome-shaped features. Their spatial characteristics may allow a better understanding and modelling of these structures; they have been found to be elongated, frequently with an orientation of 45° with respect to the flowline direction. The 10 km scale seems to be a characteristic scale of ice flow processes, as discussed in section 8.

8.e Ic Sheet Mass Balance

Understanding ice sheet dynamics is a key objective of glaciological work in both Antarctica and Greenland, along with the motion and deformation of the ice and relating these to the mass balance and ice volume. Very early into the mission changes in the elevation of the Antarctic Ice Sheet were reported (Wingham et al., 1998). The mass balance for the Antarctic ice sheet is mapped in Fig. 17 for the ERS and Envisat periods 1992–2003 and 2002–6, respectively. Rémy & Parouty (2009) and Flament & Rémy (2012) extracted the temporal trend by correcting for the radar penetration with the help of the backscattering coefficient and the whole waveform shape. The amplitude of local fluctuations can be as large as 15 cm yr−1 and are attributed to small temporal changes in precipitation. The difference between the two maps in Fig. 17 has the same ranges of variation as the average map, meaning that more than 50% of the signal is not constant over time. Indeed, one can see differences in the patterns of change between the ERS and Envisat periods. As soon as the distance to the coast is greater than 200 km, the amplitude of the variations falls to a few centimetres per year. The centre of the east Antarctic ice sheet is then stationary over a few thousand kilometres. Most of the studies of the Antarctic ice sheet mass balance using altimetry (see Zwally et al., 2005; Wingham et al., 2006; Rémy & Parouty, 2009) have found few areas of thickening in the eastern part of Wilkins Land or in the Antarctic Peninsula, and few areas of thinning near the Pine Island Bay sector and near Law Dome. Most changes in ice surfaces near the coast have been attributed to changes in ice dynamics due to perturbation of the outlet glaciers, as previously explained. Depending on the kind of corrections made, especially to account for penetration issues, various authors find this part to be slightly thinning or slightly thickening. The behaviour of the western part of Antarctica is quite different. The West Antarctic Ice Sheet (WAIS) is a marine ice sheet, since most of its bedrock is below sea level, and it is therefore very sensitive to increases in

Figure 17. Mass balance of the Antarctic ice sheet (in m yr–1) for the ERS period (1992– 2003, left) and the Envisat period (2002–6, right). Note the significant thinning over a large sector of the western ice sheet. The eastern part of the continent exhibits less impressive signals, but also shows local fluctuations that depend on the period. (Rémy & Parouty, 2009)

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ocean water temperature. It receives a relatively higher geothermal flux than the eastern part, so that the basal temperature is higher (Llubes, 2006). As a consequence, the temperature of most of the ice lying on the bedrock rises close to melting point and the ice slides down at higher velocity. This sector exhibits volume fluctuations over a wide range of timescales. Mass balance observations support the different behaviours of these two parts. Indeed, all attempts to derive the mass balance from altimetry measurements agree that there is a clear thinning of the drainage basin of the Pine Island and Thwaites glaciers in the western part of the WAIS. One team found that the whole of western Antarctica is thinning of at a rate of 47 Gt yr–1 (Zwally et al., 2005), corresponding to a rise in sea level of 0.12 mm yr–1. Another team (Shepherd et al., 2003; 2006; Wingham et al., 2006) detected dynamic responses of the ice sheet leading to either thickening or thinning, as shown in Figs 18 and 19. In summary, Antarctica happens to be close to equilibrium but strong regional signals suggest that the local dynamical responses of ice may accelerate in the future. The glaciers of both east and west Antarctica may provide a substantial contribution to global sea level change (Shepherd et al., 2001, 2008). The altimeter is not only a useful tool for mapping global trends, but it can also identify some local imbalances. However, the discrepancies in studies of some areas may be explained by the limitation of retrieving height variations due to the penetration of the altimetric radar wave in the snowpack. Such an effect has been pointed out in the Amery ice shelf. Densification processes also yield critical errors because they act directly on the ice surface and indirectly via changes in snowpack characteristics. For some authors, the changes in the magnitudes of firn depth are comparable with observed changes in ice sheet elevation (Sundal et al., 2012).

Figure 18. A: Changes in the elevation of the Antarctic (left) and Greenland ice sheets (right), 1992−2003, from ERS radar altimetry (from Shepherd & Wingham, 2007). The insets show the bedrock geometry, including floating (light grey), marine-based (mid-grey) and continental- based (black) sectors of the ice sheets. B: Changes in elevation (flow in excess of 50 m yr–1) of the trunks of glaciers such as Pine Island (basin GH in panel A), Thwaites (basin GH), Totten (basin C′D), and Cook (basin DD′). (Shepherd & Wingham, 2008)

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Figure 19. Rate of change in surface elevation of the Larsen ice shelf determined from ERS radar altimeter measurements, 1992−2001 (colour scale and 0.1 m yr−1 white contours). The elevation data are superimposed on a mosaic of Advanced Very-High-Resolution radiometer (AVHRR) satellite imagery (grey scale). Data points used in the interpolation are shown as black dots. Also shown are the locations of meteorological stations (blue dots), stakes used to measure changes in snow height (red dots) and surface mass balance (5-km grid, centred on the green dot). The 1990 boundaries of the Larsen-A, -B and -C ice-shelf sections are highlighted in blue, green and red, respectively. Larsen-A collapsed before the ERS measurements, Larsen-B has since disintegrated, and only Larsen-C remains intact. (Shepherd et al., 2003)

9. Global Digital Elevation Models

The Earth and Planetary Remote Sensing Laboratory at De Montfort University, UK, generated a global Digital Elevation Model (DEM) at 30 arcsec resolution by combining the best available ground truth data with a unique global database of ERS‑1 altimeter-derived heights over land, reprocessed with a novel expert system. This model, called Altimetry Corrected Elevations (ACE), presents radically improved spatial accuracy and resolution over large regions of the globe, in particular over Africa and South America, but has suffered so far from a serious lack of in situ data. The ACE-2 dataset was created by merging the Shuttle Radar Topography Mission (SRTM) dataset with satellite radar altimetry within the region bounded by the 60° parallels (Fig. 20). For areas lying outside the SRTM’s latitude limits other data sources were used, such as the Global Land One-km Base Elevation (GLOBE), the original ACE DEM, together with new matrices derived from reprocessing the ERS‑1 GM dataset with an enhanced retracking system, and data from other satellites. The main altimetry dataset used in the generation of the ACE-2 dataset was the ERS‑1 Geodetic Mission, which, because of its small across-track spacing, presents a uniquely dense spatial distribution of tracks over land surfaces. Data from the ERS‑2 and Envisat Ku-band were also included where appropriate. All of the altimetry data were

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Figure 20. The ACE-2 model. Over the oceans the elevation model has been supplemented with altimeter-derived bathymetry. (Smith et al., 2007) reprocessed using the Berry expert system to retrack the waveforms (Smith et al., 2007). Over 11 billion pixels from the SRTM dataset have been adjusted using this unique network of control arcs of altimeter-derived height data. In addition, an extensive investigation over the rainforests has shown that the altimetry data are returning ground values whereas the SRTM signal bounces off somewhere within the canopy. Therefore, areas of rainforest in both the Amazon and the Congo have been completely replaced by altimetry-defined surfaces.

10. River and Lake Levels for Hydrology

The launch of the ERS satellites began a new era of applications of altimetry for monitoring river and lake levels. An obvious advantage of using data derived from satellites is the global coverage and regular temporal sampling of the processed data, although there are difficulties in interpreting radar altimeter measurements made over inland water bodies (Frappart et al., 2006). The primary reason for studying altimetry over lakes was to validate altimeter measurements (i.e. Cretaux et al., 2009) as lakes have minimal tides and little dynamic variability compared with the oceans, so that the spatial changes in lake levels closely follow the geoid. The great potential of altimetry for monitoring inland water levels rapidly became apparent (Birkett, 1994; Calmant & Seyler, 2006; Calmant & Birkett, 2006). This has been applied several times over the North American Great Lakes (Frappart et al., 2006, Morris et al., 1994), the Caspian Sea (Cazenave et al., 1997); Lake Baikal Kouraev et al., 2007), the East African lakes (Birkett et al., 1999) and the largest rivers, including the Amazon (Alsdorf, 2001; Medina et al., 2008; Santos da Silva et al., 2011). The potential for generating river and lake heights on a global scale by reprocessing individual altimeter echoes was demonstrated by (Berry et al., 2005, 2007). This subsequently became ESA’s River and Lake project (http://earth.esa.int/riverandlake/). The accuracy of altimeter measurements has greatly improved over the past decade as a result of advances in the instrumentation and in the precision of

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Figure 21. Detected river and lake targets using ERS and Envisat altimetry globally (left) and over the Amazon delta (right). The colour coding indicates the quality of the time series that can be extracted from ERS altimetry; green indicates nearly uninterrupted time series. (http://earth.esa.int/riverandlakes)

satellite orbit calculations. Satellite altimetry coverage over land surfaces has also been greatly improved by ESA’s inclusion of additional tracking modes on the ERS and Envisat altimeters, which enable the instruments to track rapidly changing surfaces. This has led to substantial advances in altimeter research over ice, land and inland water bodies (Frappart et al., 2006). Several teams around the world are now involved in satellite altimetry over inland water bodies, including those led by C. Birkett (NASA, USA), A. Cazenave (Centre National d’Etudes Spatiales, CNES, France), P. Berry (De Montfort University, DMU, UK) and J. Santos da Silva (Universidade do Estado do Amazonas, Manaus, Brazil). At DMU, the team has studied the land surface response to the altimeter pulse, and this has allowed characterisation of the relationship between the underlying terrain and the individual echo shapes (Figs 21 and 22). Because of the ERS and Envisat missions it is now possible to obtain decadal time series over a number of targets globally. These targets are often large enough to present Brown model waveforms and therefore ocean retracking can be performed. However, the vast majority of inland water bodies do not return these types of signal. The use of retracking means that it is possible to monitor a significantly larger number of targets. Even so there are a number of factors that limit the capability of an altimeter to obtain the correct heights. The current generation of databases, such as ESA’s River and Lake database and the LEGOS Hydroweb (Cretaux et al., 2011c), have hundreds of targets where the results are deemed to be of high enough quality. The current system can obtain results globally from both rivers and lakes throughout the year. Satellite altimeter data provide water resource planners with an additional source of information about remote areas such as Africa, for which the availability of in situ data is limited. One example of a lake time series is that for Lake Kainji in western Nigeria (Fig. 23). This important lake is fed by the Niger River and its water levels have knock-on effects along the length of the river, which is the main source of water in the area. An altimeter can perform just as well over rivers as over lakes, although more complicated processing is required. A major benefit is that it is possible to obtain time series from along the entire length of a river system to provide as complete a picture of water flow as possible. As an example, Fig. 25 shows the ERS–Envisat time series of the Brahmaputra River that flows through India and Bangladesh.

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Figure 22. Bottom: Time series of water levels from ERS‑2 and Envisat in the Amazon Basin, 1995−2009. (Santos da Silva et al., 2010) Top: Differences between the altimetry and the gauge series. Histograms of the residuals are shown in the inset (bottom right) with rms differences (in mm).

Figure 23. Lake Kainji in western Nigeria (left) and joint ERS-2−Envisat time series of lake levels, 1995−2008 (right). (Berry et al., 2005)

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Figure 24. Lake Balbina, Brazil, monitored by satellite altimetry. This is an important, reservoir with a dam that provides energy for the 2 million inhabitants of Manaus, Brazil (Santos da Silva et al., 2010). Note that gauge data from the reservoir itself or from the Uatuma River that feeds the lake are not publicly available; the gauge series is displayed by courtesy of Amazonas Energia.

Figure 25. The Brahmaputra River in Bangladesh. Location of the ERS−Envisat time series (left) and observed river level time series (right) (http://earth.esa.int/riverandlake).

11. Global and Large-Scale Ocean Signals

One of the objectives of the ERS altimetric mission was to map the global ocean circulation and its variations and associated transfers of energy, together with ocean−atmosphere interactions and ocean tides. ERS also proved to be a great asset for studying climate change and for mapping the impacts of unpredictable events such as the 2004 tsunami in the Indian Ocean once the quality of the altimetric observations was enhanced, as described in section 2.

11.1 Ocean Tides

The ERS and Envisat satellites operated in Sun-synchronous orbits, and so were far from ideal for accessing ocean tides because they always observed the same phase of the solar tidal wave. However, ocean tides can be derived from the ERS−Envisat radar altimeter data using the correlation between the tidal constituents and/or by combining the data with hydrodynamic modelling. In polar seas, very few data are available for tidal analysis as tide gauges are sparse and the coverage of the Topex/Jason-1 satellites is limited to ±66° latitude. Satellite altimetry from ERS and Envisat was one way to observe the ocean tides. From early in the ERS mission satellite altimetry was used to derive regional and global ocean tide models (Figs 26; Andersen, 1994, 1995), as well as to determine ocean tides around and beneath large ice shelves and comparing them with observations of their vertical movements (Figs 27 and 28).

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Figure 26. Ocean tides in the North Atlantic and adjacent seas derived from ERS‑1 radar altimeter data using the correlation between the tidal constituents for the Sun- synchronous S2 constituent. The amplitudes are contoured and the phases are colour coded. (Andersen, 1994, 1995)

Figure 27. The major tidal signal in the Southern Ocean. The largest semidiurnal constituent M2 (left) and the largest diurnal constituent K1 tides (right) determined using multiple satellite altimetry data. (Yi et al., 2004, 2006)

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Figure 28. Amplitudes and phases of the four most energetic tidal constituents (K1, O1, S2, and M2) for the ice shelves on the west coast of the Antarctic Peninsula, determined from ERS radar altimetry and tide gauge recordings (squares). Ice shelf boundaries were delimited from digital coastline data (British Antarctic Survey, Scott Polar Research Institute and the World Conservation Monitoring Centre). Tidal amplitude and phase (colour) are superimposed on an AVHRR mosaic (grey) of grounded ice. Open water is white. Circles: locations (and footprint sizes) of ERS satellite ground track crossing points from which the tidal model was derived; squares: tide gauges. (Shepherd & Peacock, 2003)

11.2 Tsunamis

Tsunamis are waves triggered by earthquakes and landslides or, rarely, unusually large seafloor volcanic eruptions. A large tsunami can generate huge waves that endanger people and damage property in low-lying coastal areas. The Indian Ocean tsunami, on 26 December 2004, killed more than 200 000 people and left millions homeless. The radar altimeters on the Jason-1, Topex, Envisat and Geosat Follow-On satellites obtained profiles of sea surface heights along transects across the Indian Ocean between two and nine hours after the earthquake (e.g. Song et al., 2005; Scharroo, 2007; see Fig. 29). Such data are usually received hours to days after the event, and so are too late to be used in the detection of tsunamis or to issue warnings.

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Figure 29. A–C: Tsunami wave heights generated by the Indian Ocean earthquake on 26 December 2004, computed by the MOST (Method of Splitting Tsunami) model (A) 2:00 h, (B) 3:15 h, and (C) 8:50 h after the earthquake. These times coincide with overflights of the Topex/Jason-1, Envisat and GFO satellites, respectively. D–G: Comparison of sea height deviations as measured by the satellite altimeters and the modelled wave heights. (Scharroo et al., 2005)

11.3a Se Level Change

During the last two decades satellite altimetry has demonstrated its ability to measure temporal changes in global mean sea level with a precision of the order of 0.4 mm yr–1 or better. Such changes in mean sea level are most frequently reported using altimetry from the Topex and Jason-1 satellites, and those obtained using ERS and Envisat satellites are nearly identical (Fig. 30). Changes in sea level outside the 66° parallels can be estimated using the joint ERS and Envisat time series (Fig. 31) providing estimates even for polar regions (Fig. 32).

Figure 30. Global sea level trend from all available satellites for the period 1991−2012. Note that GIA or IB corrections have not been applied. (Scharroo et al., 2012)

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Figure 31. Changes in sea level from ERS and Envisat altimetry over the period 1992–2008. Yellow and red indicate a rise in sea level, while blue and green indicate a fall. (Andersen et al., 2002, 2009)

Figure 32. Trends in sea level in the Arctic and Antarctic oceans on a 1°×1° grid based on the Aviso MSLA delayed-time data product (0.25°×0.25°). The mean of the linear trend within the red boundary (55°N–82°N/45°W–60°E) is 2.9 mm yr–1. (Updated from Kuo, 2006; Shum & Kuo, 2011)

Figure 33. Sea level anomalies in the equatorial Pacific Ocean on 30 November 1997 (top), and 30 August 1998 (bottom), based on ERS−Envisat radar altimeter data over a 16-day period centred on those days. (DEOS; http://rads.tudelft.nl/results)

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11.4 El Niño

The radar altimeters on the ERS satellites measured sea surface heights between July 1991 and 2011. One area of interest is the equatorial Pacific Ocean where the famous El Niño roars every few years (Fig. 33). El Niño events are characterised by a relatively high sea level along the west coast of Central America accompanied by radical changes in the regional climate with heavy rainfall. At the same time, the sea level drops in the western equatorial Pacific, where extreme droughts devastate crops. The opposite extreme is called La Niña.

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Shepherd, A. & Wingham, D. (2008). Antarctic glacier thinning, 1992–2003. Scott. Geogr. J. 124, 154–164. Shepherd, A., Wingham, D., Payne, T. & Skvarca, P. (2003). Larsen ice shelf has progressively thinned. Science 302, 856–859. Shepherd, A., Wingham, D.J., Mansley, J.A.D. & Corr, H.F.J. (2001). Inland thinning of Pine Island Glacier, West Antarctica. Science 291, 862–864. Shepherd, A., Wingham, D., Wallis, D., Giles, K., Laxon, S. & Sundal, A.V. (2010). Recent loss of floating ice and the consequent sea level contribution. Geophys. Res. Lett. 37, L13503. Shum, C. & Kuo, C. (2011). Observation and geophysical causes of present-day sea level rise, in R. Lal, M. Sivakumar, S. Faiz, A. Rahman & K. Islam (Eds.), Climate Change and Food Security in South Asia, Part 2, ch. 7, pp.85–104. Springer, Berlin. doi: 10.1007/978-90-481-9516-9_7 Shum, C., Tapley, B., Kozel, B., Visser, P., Ries, J. & Seago, J. (1993). Precise orbit analysis and global verification results from ERS‑1 altimetry. Space at the Service of Our Environment, Proc. 2nd ERS‑1 Symp., vol. 2, pp.747–752. ESA SP-361, ESA, Paris. Smith, R.G, Berry, P.A.M. & Benveniste, J. (2007). Representation of rivers and lakes within the forthcoming ACE-2 Global Digital Elevation Model. Proc. Hydrospace 2007, Geneva, Switzerland. www.congrex.nl/07M19/ Song, T., Chen, J., Fu, L.-L., Zlotnicki, V., Shum, C., Yi, Y. & Hjorleifsdottir, V. (2005). The 26 December 2004 tsunami source estimated from satellite radar altimetry and seismic waves. Geophys. Res. Lett. 32, L20601. doi: 10.1029/2005GL023683 Sundal, A.V., Shepherd, A., Nienow, P., Hanna, E., Palmer, S. & Huybrechts, P. (2011) Melt-induced speed-up of Greenland ice sheet offset by efficient subglacial drainage. Nature 469, 522−U83. Urban, T., Pekker, T., Tapley, B., Kruizinga, G. & Shum, C. (2001). A multiyear comparison of wet troposphere corrections from TOPEX/Poseidon, ERS‑1 and ERS‑2 microwave radiometers and the European Centre for Medium-range Weather Forecasts model. J. Geophys. Res. 106(C9), 19 657–19 669. Vaughan, D.G. & Bamber, J.L. (1998). Identifying areas of low-profile ice sheet and outcrop damming in the Antarctic ice sheet by ERS‑1 satellite altimetry. Ann. Glaciol. 27, 1–6. Vignudelli, S., Kostianoy, A.G., Cipollini, P. & Benveniste, J. (Eds) (2011). Coastal Altimetry, Springer, Berlin. doi: 10.1007/978-3-642-12796-0 Wang, Y.M. (2001). GSFC00 mean sea surface, gravity anomaly, and vertical gravity gradient from satellite altimeter data. J. Geophys. Res. 106(C12), 31 167−31 174. Wingham, D., Ridout, A.J., Scharroo, R., Arthern, R.J. & Shum, C.K. (1998). Antarctic elevation change from 1992 to 1996. Science 282(5388), 456–458. doi: 10.1126/ science.282.5388.456 Wingham, D.J., Siegert, M.J., Shepherd, A. & Muir, A.S. (2006). Rapid discharge connects Antarctic subglacial lakes. Nature 440, 1033–1036. doi: 10.1038/ nature04660 Yi, Y. (1995). Determination of Gridded Mean Sea Surface from TOPEX, ERS‑1 and Geosat Altimeter Data. Ohio State University Geodetic Science Report 434, Ohio State University. Yi, Y., Matsumoto, K., Shum, C., Wang, Y. & Mautz, R. (2006). Advances in southern ocean tide modelling. J. Geodynamics 41(1–3), 128–132. Yi, Y., Shum, C., Andersen, O. & Knudsen, P. (2004). Extreme southern ocean tide modelling, in C. Hwang, C. Shum & J. Li (Eds), Satellite Altimetry for Geodesy, Geophysics and Oceanography. Int. Assoc. Geodesy Symp. Series 126. Springer, Berlin. Zwally, H.J., Giovinetto, M.B., Li, J., Cornejo, H.G., Beckley, M.A., Brenner, A.C., Saba, J.L. & Yi, D.H. (2005). Mass changes of the Greenland and Antarctic ice sheets and shelves and contributions to sea-level rise: 1992–2002. J. Glaciol. 51, 509–527.

254 →→20 Years of ERS → Interferometry

SAR Interferometer

20 f Years o ERS Interferometry

F. Rocca

Dipartimento di Elettronica e Informazione, Politecnico di Milano, 20133 Milano, Italy.

1. Introduction: Interferometry for Beginners

For the inexperienced reader, an interferogram is the pixel-by-pixel product of two complex conjugate SAR images of the same scene (Curlander & McDonagh, 1991; Franceschetti & Lanari, 1999; Ferretti et al., 2007). The modulus is dependent on the power reflected from that pixel and the phase is the difference of the phase delays due to the two way travel path and to the scattering process in the two instances. Hence, if the scatterers stay the same, only the travel path changes are recorded, be they due to the motion of the target or to the parallax of the takes (called baseline), or both. In fact, if the scatterers did not change at all their reflectivity from one pass to the next, the interferogram phase would show the changes of the travel path of the scatterer to the satellite and back, in terms of number of wavelengths. The interferometric fringes then would tell to a small fraction of the wavelength (5.77 cm, in the C-band) the travel path changes, and they would depend mostly on the terrain topography, for any given baseline (parallax). Thus, we can obtain Digital Elevation Models (DEMs) from interferograms. However, the two images should be taken at the same time to avoid the effects of the ever-changing atmospheric delays due to water vapour that could spoil the otherwise satisfactory DEMs. If to get rid of the atmospheric disturbances, interferometry has to be single pass, but then two well-separated receivers are needed to create the parallax (baseline). Owing to the simultaneity of the passes, there is no change in reflectivity (the coherence of the two images is high), and the atmospheric delay is the same for the two images, and thus it is totally subtracted. The two receivers should be distant enough to achieve a good parallax (several tens of metres, in the C-band) and this is costly, in space. On the other hand, if the images are taken in successive passes (multipass), only one simple satellite is needed, but then the number of passes should be sufficient to be able to reduce the atmospheric disturbances by averaging, and this only for those scatterers that did not change their reflectivity in the meantime. Another problem that needs to be solved is Phase Unwrapping (PU), i.e. to enumerate properly the interferometric fringes. With PU we find the total travel path change by counting the number of interferometric fringes from any pixel to any other or to some reference point. This problem is solvable in theory and practice, but only if we are given many more than one interferogram. Otherwise, between any two pixels, we need to find a path encountering only well separated and noiseless fringes, so that we do not miss any. This entails smooth topography and good coherence, i.e. small changes in the reflectivity from one pass to the next. In conclusion, we need either complex single-pass experiments such as the NASA/DLR/ASI Shuttle Radar Topography Mission (SRTM), or the DLR TerraSAR‑X add-on for Digital Elevation Measurement (TanDEM-X; see below), using two SAR receivers tens or hundreds of metres apart, or we may use unmanned satellites that periodically revisit the same area many times at short revisit intervals. Again, interferometry has to be either single pass, to remove the atmospheric bias as it is just the same for the two images and to achieve utmost stability of the reflectivity of the targets, or multipass. In this case, the number of passes should be sufficient to reduce the ever-changing atmospheric bias

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by exploiting statistics. This could also be helpful to solve Phase Unwrapping. Therefore, unmanned satellites are the key for interferometric advances as they can produce a very high number of interferograms at low cost. The first studies of SAR interferometry were carried out at NASA’s Jet Propulsion Laboratory (Graham, 1974; Zebker & Goldstein, 1986, Goldstein et al., 1988) and led to seminal papers by Jack Curlander, Richard Goldstein, Scott Hensley, Paul Rosen, Jacob Van Zyl, Howard Zebker, and many others. Attention was centred on short-term interferometry as the observations had been carried out using aircraft or manned space platforms. It is a useful simplification to state that the studies carried out in the US dealt mostly with single-pass interferometry. Europeans used mostly unmanned satellites, in the tradition of ERS‑1, and therefore used multipass interferometry and excelled in long-term differential interferometry studies. However, European airplane data were plentiful and well studied, and US activities using European, Canadian and Japanese unmanned SAR satellites were also numerous and important. The first civilian SAR satellite was the L-band Seasat, launched in 1978. In its short life, just 78 days, it produced numerous splendid images and was shown by JPL to have interferometric capabilities. Seasat’s important interferometric results were well exploited by the JPL team, even if they were obtained with just three serendipitous images. It was followed by the important, interferometric, shuttle-based Spaceborne Imaging Radar (SIR) missions: SIR‑A (1981, analogue), SIR‑B (1984, digital), and SIR‑C (1994, multiband L, C, X, in cooperation with DLR and ASI). In these cases, the orbiting radar deliberately imaged the same areas on two consecutive days, showing the predicted interferograms and allowing the development of satellite SAR interferometry, i.e. InSAR. The many important results obtained with Seasat (not interferometry, though) had meanwhile persuaded ESA to consider a satellite for Earth resources, to be named ERS‑1, hosting many instruments including a C-band radar. The choice of the C‑band was mostly due to the availability of the technology and its suitability for observations of sea waves and ice. In fact, interferometry was not considered at all in the planning.

2. Sar→in Milan 2.1 Phase Preserving Focusing

Meanwhile, in Milan, we had developed interferometry algorithms to a satisfying extent. Our studies of SAR had started in 1983 when Fulvio Marcoz and Alfonso Farina of Alenia proposed that our department participate in a European research project on SAR, named Adaptive Real-time Strategies for Image Processing (ARTS‑IP). Alenia had a good experience with radar; in Milan, together with Claudio Prati who had just graduated, we had experience in signal processing and our colleague Maria Giovanna Sami, who managed the team, was an expert in digital design. A team at Imperial College London also participated in the research. It is interesting to note that the SAR system we wished to design at the time does not exist even now, even if it starts being considered. The idea was to design a naval search machine: the onboard antenna would squint forward to image ships at low resolution. Then, an onboard focusing system would focus data in realtime; adding fantasy to science, we proposed to have a realtime pattern recognition system (the task of the Imperial College team) to detect ships. After, the very same antenna was supposed to be squinted aft to zoom in on the detected ships and finally downlink the result. We learned that SAR was a complex system, and that paper satellites were not adequate. However, we also learned that SAR technicians, owing to their realtime habits, had their own approximate way to focus SAR data. We

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proposed to focus the data as geophysicists did, i.e. using the exact transfer function that could even be written in a closed formula (Cafforio et al., 1991), the ω−k approach. Among other things, I had developed this algorithm for the Italian oil company AGIP back in 1977. The technique is efficient, but as the appearance of a SAR image would not change much, at the time the innovation was not considered essential. However, as it is precise and phase preserving, the technique proved to be an important tool for advancing interferometry.

2.2 Interferometry in Milan

In autumn 1987 I was on sabbatical at Stanford, and together with Claudio Prati visited JPL, at the invitation of Jack Curlander, to inform them about the ω−k focusing technique, which was new to them. In exchange, Richard Goldstein treated us with a talk on interferometry, and he showed us the interferograms that he had obtained with Seasat. His attention was mostly devoted to Phase Unwrapping. He had noticed that any 2π phase terrace added to the topography would be invisible, as it would correspond to just one cycle of the interferogram and thus disappear. However, at the beginning (source) and the end (sink) of that terrace, traces are left in the phases that look like positive or negative charges, just like the of the Cheshire cat remains even after the cat has gone. Once we had traded presents, we gave the JPL team our ω−k code, and they gave us two tapes, one containing the raw data of the three available interferometric Seasat passes, and the other with airplane interferometric data. Claudio got good interferograms in a few days. Using their approximate focusing methodology, phase preservation was hard to get and their interferograms were noisier than they should have been. Using instead the phase-preserving ω−k technique, we got our first, very clean, Seasat interferograms of Death Valley in California, which we used on our Christmas cards in 1987. Not in JPL indeed, but interferometry was rather new to others. At a seminar at the Canada Centre for Remote Sensing (CCRS) in June 1988, at the invitation of Keith Raney, I discussed ω−k focusing, phase preservation and interferometry. I had hoped to trade presents as we had done in JPL, but the audience, composed of people who were in charge of designing the forthcoming Radarsat‑1, was not as favourable to interferometry. One participant voiced the idea of storing only the moduli of the images, as it appeared that the Radarsat data would be too bulky to store them all. I did my best to show that phases were even more important, as our interferograms showed. Eventually, Radarsat‑1 archived complex values. After the launch of ERS‑1, and the take-off of interferometry, a paper describing an alternative phase-preserving focusing system, named extended chirp scaling, was published by K. Raney, R. Bamler and others who had developed it in 1992−94. Returning home to Milan in 1988, I started to look for funds to develop the technology. Until then, we had been funded only by the initial European contract. Then, thanks to Livio Marelli, ESA gave us our first contract to write a code for focusing ERS‑1 SAR data using the ω−k technique. At the time, we were trying to convince ESA to store what would later be called the Single-Look Complex (SLC) images, and Livio also tried to help us to convince the (ASI) of the validity of the ω−k technology. I remember a meeting on that with Claudio Prati, Livio Marelli, Mark Doherty, L. Guerriero, the pro tempore president of ASI, C. Albanesi and G. Milillo. Even Mark’s vivacious support could not gain us any funds. With hindsight, we were lucky: we will always thank ESA people for their quest for innovation and precision, and for fostering competition with other capable European teams. Further, the ω−k technology was very slow to be accepted by others; our first paper on ω−k focusing was submitted to IEEE Trans. Aerospace Electronic

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Systems, and duly rejected, and was then presented at the International Geoscience and Remote Sensing Symposium (IGARSS) meeting in Vancouver in 1989. But later, at the Washington IGARSS meeting in 1990, I remember a very enjoyable talk on SAR focusing with Richard Bamler and Helmut Runge; they understood the ins and outs of the question, and together we started discussing and proposing the phase preservation tests that would thereafter characterise all ESA processors, making focusing transparent to interferometry users. The paper by Bamler comparing ω−k and range Doppler focusing systems appeared in 1992 (Bamler, 1992). Chirp scaling also followed, as noted above. But we were still short of funds, and desperately needed another contract, and it could only be from ESA and on something new that could not be anything else than interferometry. Stefano Bruzzi was approached and was finally convinced to give us all the information we needed to be able to simulate the raw data from ERS‑1, yet to be launched, and a contract. So, by the end of the commissioning phase, in the summer of 1991, we were ready.

2.3S ER -1 interferometry

We received the first images from ERS‑1 on 25 August 1991, a few weeks after launch., and less than a week later (I remember recalling Claudio and Andrea Monti Guarnieri, who had recently joined the group, from their summer vacations) we produced the very first interferogram of the Gennargentu area of Sardinia, Italy, which is usable even today (Fig. 1). ERS‑1 interferometry was born, and it was full of surprises. The first surprise was the coherence between the two images forming the interferogram, as we called it, again following the geophysicists. It tells about the mechanical stability of the imaged targets in the time interval between the takes. We gave it its geophysical name, as we had no other precedent for SAR. In the US, the same thing is still at times bravely called the modulus of the complex correlation coefficient, notwithstanding the word count cost. The coherence proved to be complementary to image amplitudes: restless water is always black, especially on windy days, when its radar reflectivity is high and is therefore bright in the amplitude images. Changing vegetation and

Figure 1. The Gennargentu data (August 1991). Left: amplitude; right: interferogram. The Su Gurruppu gorge is visible at top right.

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forests were dark, towns bright, and so on, adding an invaluable new tool for researchers who wanted to classify radar images. Luckily enough, the revisit interval was just three days, and the coherence very high. The second surprise was the precision with which we could estimate the baselines, and the first, indeed approximate, DEMs. We discovered in the data (no Google then) and later on maps, the Su Gurruppu gorge and the Grotta del Bue Marino in Sardinia, and then the Patagonia and the Norway data, the fjords and the shores, and so many views of the Etna and of the Vesuvius, at all times. We derived the baselines from the data themselves, as the orbital data were not available and not precise enough. We were able to tell the timing of the orbit corrections as we could measure from the interferogram DEMs the changes in the baselines in successive passes and see their parabolic behaviour, just like a bouncing ball. Not gravity pull but solar wind push, not ground bounces but orbit corrections: anyway, intersections of parabolas.

2.4 Fringe Meetings and the Tandem Experiment

Steve Coulson at ESRIN got really interested, and so he immediately convened a meeting in Frascati in autumn 1991, to present the results that were so easy to get using the splendid ERS‑1 data. The need for subpixel coregistration and the cove of information to be found in single-pixel phases were hard to accept to people used to combining many pixels to evaluate their amplitudes for speckle reduction (Prati et al., 1990, 1994; Prati & Rocca, 1993, 1994). With hindsight, we can see that the perfection of the instrument, designed by Dornier, then Astrium, made our task much easier. The long-term stability of the local oscillator, so necessary for us beginners, was much better than needed, as it was necessary only during the formation of the synthetic antenna and during the revisit time. Now we know that the millimetre stability of parts of the solid world allows for the correct parameterisation and estimation of the rather few unknowns, be they orbital corrections, exact oscillator frequencies, atmospheric biases or, in the modern multi-baseline tomography, vertical forest structures or slow motions of the targets. We just need an energy source and a crude receiver, as parasitic interferometry would show, and astronomers using very-long-baseline interferometry know so well. Immediately we saw that in quite a few places the coherence became too low even after a few days, so that, when we entered the 35-day phase after the long geodetic interval and the initial three-day revisit interval, we knew that something new and different was needed. On the other side of the ocean, SRTM was being conceived to be carried out much later, in February 2000. Bistatic, single-pass interferometry, as noted above, would be a cure for atmospheric biases, but that cure would be expensive and hard to get. Looking to the forthcoming launch of ERS‑2 in 1995, during that first meeting on interferometry convened by Steve Coulson in Frascati in October 1991, we proposed what was later called the tandem experiment, namely, the imaging of the same scene with the two satellites in rapid sequence in order to create highly coherent interferometric pairs. The multiplicity of passes would compensate for atmospheric delays, small to start with as the delay was supposed to be short. Being unfamiliar with the real problems of orbit engineering, we would have liked some sort of temporal tomography, to be able to change progressively the revisit interval from a few minutes to a few days, in order to elucidate the mechanics of the scatterers. But this was still science fiction, as was our initial radar programme, although it still would be an interesting experiment, maybe doable with a geosynchronous machine. Anyway, the joint use of the two satellites would have many applications, and a meeting in Frascati in early 1995 recommended to ESA the experiment that was indeed carried out, but only for a nine-month period in 1996, due to budget limitations. This splendid data set has been exploited again and again.

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Figure 2. The first ERS‑1/ERS‑2 tandem interferogram, central Italy, May 1995. (ESA/POLIMI)

The DEMs that could be derived using multiple baselines to solve unwrapping problems as well as atmospheric biases would be comparable with and, some believed, exceed in quality those yielded by the much more expensive SRTM, to be carried out five years later. There were doubts, voiced by an eminent researcher, that the two local oscillators and the different orbits could create unwanted unacceptable fringes. They did, but it was an easy correction to carry out. Further, the lifetime of ERS‑1 could be impaired if it had to follow ERS‑2 (maybe the same, one said). But the experiment was carried out successfully, and the very first joint interferogram was published on the ESA website in May 1995, a few days after the end of the Commissioning Phase of ERS‑2. Our only problem was to adapt for different pulse repetition frequencies (Fig. 2), but the fact that tandem operation was not easy was confirmed by the following comments that appeared in an ESA Bulletin (Duchossois & Martin, 1995; emphasis added): ‘The passes of the two satellites over the ground receiving stations last about 10 min. The reconfiguration of the stations between the end of one satellite’s pass and the beginning of the pass of the next takes about 15 min; since the is approximately 100 min, the time interval between the two satellites has to be between 25 and 75 min. With the current orbital configuration, ERS‑2 follows ERS‑1 with an approximate delay (called the orbit phasing of the two satellites) of 35 min. Because of this delay and Earth’s rotation, the ground-track patterns of ERS‑2 are shifted westwards with respect to those of ERS‑1. The orbit phasing has been adjusted to ensure that ERS‑2’s track over the Earth’s surface coincides exactly with that of ERS‑1 24 h earlier. Within the repeat cycle of 35 days, the opportunity to observe any point on the ground under identical conditions (altitude, incidence angle, etc.) is therefore doubled during the tandem operations. Different ground-site revisiting intervals can be achieved by changing the orbital phasing of the two satellites.’

2.5 Interferometric Applications

One of the most important applications of the tandem dataset was forestry. The data were exhaustively studied by several experts: Jan Askne of Chalmers University of Technology in Sweden, and his students Patrick Dammert and Maurizio Santoro, and then Lars Ulander, from the CARABAS team who

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independently rediscovered the ω−k focusing technique in 1987; Urs Wegmüller and Charles Werner (Wegmüller, 1997) who used Gamma, the symbol of coherence, as the name of their startup company in 1995; Christiane Schmullius and her team; and many others within the splendid SIBERIA activity who were able to exploit the tandem dataset to the full (Schmullius et al., 1999). In 2000, SRTM came and the results, published after quite a while, were very satisfactory. The SRTM DEM is used by all of us. Meanwhile, important results on Phase Unwrapping were published by Dennis Ghiglia (Ghiglia & Romero, 1996) and then Mario Costantini (1998). Costantini used minimum flow methodologies for PU, and minimised the global average height (the cost of the flow of the charges from sources to sinks) of the walls of the terraces to be added to the DEM to make it as continuous as possible. As the height of these walls corresponds to an integer number of altitudes of ambiguities (the topography change corresponding to one fringe), it is invisible from the data as to add it to the DEM does not change the interferogram phases. The minimum flow methodology is now well known and is used in many codes. Further, the great effort of Michael Eineder and his team to unwrap and create the DEMs from the SRTM X‑band data using multi-baseline and ascending/descending views should also be remembered. We obtained further results in interferometry in 1994 by introducing the wavenumber shift paradigm, i.e. showing that centre frequency and incident angle played complementary roles. By using multipass observations with different baselines one could achieve wider range resolution, if the targets were stable. The quest for stable targets would then lead to the selection of permanent scatterers, to be seen later. Again using the wavenumber shift paradigm, we introduced ScanSAR interferometry, if the azimuth bursts could be spatially synchronised in order to have Doppler spectrum overlap (Gatelli et al., 1994). Beautiful examples were provided, thanks to the efforts of Betlem Rosich (Rosich & Laur, 1996). Results on real data were given by DLR and CCRS in 1999, and then, with ESA−ESOC working hard to achieve synchronisation, beautiful examples could systematically be provided with Envisat, yielding interferograms 400 km wide that proved very useful for seismological applications (Fig. 3). The operational mode of Sentinel‑1 is Terrain Observation by Progressive Figure 3. The 2011 Japan earthquake. Scans (TOPS), a slightly different and more efficient sort of ScanSAR that An example of ScanSAR interferometry we proposed later. Using once again the same paradigm, we proposed obtained with ALOS PALSAR data. (© JAXA/ interferometric quick looks and ERS‑2/Envisat interferograms, compensating METI) for the 30 MHz frequency change with a −2 km baseline. We introduced what is now called Cross-Interferometric SAR (CInSAR). The large baseline allows very small altitude ambiguities and therefore very good DEMs of flat areas such as coastal plains and marshes (Fig. 4). Meanwhile ESA provided ever more products for interferometry: SLC images and a catalogue of normal baselines were made available to improve services. Two important papers on SAR interferometry were published (Bamler & Hartl, 1998; Rosen et al., 2000) just in time for SRTM. Ramon Hanssen’s book Radar Interferometry (and atmospheric effects) was published in 2001, and our ESA Manual on InSAR Principles, written together with Didier Massonnet, was published in 2007 (Ferretti et al., 2007). With regard to the scientific audience (following Harzing’s Publish or Perish rule), by September 2011 the papers by Goldstein et al. (1988) on 2D phase unwrapping, the one by Massonnet et al. (1993) on the Landers earthquake, and ours on permanent scatterers (Ferretti et al., 2001; to be discussed later), had received a total of 850 citations. The data available were growing and growing. New satellites were available for interferometry, like Radarsat-1 and the Japanese Earth Resources Satellite 1 (JERS‑1), and all showed the familiar interferograms. Beautiful images, but then what? The impairment due to the atmospheric vapour was significant, and only the now concluded tandem mission, but carried out with a very short revisit interval, could provide a cure.

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Figure 4. Digital Elevation Model of the Po delta, Italy, an example of cross interferometry between ERS‑2 and Envisat; area covered 50 × 30 km. (From Santoro et al., 2008; ESA/Gamma Remote Sensing AG)

2.6 Differential Interferometry: single pass and multipass

But the best was still to come. The idea of Differential Interferometric SAR (DInSAR) had been proved by Richard Goldstein, who showed that Seasat measured vertical millimetre motion of agricultural fields in California, and surmised that this could be due to the changing humidity of the terrain. Further studies were made by the team of Laurence Gray at the Canadian CCRS. Using an airborne SAR in C- and X‑bands, with eight repeated passes, they measured the millimetre motion of corner reflectors in 1990. The results, first presented at the Progress in Electromagnetics Research Symposium in 1991, were published in 1993 (Gray & Farris-Manning, 1993). The first studies of ground motion using satellite data were carried out on the landslide in St Étienne de Tinée, near Nice, France, in 1992 (Fig. 5). Francesco

Figure 5. Detection of the landslide at St. Étienne de Tinée, France, 1992, using SAR interferometry and modelling. (From Fruneau et al., 1996)

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Parizzi and Bénédicte Fruneau, then a PhD student with Josè Achache, but for a time in Milan, observed the first landslide fringes in February 1992. They had just three images. Then the 3‑day repeat phase in winter 1992 provided the data needed to separate the topographic from the motion fringes. The atmospheric effects were reduced by the fact that the motion was local. The splendid interferogram of the co-seismic motion of the Landers earthquake in California, published by Massonnet et al. (1993, 1994; see Fig. 6) generated considerable interest among seismologists. That paper was exemplary, in that not only was the interferometry part significant but also the seismological analysis was very detailed. Co-seismic motion indeed proved to be easy to see, and many other examples followed. But glacier motion was also made visible as the many beautiful examples by Gilles Peltzer and Lawrence Gray showed. But then came the real questions: ok for co-seismic motion, glaciers, etc. but how about pre-seismic motion? Is it there, can we see it? Where are the signatures of the faults in motion? Could this information be useful for understanding earthquakes?

3.e Th Bonn Experiment Figure 6. The interferogram yielding the co-seismic signal of the Landers The first experiment to measure millimetre ground motion from a satellite was earthquake, 1992. (Massonnet et al., 1993) the Bonn experiment, a collaboration between ESA, Milan and the University of Stuttgart team led by Philip Hartl and Karl-Heinz Thiel. We proposed to install 18 Corner Reflectors (CRs) along a line in February–March 1992, and then to move one or two by a couple of centimetres. In a blind experiment, we would tell, from the satellite data, which ones had been moved. The final meeting was in Milan in May 1992, and Steve Coulson was the umpire. The results were clear; the two CRs that had been moved were identified and their motion established. As Steve said, it was not just a success for these two; the fact that the other 16 were seen not to have moved had been a result too. The experiment was not that easy. To position so many CRs in a short time had been difficult enough. Even worse, a thunderstorm eradicated most of the corners, so that the duration of the experiment was noticeably reduced. Nonetheless, we had the proof that DInSAR did work.

3.1 Corner Reflectors Everywhere?

But without CRs what to do? First, install as many as you can, we thought. At the time, people were happy if they could see their CRs, whereas now they become anxious if they move by more than a millimetre! So, we initiated a study for the EU with Jose Achache and Hartl, and we placed a few CRs in the Pozzuoli Solfatara and on Mount Vesuvius near Naples. We were helped by the late V. Murino, from the University of Naples. But too many other Neapolitans were too curious and liked to touch the objects; at times, they liked them so much they carried them away. In fact, we got nothing out of these efforts. The CRs were visible for a few months and then either they moved or disappeared altogether. Germany is indeed far from Naples, and the Bonn experiment had a short life anyway. Then, expecting strange and unguarded objects to remain untouched for a long time appeared to be, and still is a very difficult problem to solve, as Ramon Hanssen at TU Delft found out later in the much quieter Netherlands. So, we began to feel the need for natural persistent or Permanent Scatterers, as we later called them, a long time before we found them.

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3.2 Long-term Interferometry

Meanwhile our team had been joined by Alessandro Ferretti who had written his PhD thesis on multi-baseline DEMs (Ferretti et al., 1999). It was nice, it worked, but the coherent combination of multiple datasets with multiple baselines was a hard problem to solve. For one, we had to compensate for atmospheric biases. We were not alone in that research: we would be competing with a research group from the National Research Council (CNR/ IREA) in Naples, coordinated by Riccardo Lanari. Many more groups followed and, as all research can and should be replicated, that was what they did. Our two groups followed two different tracks that are now merging. The IREA team was more interested in spatial continuity and, due to the vagaries of the baselines of ERS satellites, could not find coherence in temporally contiguous datasets if the baselines were too far apart: the coherence on the distributed scatterers was lost due to the wavenumber shift. Thus, the total baseline span had to be divided into groups of Small Baselines (SBAS) so to create space continuous interferograms, but then the different baseline groups would be temporally discontinuous. A smooth motion could be identified by the joint analysis of the complete set, optimising smoothness using singular value decomposition. They started publishing on this topic in 2000. In Milan, we followed the other track of time continuity, and thus space discontinuity. We looked for points that were there all the time, natural CRs, which we named Permanent Scatterers (PSs). These points did not change, or changed in a predictable way the phase and amplitude of their reflection with time or baseline. Thus, we could use them in all the interferograms as pivots on which to determine the local atmospheric bias (the Atmospheric Phase Screen, APS). If their spatial density was high enough, we were able to find the APS, peel it and then find many more PSs. The problem was to find the first ones, and this we did by looking into their amplitudes, independent of the APS: if the phases are stable, the amplitudes should be as well, shouldn’t they?

3.3e Th First Results

By the end of 1998 we had obtained many quantitative results of the PS technology, over the Phlegraean Fields (subsiding), Naples (subsiding), Vesuvius (not moving), Etna (cooling), and then Ancona, where there had been a gigantic landslide a few years ago and the terrain was still sliding, down mostly, but up in places. This we found out, and we were comforted that it was not a mistake as Alfredo Mazzotti and the municipality provided us with historical data and levelling analyses. Before any publication, ESRIN set up a video conference to inform the key Earth observation value-added companies in February 1999. Anyway, we were able to patent the concept for our school in May 1999. In the following months, we recognised the incredible stability of the terrain, and that millimetre motions could be measured so easily and so accurately across seismic faults, landslides and buildings in motion, even though none of the imaged objects was marked with a precise reference point. Maybe this was not a killer application, but it was a way to use remote sensing to find things that were not visible to bystanders. We were competing with the ancient art of optical levelling, but our advantage was that, thanks to the ERS archives, we could see things that had happened in the past, and no one else could. At IGARSS ’99 we published the results of a study of Pomona, California, where we could see the subsidence due to water extraction and, for the first time, a fault in motion (Fig. 7). Then we processed results for the Los Angeles basin (Fig. 8), and we started seeing faults moving everywhere. The competition with GPS was rapidly followed by a fruitful cooperation as the two systems are complementary: GPS is absolute, space discontinuous, time

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Figure 7. Subsidence in Pomona, California. The blue uplift (top left) is due to a moving fault. (Ferretti et al., 2000)

Figure 8. Subsidence in the Los Angeles basin. (TRE)

continuous and regional, while DInSAR is relative, space continuous, time discontinuous and local (Ferretti et al., 2000, 2001). The extraction of information from a set of coherent points was also proposed by Stefania Usai, from TU Delft, at IGARSS ’99. Following on from her work, a significant research group was formed in Delft, headed by Ramon Hanssen who had spent part of his PhD studies with Howard Zebker at Stanford from JPL since 1995. Another interesting result was obtained for the disaster of Via di Vigna Jacobini in Rome in December 1998, when a building had collapsed because too heavy equipment had been installed without sufficient prior engineering analysis, as the judge later said. We could see the building going down, and down, until it collapsed. The experiment, made in February 1999 to prove that we were actually looking at that building, was carried out in cooperation with Fabrizio Ferrucci, then of the Italian Civil Protection Department, who mobilised the assistance of Roman firemen, Francesco Posa of the University of Bari who gave us three gigantic CRs to position on the ground, and also Marco Anzidei of the Istituto Nazionale di Geofisica e Vulcanologia (INGV), who used GPS technology to locate the reflectors. Indeed, we had been watching the building before it collapsed.

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Figure 9. Italy’s Permanent Scatterers from ERS ‑1, ERS‑2 and Envisat data. (TRE)

After that, the technologies of PS, SBAS, and that of interferogram stacking proposed by Sandwell & Price (1998) provided many results all over the world (Fig. 9). ERS, and then Envisat and Radarsat‑1, provided so many datasets that all researchers could fulfil their subsidence dreams.

4. Fringe Meetings, PSIC4 and the Market

Steve Coulson and then Yves Louis Desnos contributed to interferometry by organising Fringe Meetings in 1991 and 1992 (both in Frascati), and then in Zurich in 1996, Liège in 1999, and in Frascati every two years after 2003. Each meeting was attended by a couple of hundred people, 150 papers or posters, and lots of interaction. The last one, in September 2011, attracted 320 participants. The main topics discussed at the Fringe Meetings included Phase Unwrapping, conventional DInSAR, DEMs, new missions, etc., but after 2003 there were more papers related to PS-like algorithms. It was then decided, in agreement with ESA, that the growing family of second-generation DInSAR algorithms aiming at exploiting natural radar targets would be referred to as Persistent Scatterer Interferometry (PSI). Also during Fringe 2003, with Richard Bamler, we proposed to establish a comparison of different PS techniques: the PSI Codes Cross-Comparison and Certification (PSIC4); see http://earth.esa.int/psic4/). Although it would be unfair to say that the commercial exploitation of SAR interferometry started with the PS technique, PSI was a boost for space geodesy and created a new market sector for of SAR data distribution companies eager to find new civilian

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applications of radar data, which is still used primarily for intelligence and security applications. ESA had supported the creation of a market for PSI applications from the beginning and financed several projects based on PSI products and services. Among them, it is worth mentioning Terrafirma, an ESA GMES project, managed by Fugro NPA, which aims to create ‘a pan-European ground motion hazard information service’ (www.terrafirma.eu.com). In order to speed up the expansion of PS applications, ESA focused on two key concepts, validation and standardisation, neither of which turned out to be at all easy to reach. The former is needed to fulfil the need to involve different scientific communities and create a common language for comparing results. The latter is needed for the simple reason that this time there was indeed a market (!) for this technology, and differentiation rather than standardisation is a must in any marketing plan. Looking back over the last 10 years, both lines of action by ESA were successful, since they forced all actors involved in PSI to improve the quality and reliability of PS results, figure out any possible applications of PS data, and learn how to speak in layman’s terms about this remote sensing technology. We conclude this section by pointing out, once again, the importance of SAR data availability and data acquisition policies for the development of SAR interferometric technologies/applications. All PSI algorithms owe much to ESA’s ERS missions. The ERS‑1 and ERS‑2 satellites were uncomplicated and manageable using simple acquisition policies. Since interferometric data stacks were available over many areas of the world, with dozens of image acquisitions, the development of the second-generation DInSAR algorithms became possible. The existence of an up-to-date archive of speculative scheduled raw data acquisitions has helped to build the confidence of the user community in using InSAR. Also, the market potential of speculative scheduled SAR has encouraged satellite owners and distributors to become interested in such programmes.

5. Today and Tomorrow

The current constellations of X-band satellites, the future Sentinel‑1 A and B, the Argentinean L-band system, the Chinese S-band ones, etc., are all systems that will offer interferometry in the short and the long term. The key parameter, the revisit time, will shorten progressively and geosynchronous systems have been proposed to reduce that interval to 12 h. Maybe their time will come too. Indeed, observations of dykes, oil and gas fields (Fig. 10) and cities will continue and the creation of worldwide DEMs will improve. The mechanisms of interferometry are now well known, and indeed the next steps, polarimetry and tomography, also very well understood. Lower-frequency radiation like P-band will penetrate the targets and multi-baseline systems will be able to create cross-track resolution, thus allowing the retrieval of the vertical structure of forests, for example.

Figure 10. Surface deformation of a gas field.

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The future will tell if systematic global monitoring will become operational, but there are many indications that it will. Microwaves will do their job illuminating targets that beams of light cannot penetrate, and scientists will find other interesting topics to study. The adventure of interferometry as a science is basically concluded, and it is now an industrial and commercial engineering activity.

R eferences

Bamler, R. (1992). A comparison of range-Doppler and wave-number domain SAR focusing algorithms, IEEE Trans. Geosci. Rem. Sens. 30(4), 706−713. Bamler, R. & Hartl, P. (1998). Synthetic Aperture Radar interferometry, Inverse Problems 14, R1–R54. Cafforio, C., Prati, C. & Rocca, F. (1991). SAR data focusing using seismic migration techniques. IEEE Trans. Aerospace Electron. Syst. 194−207. Costantini, M. (1998). A novel phase unwrapping method based on network programming. IEEE Trans. Geosci. Rem. Sens. 36(3), 813–821. Curlander, J.C & McDonough, R.N. (1991). Synthetic Aperture Radar: Systems and Signal Processing. Wiley, New York. Duchossois, G. & Martin, P. (1995). ERS‑1 and ERS‑2 tandem operations. ESA Bulletin 83, 54–60. Engdahl, M., Borgeaud, M. & Rast, M. (2001). Use of ERS-1/2 tandem interferometric coherence in the estimation of agricultural crop heights. IEEE Trans. Geosci. Rem. Sens. 39(8), 1799–1806. Ferretti, A., Monti Guarnieri, A., Prati, C., Rocca, F. & Massonnet, D. (2007). INSAR Principles: Guidelines for SAR Interferometry Processing and Interpretation. ESA TM-19, European Space Agency, Noordwijk, the Netherlands. www.esa.int/esaMI/ESA_Publications/SEM867MJC0F_0.html Ferretti, A., Prati, C. & Rocca, F. (1999). Multibaseline INSAR DEM reconstruction, the wavelet approach. IEEE Trans. Geosci. Rem. Sens. 37, 705-715. Ferretti, A., Prati, C. and Rocca, F. (2000). Nonlinear subsidence rate estimation using permanent scatterers in differential SAR. IEEE Trans. Geosci. Rem. Sens. 38, 2202–2212. Ferretti, A., Prati, C. and Rocca, F. (2001). Permanent scatterers in SAR interferometry. IEEE Trans. Geosci. Rem. Sens. 39(1), 8–20. Franceschetti, G. & Lanari, R. (1999). Synthetic Aperture Radar Processing, CRC Press, Boca Raton, FL. Fruneau, B, Achache, J. & Delacourt, C. (1996). Observation and modelling of the Sant- Étienne-de-Tinée landslide using SAR interferometry. Tectonophysics 265, 181–190. Gatelli, F., Monti Guarnieri, A., Parizzi, F., Pasquali, P., Prati, C. & Rocca, F. (1994). IEEE Trans. Geosci. Rem. Sens. 32, 855−865. Ghiglia, D.C. & Romero, L.A. (1996). Minimum Lp-norm two-dimensional phase unwrapping. J. Opt. Soc. Am. A 13(10), 1−15. Goldstein, R., Zebker, H. & Werner, C., (1988). Satellite radar interferometry: two dimensional phase unwrapping, Radio Sci. 23(4), 713–720. Graham, L.C. (1974). Synthetic interferometer radar for topographic mapping. Proc. IEEE 62(6), 763−768. Gray, A.L. & Farris-Manning, P.J. (1993). Repeat-pass interferometry with airborne synthetic aperture radar. IEEE Trans. Geosci. Rem. Sens. 31(1), 180−191. Hanssen, R.F. (2001). Radar Interferometry: Data Interpretation and Error Analysis, Kluwer Academic, Dordrecht, the Netherlands. Massonnet, D., Feigl, K., Rossi, M. & Adragna, F. (1994). Radar interferometric mapping of deformation in the year after the Landers earthquake. Nature 369, 227−230.

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Massonnet, D., Rossi, M., Carmona, C., Adragna, F. , Peltzer, G., Feigl, K. & Rabaute, T. (1993). The displacement field of the Landers earthquake mapped by radar interferometry. Nature 364, 138–142. Pasquali, P., Stebler, O., Small, D., Holecz, F. & Nuesch, D. (1998). SAR polarimetric interferometry experiments. Workshop on Advances in Radar Methods, Progress in Electromagnetics Research Symp., Baveno, Italy, 20–22 July. Prati, C. & Rocca, F. (1993). Improving slant-range resolution with multiple SAR surveys. IEEE Trans. Aerospace Electron. Syst. 29(1), 135−144. Prati, C. & Rocca, F. (1994). Process for Generating Synthetic Aperture Radar Interferograms. US Patent No. 5 332 999. Prati, C., Rocca, F., Guarnieri, A.M. & Damonti, E. (1990). Seismic migration for SAR focusing: Interferometrical applications. IEEE Trans. Geosci. Rem. Sens. 28(4), 627−640. Prati, C., Rocca, F., Monti Guarnieri, A. & Pasquali, P. (1994). Interferometric Techniques and Applications. ESA Study Contract Report. Contract No. 3- 7439/92/ HGE-I.C. Rosen, P., Hensley, S., Joughin, I., Li, F., Madsen, S., Rodriguez, E. & Goldstein, R. (2000). Synthetic aperture radar interferometry. Proc. IEEE 88(3), 333–379. Rosich, T.B. & Laur, H. (1996). Phase preservation in SAR processing: The interferometric offset test. IGARSS 1996, Lincoln, NE, USA, May 1996. Rott, H. & Siegel, A. (1996). Glaciological studies in the Alps and in Antarctica using ERS interferometric SAR. Proc. Fringe 1996, ESA SP-406, vol. 2, European Space Agency, Noordwijk, the Netherlands, pp.149–159. Sandwell, D.T. & Price, E.J. (1998). Phase gradient approach to stacking interferograms. J. Geophys. Res. 103, 30 183−30 204. Santoro, M., Wegmüller, U., Strozzi, T., Werner, C., Wiesmann, A. & Lengert, W. (2008). Thematic applications of ERS–Envisat cross interferometry. IGARSS 2008. Schmullius, C., Holz, A., Marschalk, U., Roth, A., Vietmeier, J. & Wagner, W. (1999). Operational readiness of ERS SAR interferometry for forest mapping in Siberia. Fringe 99, ESA SP-478, European Space Agency, Noordwijk, the Netherlands. SRTM Shuttle Radar Topography Mission: Mapping the World in Three Dimensions. http://srtm.usgs.gov/ Wegmüller, U. (1997). Soil moisture monitoring with ERS SAR interferometry. Proc. 3rd ERS Symp. ESA SP-414, European Space Agency, Noordwijk, the Netherlands, pp.47-52. Zebker, H.A. & Goldstein, R.M. (1986) Topographic mapping from interferometer synthetic aperture radar observations. J. Geophys. Res. 91(B5), 4993–4999.

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→→ERSAR S over Land

SAR Image Mode

ERS SAR over Land

T. Le Toan

Centre d’Etudes Spatiales de la Biosphère, CNRS-CNES Université Paul Sabatier, Toulouse, France

1. Introduction

With the launch of the first Landsat in 1973, mapping of land surfaces from space became a reality. In the late 1970s, following increasing concerns that human activities are starting to affect the ecological balance of the planet, environmental monitoring and conservation became major objectives, to be addressed by global and repeated observations. After the short Seasat mission in 1978, it was clear that radar missions were needed to undertake measurements of areas not covered by existing missions. The first European Remote Sensing satellite, ERS‑1, launched in 1991, was designed primarily to monitor the sea state, surface winds, ocean circulation and sea/ice levels. All- weather imaging of ocean, ice and land was regarded as an additional benefit. However, the land community was very interested in using Synthetic Aperture Radar (SAR) for land observations, primarily because imaging radar was the only means available for mapping areas under persistent cloud cover and for monitoring land surface changes. To assess the use of SAR data for land applications, research was undertaken well before the launch of ERS‑1 to develop methods of land surface mapping and monitoring land surface parameters. Following the launch of ERS‑1 in 1991 and of ERS‑2 in 1995, ERS data were the basis of many scientific projects and operational services, which continued with Envisat’s Advanced SAR (ASAR) and will continue with Sentinel-1. This chapter recounts the journey from the era before ERS until today, outlining the scientific and operational achievements and their impacts on the public, and on new developments. This chapter is not intended to be comprehensive, but focuses on the use of ERS and Envisat SAR for agriculture and forest research. The observations and opinions presented here are also subjective since they represent a personal view of the history of ERS.

2. The Era before ERS

Until the 1970s, radar remote sensing research was focused on understanding geometric factors associated with SAR imaging, and on the generation of geological and geomorphological maps of cloud-covered areas. The use of SAR data for a more diverse range of land applications required an understanding of radar backscatter from natural surfaces such as soil, water and vegetation. For this purpose, ground-based scatterometer experiments were initiated by Fawwaz T. Ulaby of the University of Kansas. Between 1970 and 1984, extensive measurements were made and many innovative experiments were conducted in the USA and in Europe, most notably in the Netherlands (P. Hoogeboom, E. Attema) and in France (T. Le Toan). Other teams working on ground- based systems (active and passive) included those at the University of Bern, Switzerland (E. Shanda), the National Research Council (CNR/IREA) in Florence, Italy (P. Pampaloni) and the European Joint Research Centre, JRC (A. Sieber). In these ground-based experiments, the primary targets were agricultural fields of bare soil and various crops (e.g. wheat, corn, soybean, alfalfa, grass) that were monitored throughout the growing season. The results, published in several papers and summarised in books (Ulaby et al., 1986), indicated that at frequencies of 1–18 GHz the backscattering coefficient σ0 is sensitive to variations in soil and

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vegetation parameters. An example of the early results is shown in Fig. 1, where the backscattering coefficient σ0 (at the C-band, 20° incidence angle and VV polarisation) of bare soil surfaces for three roughness states of agricultural fields was found to be sensitive to the soil moisture content. These examples provided the first stimulus for the use of radar remote sensing for agricultural applications. In the late 1980s, European airborne campaigns were organised to collect data for diverse test sites and conditions. The first campaign, AgriSAR (1986), used the Varan-S from CNES over agricultural sites three or four times during the growing cycle. The MAESTRO-1 campaign (1989) was organised jointly by the JRC and ESA (P. Churchill and E. Attema). The NASA/JPL AirSAR was deployed to collect and evaluate P, L and C data over five European test sites, mostly forests (Churchill & Attema, 1994). The MAC Europe campaign in 1994 was also conducted over forest sites. Airborne scatterometers have been developed in Finland (M. Hallikainen) and in France (R. Bernard). The diverse scenes observed by airborne systems revealed much more complex relations between C-band data and vegetation and soil parameters than had the results obtained during ground-based studies. For bare soil under a wide range of tillage conditions, for example, it was found that the effects of surface roughness needed to be accounted for in the soil moisture retrievals (Beaudoin et al., 1990). Because of the diverse crop types encountered, it was found to be difficult to derive robust methods of crop classification or retrieval of crop parameters using single SAR measurements (in terms of frequency, polarisation and incidence angle). There was one exception, however, and that was the rice observed during the AgriSAR campaign in the Camargue, France. Because of the dominant vegetation−water interaction mechanism, rice can be monitored based on its unique temporal and polarisation signature (Laur, 1989; Le Toan et al., 1989). For forests, the campaign data using multifrequency SAR showed that C-band data had only weak sensitivity to forest parameters, and the lowest frequency (P-band) was found to be the most suitable for biomass measurements (Le Toan et al., 1992). With the growth of experimental databases, important progress in modelling was achieved, leading to a significant expansion of our capability to interpret radar signals. A simple cloud model gave a first key to understanding the σ0 dependence on the main soil and vegetation parameters (Attema & Ulaby, 1978), while more complex models simulated σ0 using radiative transfer models (A.K.

Figure 1. Measured backscattering coefficient σ0 (left scale) as a function of soil moisture content for three surface roughness states. The solid curve is the reflectivity (right scale) calculated on the basis of dielectric measurements. (Le Toan, 1982; Ulaby et al., 1986)

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Fung, University of Kansas) and random medium models (J.A. Kong, MIT). Modelling studies were later undertaken by teams at the University of Rome Tor Vergata, Chalmers University of Technology in Sweden, and the CESR (now the Centre d’Etudes Spatiales de la Biosphère (CESBIO) in France. In parallel, several European teams intensified their activities devoted to SAR image processing, including those at the Marconi Research Centre in the UK (S. Quegan), the University of Stuttgart in Germany (P. Hartl), and at CESR (A. Lopes, R. Touzi) and IRESTE in France (J. Saillard, E. Pottier), to name but a few. Their achievements included the development of methods for calibration, speckle filtering, and geometric and radiometric correction. SAR polarimetry, first demonstrated in mid-1980s, was capturing the interest of an increasing number of researchers. However, beyond a few applications, polarimetry for land did not lead to the full development of applications as many had predicted. In contrast, interferometry, which in the mid-1980s had been regarded as a tool only for topographic mapping, became an indispensable observation technique for many land applications (see Rocca, this volume). It is interesting to note that in 1981, before the era of SAR satellites, the first International Geoscience and Remote Sensing Symposium (IGARSS) was devoted to radar remote sensing, and attracted over 400 participants from 20 countries.

3. The ERS years: research and operational results

Although ERS‑1 was designed for observation of ocean processes and polar ice regions, the use of SAR data for land observations soon attracted a large number of scientific projects, most of which involved studies of renewable resources such as agriculture and forests. In 1998, about 39% of ESA’s Announcements of Opportunity (AO) projects were devoted to oceanography, sea ice and ice sheet analysis, 9% to studies of atmospheric processes, 40% to renewable resources and environmental monitoring in land applications, and 11% to geology and hazards (Rémondière & Laur, 1998). The research on the use of SAR for land applications has been reported in various symposia and workshops, and in most remote sensing journals. The results of the ERS AO investigations were presented at ESA’s international symposia and workshops, from the first in Cannes in 1992, to the most recent Living Planet symposium in Gothenburg, Sweden, in 2010. The first symposium dedicated to land applications, on the retrieval of bio- and geophysical parameters from SAR data, was held in Toulouse in 1995 (T. Le Toan), and the series of meetings continued in Noordwijk, the Netherlands, in 1998 (M. Borgeaud), Sheffield, UK, in 2001 (S. Quegan), Innsbruck, Austria, in 2004 (H. Rott) and Bari, Italy, in 2007 (F. Mattia). It would be an enormous task to summarise all that we learned during those years about how to make use of SAR data for our environment. Instead, this chapter outlines the approaches used, the results obtained and the lessons learned in just a few research and application domains: soil moisture retrieval, forest mapping, crop monitoring and hazard mapping.

3.1 Soil Moisture Retrieval

The first ERS experiment was conducted in the Ghard area of western Morocco in 1992. It clearly showed that while irrigated and non-irrigated agricultural fields could be detected in the ERS‑1 SAR images, the relationships between the SAR signal and soil moisture are influenced by surface roughness (Fig. 2) and vegetation cover. Accounting for these effects proved to be a major scientific challenge when using single VV, 23° C-band SAR data to measure soil moisture (Le Toan et al., 1993). Meanwhile, theoretical model simulations of σ0 failed in comparison with ERS SAR data (Borgeaud et al., 1995), and the reason, we found, was the

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scale effect in in situ measurements for providing model inputs. The measured roughness correlation length with 3 m profiles differed from the measurements at the SAR spatial resolution, as found by measurements with a longer profiler (>10 m), for which a specific setup was developed (Mattia & Le Toan, 1999; Davidson et al., 2000). To assimilate the soil moisture estimates in a flood forecasting scheme, a model inversion of ERS data based on a priori knowledge of the roughness state was proposed (Bach & Mauser, 2003; Davidson et al., 2003). However, we know that for agricultural and hydrological applications the most important requirement is the data short repeat cycle. With such repeated data over a few days it would be possible to track changes in soil moisture, since the changes in other parameters (surface roughness, vegetation structure and biomass) take place at longer temporal scales. This was later demonstrated with data from the Envisat Advanced SAR (ASAR) wide-swath mode in a 3-day repeat cycle, which showed changes in soil moisture content at a coarse spatial resolution (1 km) (e.g. Vista GmBH). Overall, the lack of systematic data acquisitions was a serious limitation on the use of ERS (and later Envisat) SAR data for estimating soil moisture for applications such as agricultural monitoring and hydrological modelling. For global observations, methods based on change detection using dense time series and coarse-resolution data such as ERS scatterometer data had already been developed and used in process models (W. Wagner). More recently, the Soil Moisture and Ocean Salinity mission (SMOS) mission was launched in 2009 for global soil moisture monitoring (Y. Kerr). But for many applications in agriculture and hydrology, which require higher spatial

Figure 2. Left: ERS‑1 image of the agricultural area of Gharb, Morocco. Irrigated and non-irrigated fields, and fields where irrigation was under way can be identified. Right: ERS backscattering coefficient as a function of soil moisture for fields at two types of roughness status (old and new fallow bare fields of the same rms height s with different surface correlation lengths, l). (Le Toan et al., 1993, in Massonnet & Souyris, 2008)

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resolution, expectations are now focused on the Sentinel-1 mission, which will provide an opportunity to put to effective use the knowledge accumulated and methods developed so far (Satalino et al., 2003; Balanzano et al., 2011).

3.2 Forest and Biomass Mapping

Among the ERS projects on renewable resources, the most popular theme was forests. By that time, it had been recognised that deforestation in tropical regions was contributing to increased global carbon emissions. The SAR has proved itself to be the only realistic source of geographical information on tropical forests, where the mean annual cloud cover can reach 90%. Mosaics of ERS images have provided unprecedented maps of large tropical regions, such as that of Central Africa generated by the JRC as part of the ESA–EU Tropical Ecosystem Environment Observation by Satellites (TREES) project (1994). But the most remarkable example was the map of French Guiana (Rudant et al., 1997), an area that is covered by rainforest and mostly inaccessible, and for which, except for the coastline, existing maps were incomplete and inaccurate. Figure 3 shows the ERS mosaic image using 1992–94 ERS data. The mosaic image was used by IGN, the French mapping agency, to derive the first cartographic products such as land cover and topographic maps showing forested areas, clearings, agricultural fields and settlements.

Figure 3. Mosaic image of French Guiana, South America, based on ERS‑1 data, 1992–94. (J.P. Rudant)

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Quite soon after the beginning of data acquisition, it was recognised that the multitemporal ERS data provided more valuable information than single images. Methods based on time series data for speckle filtering and for change detection were developed and used for deforestation mapping (e.g. in Sumatra by CESBIO and ESA in 1998). To assess the use of SAR data for monitoring forests, the EU’s European Forest Observations by Radar (EUFORA) project (fourth Framework Programme, 1996−98) brought together several teams to work on a collaborative basis (Le Toan et al., 1998). Many of the project’s findings, including the application of multitemporal ERS data to forest mapping (Quegan et al., 2000) and methods based on SAR interferometry, are still being used. While the most striking land applications of SAR interferometry have been measurements of terrain displacements following earthquakes and subsidence monitoring, the findings for other applications have received much less attention. In the EUFORA project, for example, J. Askne and his team from Chalmers University assessed the potential of interferometry for forest applications (Askne et al., 1997). The Swiss company Gamma Remote Sensing (Wegmüller & Werner, 1997; Strozzi et al., 2000) initiated the operational use of interferometric coherence in land use and forest mapping. The coherence is a measure of the unwanted phase noise of the interferogram when dealing with terrain displacements. But as an interesting point for vegetation observations, the coherence decreases with volume scattering and temporal changes in the target area. Water and forests show low coherence, while bare soil and urban areas show high coherence, and agricultural fields intermediate coherence. Figure 4 presents one of the first examples of the simplest and most popular method of land surface mapping using backscatter intensity and interferometric coherence. Beyond forest mapping using interferometry, the next step involved the retrieval of forest parameters. The idea was simple: the volume scattering and temporal changes due to the motion of scatterers increase with the number of scatterers in a forest canopy. Hence the interferometric coherence was expected to decrease with forest biomass. Experimental results relating ERS

Figure 4. RGB colour composite of interferometric coherence (red), backscatter intensity (green), and backscatter change (blue), in an area near Bern, Switzerland. The data were obtained by ERS‑1, 24−27 November 1991. (Gamma Remote Sensing)

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repeat pass coherence at different time intervals have been reported. The sensitivity to biomass was found to decrease after a certain value of biomass was reached, e.g. 40−60 t ha−1 for a temperate forest (Le Toan & Floury, 1998), or for the Siberian forest (Wagner et al., 2003). Some exceptions were observed in boreal forests in winter conditions, where the sensitivity can remain high with biomass values up to 300 t ha−1 (Askne et al., 2003). The interferometric phase information from ERS SAR was also examined as a possible source of information to derive forest canopy heights. When the height of the phase centre in a forest canopy was compared with the canopy height (Floury et al., 1997), however, it was found that more information was needed to retrieve both the canopy height and the ground topography. This was later realised with single-pass interferometry for ground topography with the Shuttle Radar Topography Mission (SRTM) and the emergence of the polarimetric interferometry (Pol-InSAR) technique, based on Spaceborne Imaging Radar SIR-C/X-SAR data (S. Cloude and K. Papathanassiou). Building on the use of interferometric coherence and intensity, the SAR Imaging for Boreal Ecology and Radar Interferometry Applications (SIBERIA) project, led by C. Schmullius, was one of the largest remote sensing campaigns conducted in Europe. It used three satellites to acquire data for the central Siberian taiga, an area of global ecological importance. In an unprecedented joint effort involving the German DLR, ESA and the Japanese National Space Development Agency (NASDA), ERS‑1/ERS‑2 and the Japanese Earth Resources Satellite (JERS‑1) were used to acquire data over an area of more than 1.2 million km2 during autumn 1997 and summer 1998. The project generated 96 maps (100 × 100 km) of forest cover and biomass (Fig. 5), enabling Russian foresters to update their information on the status of central Siberian forests. More recently, in the ESA and the Chinese Ministry of Science and Technology (MOST) Dragon programme, coordinated by Y.-L. Desnos and Li Zeng Yuan, the archived ERS‑1/ERS‑2 tandem data were used to generate forest cover and biomass maps of China, in a forestry project also led by C. Schmullius. The SIBERIA-1 data were later used to test a process-based dynamic global vegetation model. Such models attempt to capture the best representations of ecosystem processes, estimate the global spatial distribution of carbon fluxes and, when coupled to a climate model, predict current and future carbon and water fluxes. The model results and measurements in Fig. 6 show differences in both the range and spatial distribution of biomass values (Le Toan et al., 2004). The differences were suggested to be partly the result of forest disturbance contained

Figure 5. The SIBERIA project. One of the 96 forest cover and biomass maps generated using ERS‑1/ERS‑2 and JERS‑1 data acquired over central Siberia in 1997 and 1998. (SIBERIA‑1 project, led by C. Schmullius)

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Figure 6. Measured and model-based values of above-ground biomass in central Siberia (t C ha−1). Top left: The SIBERIA-1 map degraded to pixels of 0.5°; bottom left: the results of the Sheffield Dynamic Global Vegetation Model (SDGVM). Right: Scatterplot comparing the model results and SIBERIA-1 measurements of above- ground biomass. (Adapted from Le Toan et al., 2004)

in the data but not considered by the models, and the saturation in model values as being due to incorrect assumptions about tree mortality). It was clear that the model needed to be improved by taking into account the reality revealed by such biomass maps, and that global maps that cover a much wider range of biomass than the SIBERIA-1 data are required for testing the model across all biomes. Such work paved the way for ESA’s future Biomass mission (Le Toan et al., 2011).

3.3 Rice Monitoring

As stated in section 2, agricultural crops were the first study targets for which the all-weather capability of the ERS SAR was expected to provide a unique means of crop monitoring. Many papers have been published on crop classification and on the retrieval of crop biomass using ERS data. The analysis confirmed earlier airborne campaign results showing that the C-band backscatter signal is sensitive to both vegetation and soil parameters (e.g. Picard et al., 2003; Mattia et al., 2003). It was also found that using a single SAR measurement (VV, 23°) it is difficult to decouple from the signal soil moisture, surface roughness, vegetation structure and biomass. This is the reason why few effective applications have been derived from ERS data. The only exception was inundated rice fields, where the signal is governed mainly by vegetation parameters. This is because of the dominant vegetation–water interaction scattering mechanism, which also explains why the ERS backscatter showed a significant increase (10 dB) during the plant growing phase (Le Toan et al., 1997; Wang et al., 2004). This temporal behaviour has been reported in a number of studies using ERS (and also ASAR and Radarsat) data in Japan (Kurosu et al., 1995), Indonesia (Le Toan et al., 1997; Ribbes & Le Toan, 1999), China, India, the Philippines, Thailand (Aschbacher et al., 1995) and Vietnam (Liew et al., 1998). In most rice-growing regions, where fields are small and do not have the same crop calendar, the rice mapping methods have been developed based on the unique temporal behaviour of the backscatter. Such methods have proven to be effective at a number of sites (Le Toan et al., 1997). Figure 7 shows a map of rice fields near Samarang, Java, Indonesia, distinguished into early and late rice, according to the irrigation scheme in the region. Regional projects for rice monitoring have also been pursued beyond the research stage such as for the Mekong delta in Vietnam. However, an important factor that has limited the development of rice applications has been the difficulty in acquiring systematic multitemporal SAR data during the growing season. Meanwhile, rice cultivation has raised concerns about its impacts on various environmental aspects, from water management to climate change. Rice is the primary food source for more than half of the world’s population; according to the UN Food and Agriculture Organization, the demand is likely to rise by nearly

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Figure 7. Map of rice fields (22 × 22 km) derived from multitemporal ERS data. Cyan, late rice; magenta, early rice; yellow, other crops. Region of Semarang, Java, Indonesia (6°55 S, 110°41 E). (Le Toan et al., 1997)

70% by 2025. At the same time, growing populations and the rapid pace of economic development are leading to a decline in the rice-growing area. Changes in the distribution of rice paddies and in farm management towards more intensive production systems (multi-cropping, water management, fertilisers and high-yield cultivars) are likely to intensify over the coming decades. These changes in the rice-growing area and farming practices are likely to have significant impacts on the global climate, since irrigated rice fields are among the major sources of methane emissions. Estimates of seasonal changes in atmospheric methane concentrations based on observations by the SCIAMACHY instrument on Envisat (Fig. 8) clearly show the importance of methane emissions from the rice-growing regions of Asia (Schneising et al., 2009). One of the first ESA–China Dragon projects was ‘Rice monitoring in China’ in 2003. China is the world’s largest rice producer, accounting for 32–35% of global production in 2000. This makes China the preferred country for

Figure 8. Seasonal column-averaged methane mole fraction from Envisat/SCIAMACHY in 2003. The highest atmospheric methane concentrations were observed over Asian countries during the rice-growing season. (Schneising et al., 2009)

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studies of rice production systems and their impact on global climate change. The area harvested has declined since the mid-1970s, and at the same time, the increased demand for rice has been met by raising yields and improved farming practices. China has introduced novel rice irrigation techniques that allow the paddy fields to dry out intermittently, reducing water consumption without reducing yields. The shift from continuous flooding to midseason drainage water management has resulted in a significant reduction in methane emissions from China’s paddy fields (Huang et al., 2004). Figure 9 shows the model calculation of methane emissions from rice fields based on recent statistics and input data on irrigation systems and farming practices). A major objective of the Dragon project has been to develop and improve remote sensing methods to derive model input data to refine the model calculation results. Methods using the Envisat ASAR Alternating Polarization Precision (APP) and wide-swath mode to map rice fields, rice varieties and irrigation status over a study region have been developed (Le Toan et al., 2005) to provide inputs for methane and CO2 emission models. Figure 10 shows a map of rice fields in China, where flooded rice and drained fields can be distinguished. However, the increasing interest in the Envisat data (and increased conflict data requests) in China has limited the availability of data to test the methods for the entire rice-growing season in the regions under study. To demonstrate large-scale rice mapping, studies were conducted in Vietnam in 2007, where the demand for ASAR data was much less important. Mapping rice over large areas (regional to continental scales) would require the acquisition and processing of a daunting amount of high-resolution data. Instead, the wide-swath sensors on Envisat (and the future Sentinel-1 system) have opened the way to the adaptation of these methods to medium resolution (50–100 m) mapping for regional statistical and environmental studies. Figure 11 (left) shows a map of two rice seasons in the Mekong delta in Vietnam in 2007 (Bouvet & Le Toan, 2011). The map can be compared with the salinity map of the region (right), indicating a general correlation between salt-free soil and areas under rice cultivation. However, local exceptions could be observed, either by use of irrigation water, or by change of rice into aquaculture. Studies are under way to gain an understanding of the overall environment of the Mekong delta and the impacts of human activities and climate change. Sentinel-1 will be a major source of data for such studies.

Figure 9. Distribution of methane emissions from rice fields in China, based on the CH4MOD model at the Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing; averages for the period 2001−5. (Huang Yao)

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Figure 10. Map of rice fields in Jiangsu province, China, using Envisat ASAR. Magenta: flooded rice fields; green: rice fields with mid-season drainage. (A. Bouvet)

Figure 11. The Mekong delta, Vietnam. Left: Rice map (one and two crops a year in 2007) derived from Envisat ASAR data. (Bouvet & Le Toan, 2011) Right: Soil salinity map of the same period. White, no salinity; magenta, through blue, green to red: decreasing salinity. (Southern Institute of Water Resources Research, Vietnam, 2009)

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3.4 Hazard Mapping

The demand for hazard mapping has increased due to the intensification of natural disasters that affect human lives and infrastructure, and the increasing use of risk-prone areas by growing populations. The interest is mainly in assessments of both the extent and the level of damage, which is important for the planning of protection measures. Data acquired during or after such events can be combined with archive data to produce maps showing the changes. SAR is particularly useful because of its capability to detect changes regardless of the weather during the day and at night. ERS‑1 and ERS‑2 have frequently been used for mapping storm damage to forests, for which tandem InSAR data have proved effective in detecting abrupt changes characterised by decreased coherence. For example, in the three months before the end of ERS‑1, the two satellites gathered a large volume of tandem InSAR data that was used (e.g. by Gamma Remote Sensing and Sarmap) for mapping the storm damage to European forests in December 1999. With ERS‑1 and ERS‑2, major advances were made towards the operational application of SAR data for flood mapping and monitoring. One of the first results was the mapping of 400 km of flooded banks of the River Thames in the UK in December 1992, which highlighted the capability of ERS data to provide valuable information (Blyth et al., 1993). Since then, flood products have been generated for various flood events around the world (e.g. Badji & Dautrebande, 1997; Yésou et al., 2001). During the dramatic flooding of the River Oder in central Europe in summer 1997, the capability of acquiring data over an area of more than 110 000 km2 was demonstrated (De Roo et al., 1999). In addition to the exploitation of multitemporal ERS or Envisat data, interferometric coherence has also been used to derive flood maps, such as those produced by Gamma Remote Sensing during the River Adour floods in southwest France in 2009, based on ERS‑2/Envisat tandem observations. One of the last uses of ERS for flood mapping in near- realtime was during the 2010 spring floods in Poland (Tholey et al., 2011). In 2000, ESA and CNES, the French space agency, initiated the International Charter ‘Space and Major Disasters’ to make it easier for emergency services to access satellite data in the event of natural or man-made disasters. By 2012, the number of Charter members had risen to 21. Between 2000 and 2010, the charter was invoked about 300 times, nearly half (46.5%) of them for flood events, 17% for windstorms in forests and 12% for earthquakes. The first mapping products were delivered under the Charter in 2001, in response to the flooding in the Saône River valley in France. Figure 12 shows images produced

Figure 12. The first image products delivered to emergency services under the International Charter on Space and Major Disasters, following the floods in the Saône River valley in France, 27 March 2001. Left: ERS radar map of the flooded area; centre: map of flooded areas obtained by co-registering the ERS image with a SPOT image; right: land affected by floodwaters. (SERTIT)

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by SERTIT, at the University of Strasbourg (H. Yésou), that were quickly delivered to the emergency services to enable them to assess of the extent of the flooding and plan their response activities. The images were also used in the development of plans to prevent such floods in the future. The use of SAR data for hazard mapping, starting with ERS, has undoubtedly had a significant impact on the public. With Sentinel-1, mapping of windstorm damage in forests and monitoring of the extent of floods are expected to be fully operational.

4. Conclusions

ERS‑1, launched in 1991, carried an imaging SAR and other instruments to measure ocean surface temperatures and winds at sea. ERS‑2 was launched in 1995 with an additional sensor for atmospheric ozone research. In March 2000, ERS‑1 finally ended its operations, after far exceeding its planned lifetime, and in July 2011 ERS‑2 was retired. At their time of launch the two ERS satellites were the most sophisticated Earth observation spacecraft ever developed by Europe. They collected a wealth of valuable data on Earth’s oceans, polar ice caps and land surfaces. The initial objective of land surface observations was to map cloud-covered regions. But most of the achievements have been based on the capability of the data to monitor changes in land surfaces used either for agriculture or forestry, and to monitor natural disasters such as severe flooding or earthquakes in remote parts of the world. Twenty years after the launch of ERS‑1, followed by ERS‑2 and Envisat, we have learned a great deal about how SARs see land surfaces, and how to make best use of the data for applications and science. Some unexpected techniques also emerged, such as the use of interferometry for land observations, as well as outstanding applications such as rice monitoring, and use of the data for flood monitoring operations. We have gained a better understanding of how to use Earth observation for environmental monitoring and conservation. The achievements of ERS have clearly paved the way for further investigations of how future missions can benefit both science and applications. In the future, moving from case studies to the global environment will require missions with global coverage, systematic data acquisition and short repeat cycles, coupled with the provision of long-term data. This will be achieved with the missions such as Sentinel-1.

Acknowledgements

This chapter reflects many years of research during which I have had the chance to work with and learn from many friends and colleagues. My very special thanks go to the CESR-CESBIO members who worked with me on ERS SAR research programmes: Henri Laur, Eric Mougin, RidhaTouzi, André Beaudoin, Florence Ribbes, Nicolas Floury, Ghislain Picard, Jean- Michel Martinez, Francesco Mattia, Jean Claude Souyris, Malcolm Davidson and Alexandre Bouvet.

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Acronyms and Abbreviations

Acronyms and Abbreviations

ACE Altimetry Corrected Elevation CCRS Canada Centre for Remote Sensing ADEOS Advanced Earth Observing Satellite (Japan) CCT Computer-Compatible Tape AGU American Geophysical Union CCU Central Control Unit AGW Atmospheric Gravity Wave CDN Content Delivery Network AirSAR Airborne Synthetic Aperture Radar CEOS Committee on Earth Observation ALOS Advanced Land Observing Satellite (JAXA) Satellites AMI Active Microwave Instrument CERN Organisation Européenne pour la AMSR-E Advanced Microwave Scanning Recherche Nucléaire / European Radiometer for EOS Organization for Nuclear Research AO Announcement of Opportunity CERSAT Centre ERS d’Archivage et de Traitement AOCS Attitude and Orbit Control Subsystem / ERS Processing and Archiving Facility AOD Aerosol Optical Depth (IFREMER, France) AMAS Advanced Millimetre Wave Atmospheric CESBIO Centre d’Études Spatiales de la Biosphère Sounder / Centre for the Study of the Biosphere AMF Air Mass Factor from Space (France) AMS American Meteorological Society CESR Centre d'Etude Spatiale des AMSR-E Advanced Microwave Scanning Rayonnements / Centre for the Study of Radiometer for EOS Radiation in Space (France) APS Atmospheric Phase Screen CFCs Chlorofluorocarbons ARC Active Radar Calibration CFRP Carbon-Fibre-Reinforced Polymer ARC ATSR Reprocessing for Climate project CInSAR Cross-interferometry SAR ARTS-IP Adaptive Real-time Strategies for Image CLAES Cryogenic Limb Array Etalon Processing (EU) Spectrometer ASAP Austrian Space Application Programme CLS Collecte de Localisation Satellite (France) ASAR Advanced Synthetic Aperture Radar CNES Centre National d’Etudes Spatiales ASCAT Advanced Scatterometer (MetOp) (France) ASCAT-SG ASCAT Second Generation CNR/IREA National Research Council, Institute ASI Italian Space Agency for Electromagnetic Sensing of the ATLAS Atmospheric Laboratory for Applications Environment (Italy) and Science COASTALT Coastal Altimetry Project (ESA) ATMOS Atmospheric Trace Molecule COMSS Coastal Ocean Monitoring Satellite Spectroscopy project System ATSR Along-Track Scanning Radiometer CR Corner Reflector AU Accounting Unit (ESA) CSTARS Center for Southeastern Tropical AVHRR Advanced Very-High-Resolution Advanced Remote Sensing (Miami, USA) Radiometer AWDP ASCAT Winds Data Processor DARA Deutschen Agentur für Raumfahrtangelegenheiten /German BAS Bathymetry Assessment System Aerospace Agency (now DLR) BDDN Broadband Data Dissemination Network DEM Digital Elevation Model BIRA Belgisch Instituut voor Ruimte- DEOS Delft Institute for Earth-oriented Space Aëronomie / Belgian Institute for Space Research Aeronomy DGM Delft Gravity Model BRGM Bureau de Recherches Géologiques et DIA Discrete Interaction Approximation Minières / Bureau of Geological and DInSAR Differential Interferometric SAR Mining Research (France) DLR Deutsches Zentrum für Luft- und BMFT Bundesministerium für Forschung und Raumfahrt / German Aerospace Center Technologie / German Ministry of Science DLS Dynamic Limb Sounder and Technology DNSC Danish National Space Center BUV Backscattered Ultraviolet DOAS Differential Optical Absorption Spectroscopy Cal/Val Calibration and Validation DOD Differential optical density CARABAS Coherent All RAdio BAnd Sensing DTU Technical University of Denmark CATGAS Calibration Apparatus for Trace Gas DUE Data User Element Absorption Spectroscopy

293 SP-1326

DWD Deutsche Wetter Dienst / German GeoTROPE Geostationary Tropospheric Pollution Weather Service Explorer DWS Doppler Wind Sounder GFO Geosat Follow-On GFSC Goddard Space Flight Center (NASA) EARSC European Association of Remote Sensing GFZ Deutsches GeoForschungsZentrum − Companies Helmholtz Centre Potsdam / German EARSeL European Association of Remote Sensing Research Centre for Geosciences Laboratories GHRSST Global Ocean Data Assimilation ECMWF European Centre for Medium-Range Experiment (GODAE) High-Resolution Weather Forecasts Sea Surface Temperature pilot project ECV Essential Climate Variable GIA Glacial Isostatic Adjustment EGM Earth Geopotential Model GLOBE Global Land One-km Base Elevation EGSE Electrical Ground Support Equipment Project ELDO European Launcher Development GLOMAC Global Modelling of Atmospheric Organisation Chemistry EMSA European Maritime Safety Agency GM Geodetic Mission ENSO El Niño–Southern Oscillation GMES Global Monitoring for Environment and EO Earth Observation Security EOAC Earth Observation Advisory Committee GMF Geophysical Model Function EOPAG Operation Plan Advisory Group GMT Greenwich Mean Time EOS Earth Observing System (NASA) GODAE Global Ocean Data Assimilation EM Engineering Model Experiment (WMO) EMR‑1/-2 ERS Microwave Radiometer 1/2 GOME Global Ozone Monitoring Experiment EPS Eumetsat Polar System GOMOS Global Ozone Monitoring by Occultation ERBS Earth Radiation Budget Satellite (NASA) of Stars ERM Exact Repeat Mission GOMSS Global Ocean Monitoring Satellite System ERS‑1/-2 European Remote Sensing satellites 1/2 GPS Global Positioning System ERSC ERS Consortium GRGS Groupe de Recherches en Géodésie ERTS Earth Resources Technology Satellite Spatiale (France) ESA European Space Agency GSAG GOME Scientific Advisory Group ESAC European Space Astronomy Centre GSFC Goddard Space Flight Center (NASA) (Madrid, Spain) ESCAT ERS Wind Scatterometer HALOE Halogen Occultation Experiment ESRIN European Space Research Institute HCFCs Hydrochlorofluorocarbons (Italy) HCMM Heat Capacity Mapping Mission ESRO European Space Research Organisation HIRLAM High-Resolution Limited Area Model EU European Union HLOP High-Level Operation Plan EUFORA European Forest Observations by Radar HRDLS High-Resolution Dynamic Limb Sounder project (EU) (NASA) Eumetsat European Organisation for the Exploitation of Meteorological Satellites IB Inverted Barometer EURECA European Retrievable Carrier (ESA) IDHT Instrument Data Handling and Transmission FCDR Fundamental Climate Data Record IGN Institut Géographique National / FM Flight Model National Institute of Geographic and FSLP First Spacelab Flight Payload Forest Information (France) FTS Fourier Transform Spectrometer IFARS Institute for Applied Remote Sensing FWHM Full Width at Half Maximum (Germany) FZ Fracture Zone IFREMER Institut Français de Recherche pour l’Exploitation de la Mer / French Research GCOS Global Climate Observing System Institute for Exploitation of the Sea GDR Geophysical Data Record ILAS Improved Limb Atmospheric GEO Group on Earth Observation Spectrometer GEOSS Global Earth Observation System of IM Image mode Systems IMG Interferometric Monitor for Greenhouse GeoSCIA Geostationary Scanning Imaging Gases Absorption Spectrometer for Atmospheric Cartography

294 Acronyms and Abbreviations

IMGA Istituto per lo Studio dello Metodologie MAPS Measurement of Air Pollution from Geofisiche Ambientali / Institute for the Satellites Study of Geophysical and Environmental MARISS Maritime Security Service project (GMES) Methodologies (Italy) MARSAIS Marine SAR Analysis and Interpretation IMR Imaging Microwave Radiometer System INGV Istituto Nazionale di Geofisica e MARSEN Marine Remote Sensing Experiment Vulcanologia (Italy) MASL Mean altitude above sea level InSAR Interferometric SAR MERIS Medium-Resolution Imaging IOC Intergovernmental Oceanographic Spectrometer (Envisat) Commission MIPAS Michelson Interferometer for Passive IPCC Intergovernmental Panel on Climate Sounding Change MLS Microwave Limb Sounder (Aura) IR Infrared MODIS Moderate-Resolution Imaging IREA Istituto per il Rilevamento Spectroradiometer (NASA) Elettromagnetico dell'Ambiente / MOST Method of Splitting Tsunami model Institute for Electromagnetic Sensing of (NOAA) the Environment (Italy) MoU Memorandum of Understanding IRESTE Institut de Recherché et d'Enseignement MPI Max Planck Institute (Germany) Supérieur aux Techniques de MRSE Microwave Remote Sensing Experiment l’Electronique (France) MSLA Maps of Sea Level Anomalies (Aviso, ISAMS Improved Stratospheric and mesospheric France) Sounder MSS Mean Sea Surface Ispra Institute for Environmental Protection MSSL Mullard Space Science Laboratory (UK) and Research (Italy) MWR Microwave Radiometer ISW Internal Solitary Wave IT Information Technology NASA National Aeronautics and Space IUP/UB Institute of Environmental Physics, Administration (USA) University of Bremen NASDA National Space Development Agency (now JAXA, Japan) JERS‑1 Japanese Earth Resources Satellite 1 NERC Natural Environment Research Council JGM-3 Joint Gravity Model 3 (UK) JPL PO.DAAC Jet Propulsion Laboratory − Physical NERSC Nansen Environmental and Remote Oceanography Distributed Active Archive Sensing Center (Norway) Center (USA) NIVR Nederlands Instituut voor JRC Joint Research Council (EC) Vliegtuigontwikkeling en Ruimtevaart / Netherlands Institute for Aerospace KNMI Koninklijk Nederlands Meteorologisch Programmes Instituut / Royal Netherlands NOAA National Oceanic and Atmospheric Meteorological Institute Administration (USA) NOC NWP Ocean Calibration LASP Laboratory for Atmospheric and Space NRCS Normalised Radar Cross-Section Physics (Boulder, USA) NRT Near-Realtime LASS Land Application Satellite System NSCAT NASA Scatterometer LBR Low Bit Rate NWP Numerical Weather Prediction LED Light-emitting diode NWP SAF Satellite Application Facility for LEGOS Laboratoire d'Etudes en Géophysique et Numerical Weather Prediction (Eumetsat) Océanographie Spatiales (France) LIMS Limb Infrared Monitor of the OCM Ocean Colour Monitor Stratosphere ODS Ozone-depleting substance LRIR Limb Radiance Inversion Radiometer OII Optical Imaging Instrument LRR Laser Retroreflector OMI Ozone Monitoring Instrument LRTAP Convention on Long-Range OSI SAF Ocean and Sea Ice Satellite Application Transboundary Air Pollution Facility LST Land Surface Temperature OSTIA Operational Sea Surface Temperature and Sea Ice Analysis (UK Met Office) MABL Marine Atmospheric Boundary Layer OTD Optical Transient Detector (NASA) MAC-Europe Multisensor Airborne Campaign Europe OVOC Oxygenated volatile organic compound (NASA/ESA)

295 SP-1326

PALSAR Phased Array type L-band Synthetic SAO Smithsonian Astrophysical Observatory Aperture Radar (USA) PAN Peroxyacetyl nitrate SAR Synthetic Aperture Radar PAMIRASAT Passive Microwave Radiometer Satellite SARLab Synthetic Aperture Radar on Spacelab PARSA Partition Rescale and Shift Algorithm SARSat Synthetic Aperture Radar Satellite PB-EO Programme Board for Earth Observation SASS SeaSat-A Scatterometer System (NASA) (ESA) SBUV Solar Backscatter Ultraviolet experiment PB-RS Programme Board for Remote Sensing (NOAA) (ESA) ScanSAR Scanning Synthetic Aperture Radar PFC Polyfluorinated compound SCD Slant Column Density PI Principal Investigator SCIAMACHY Scanning Imaging Absorption PIPOR Programme for International Polar Ocean Spectrometer for Atmospheric Research Chartography (Envisat) PISTACH Prototype Innovant de Système de SCIAS Scanning Imaging Absorption Traitement pour l’Altimétrie Côtière et Spectrometer l’Hydrologie / Coastal Altimetry project SCR Selective Chopper Radiometer (CNES) SDP SeaWinds Data Processor PLSO Post-Launch Support Office (ESTEC) SERTIT Service Régional de Traitement d'Image PMD Polarisation Measurement Device et de Télédétection, University of POEM Polar Orbiting Earth Observation Mission Strasbourg (France) POLDER Polarisation and Directionality of the SHOM Service Hydrographique et Earth’s Radiance (France) Océanographique de la Marine / POLIMI Polytechnic University of Milan Hydrographic and Oceanographic Service Pol-InSAR Polarimetric Interferometric SAR (France) PRARE Precise Range and Range Rate Equipment SIBERIA SAR Imaging for Boreal Ecology and PS Permanent Scatterer Radar Interferometry Applications PSC Polar Stratospheric Cloud project (EU/ESA/NASDA) PSI Persistent Scatterer Interferometry SIR Spaceborne Imaging Radar PSIC4 PSI Codes Cross-Comparison and SLC Single-Look Complex Certification SLSTR Sea and Land Surface Temperature PU Phase Unwrapping Radiometer SMAP Soil Moisture Active Passive mission QC Quality Control (NASA) QPSK Quadrature Phase Shift Keying SME Solar Mesosphere Explorer (NASA) QWG Quality Working Group SMEs Small and medium enterprises SMOS Soil Moisture and Ocean Salinity mission RA Radar Altimeter (ESA) RADS Radar Altimetry Database System SMMR Scanning Multichannel Microwave RAL Rutherford Appleton Laboratory (UK) Radiometer REAPER Reprocessing of Altimeter Products for SMR Sub-millimetre Microwave Radiometer ERS (Odin) RESPAG Remote Earth Sensing Programme SOAZ Système d’Analyse par Observation Advisory Group Zenithale RIVM Rijksinstituut voor Volksgezondheid SODETEG Société d’Études Techniques et en Milieu / National Institute for d’Entreprises Générales Public Health and the Environment SPOT Système Pour l’Observation de la Terre (the Netherlands) SRON Netherlands Institute for Space Research rms Root mean square SRTM Shuttle Radar Topography Mission ROSIS Reflective Optics System Imaging (NASA) Spectrometer SSAG SCIAMACHY Science Advisory Group RSAHG Remote Sensing Ad Hoc Group SSHA Sea Surface Height Anomaly RSPP Remote Sensing Preparatory Programme SSI Solar Spectral Irradiance SSM/I Special Sensor Microwave/Imager SAG Science Advisory Group SST Sea Surface Temperature SAGE I Stratospheric Aerosol and Gas SSBUV Shuttle SBUV Experiment I STS Space Transportation System (NASA) SAM Stratospheric Aerosol Measurement SWH Significant Wave Height SAMS Stratospheric and Mesospheric Sounder SWM SAR wave mode

296 Acronyms and Abbreviations

SWOT Surface Water and Ocean Topography UB University of Bremen satellite (NASA) UNDCP United Nations International Drug SZA Solar Zenith Angle Control Programme UNECE United Nations Economic Commission TanDEM-X TerraSAR-X add-on for Digital Elevation for Europe Measurement (DLR, Germany) UNEP United Nations Environment Programme TEC Total electron content UNESCO United Nations Educational, Scientific TIROS Television Infrared Observation Satellite and Cultural Organization Program (NASA) UNFCC United Nations Framework Convention TNO-TPD Netherlands Organisation for Applied on Climate Change Scientific Research, Institute of Applied UNOOSA United Nations Office for Outer Space Physics Affairs TMI TRMM Microwave Imager UTC Coordinated Universal Time Topex Ocean Topography Experiment (NASA/ UV Ultraviolet CNES) TOMS Total Ozone Mapping Spectrometer VLBI Very-Long-Baseline Interferometry (NASA) VC Virtual Constellation (CEOS) TOPS Terrain Observation by Progressive Scans VOC Volatile organic compound TRE Tele-Rilevamento Europa (Italy) VMR Volume mixing ratio TREES Tropical Ecosystem Environment Observation by Satellites project (EU) WAIS West Antarctic Ice Sheet TRMM Tropical Rainfall Measuring Mission WAM Wave Atmospheric Model (ECMWF) (NASA/JAXA) WSM Wide-Swath Mode TROPOMI Tropospheric Monitoring Instrument WM Wave Mode TT&C Telemetry, Tracking and Command WMO World Meteorological Organization TWT Travelling Wave Tube WVC Wind Vector Cell

UARS Upper Atmosphere Research Satellite (NASA)

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