Study on Earth Radiation Budget Mission Scenarios
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Study on Earth Radiation Budget Mission Scenarios
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i1' Autoren: R. Dlhopolsky R. Hollmann J. Muller R. Stuhlmann
GKSS 97/E/71 ISSN 0344-9629 DISCLAIMER
Portions of this document may be illegible in electronic image products. Images are produced from the best available original document. GKSS 97/E/71
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Study on Earth Radiation Budget Mission Scenarios
Autoren: R. DIhopolsky R. Hollmann J. Muller R. Stuhlmann (Institut fur Atmospharenphysik)
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Study on Earth Radiation Budget Mission Scenarios
R. Dlhopolsky, R. Hollmann, J. Muller, R. Stuhlmann
126 pages with 21 figures and 29tables
Abstract The goal of this study is to study optimized satellite configurations for observation of the radiation balanceof the Earth.We present a literature survey of Earth Radiation Budget missions and instruments. We develop a parametric tool to simulate realistic multiple satellite mission scenarios. This tool is a modular computer program which models satelliteorbits and scanning operation. We use Meteosat data sampled at three hour intervals as a databaseto simulate atmospheric scenes. Input variables are satellite equatorial crossing time and instrument characteristics. Regional, zonal and global monthly averages of shortwave and longwave fluxes for an ideal observing system and several realistic satellite scenarios are produced. Comparisons show that the three satellite combinations which have equatorial crossing times at mid-morning, noon and mid-afternoon provide the best shortwave monitoring. Crossing times near sunrise and sunset should be avoided for the shortwave. Longwave diurnal models are necessary over and surfaces and cloudy regions, if there are only two measurementsmade during daylight hours. We have found in the shortwave inversion comparison that at least 15 % of the monthly regional errors can be attributedto the shortwave anisotropic models used.
Studie fiber optimale Satellitenkonfigurationen zur Besthnmung der Strahlungsbilanz der Erde
Zusammenfassung Die Zielsetzung dieser Studie ist die Ermittlung einer optimierten Satellitenkonfiguration fur die Zwecke zukiinftiger Strahlungsbilanzmessungen der Erde. Zuerst wird in diesem Bericht eine Literaturstudie fiber die satellitengestiitzten Strahlungsbilanz-Missionen und deren verwendeten Instrumente der letzten Jahrzehnte gegeben. Zur Simulation von realistischen Satelliten- missionen wurde ein Programmpaket entwickelt. Als EingabegroBen dienen Orbitparameter, wie Aquatoruberflugszeit, und Instrumentspeziflkationen. Fur die Atmosphare werden als Datengrundlage ausMETEOSAT-Daten abgeleitete StrahlungsfluBdichten in dreistiindigen Intervallen verwendet. Als Ergebnis werden aus den fur die verschiedenen Szenarien simulierten Strahlungsbilanzmessungen Monats-, zonale sowie regionale und globale Mittelwerte derkurz- und langwelligen StrahlungsfluBdichten besthmnt und mit den tatsachlichen Feldem verglichen. Die Vergleiche zeigen, dafi die optimale Kombination aus drei Satelliten besteht: ein Satelht mit einem Vormittagsiiberflug des Aquators, einer am Mittag und einer am Nachmittag. Es zeigt sich, daJ3 Aquatoriiberflugszeiten in der Nahe des Sonnenaufgang und Sonnemmtergangs fur die kurz- wellige Komponente vermieden werden sollten. Fur bewolkte Szenen und Landbeobachtungen der langwelligen StrahlungsfluBdichte ist bei nur zwei Messungen am Tag ein Tagesgangmodell erforderlich. Es wird der Inversionsprozefi der kurzwelligen StrahlungsfluBdichte untersucht, wobei sich zeigt, daB 15 % des Fehlers im regionalen Monatsmittel aufdie verwendeten Anisotropiemodelle zuriickzufiihren ist. Masnuscript received / Manuskripteingang in der Redaktion: 18. Dezember 1997
Contents
1 Introduction 15
2 Review of ERB Mission objectives and Requirements 17 2.1 Previous, Current and Planned ERB Missions...... 17 2.1.1 Nimbus 2,3,6 and 7...... 19 2.1.2 ERBE...... 21 2.1.3 ScaRaB - METEOR 3/7...... 21
2.1.4 CERES-EOS...... 21 2.2 Review of Algorithms for Inversion...... 22 2.2.1 Satellites with ERB Instruments...... 22 2.2.2 Non-dedicated ERB Satellites...... 34 2.2.3 ERB Data by Synergy of Different Satellites...... 38 2.3 Summary...... 41
3 Review of existing and Planned ERB Instruments 43 3.1 Introduction...... 43 3.1.1 Top of Atmosphere Radiation budget ...... 43 3.1.2 Atmospheric Radiation budget ...... 43 3.1.3 Surface Radiation Budget ...... 44 3.2 Summary of ERB Instruments...... 44 3.2.1 Nimbus 2 and 3...... 48 3.2.2 Nimbus 6 and Nimbus 7...... 48 3.2.3 ERBE...... 48
v - vi -
3.2.4 ScaRaB...... 49 3.2.5 CERES...... 50 3.2.6 ISCCP: NOAA, METEOSAT, GOES, GMS...... 50 3.2.7 GERB...... 50
3.3 Instrument Types for multi-satellite ERB Missions...... 51 3.3.1 Measured Parameters...... 51 3.3.2 ISCCP as a Model ERB...... 54 3.4 ERB Monitoring at the Top of Atmosphere...... 57 3.4.1 Random Error...... 58 3.4.2 Systematic Error...... 59 3.4.3 Conversion to TOA Fluxes ...... 61 3.4.4 Coverage of the Diurnal Cycle...... 61 3.5 Summary...... 62
4 Parametric Tool to model ERB Mission Scenarios 63 4.1 Development Strategy ...... 63 4.1.1 Data Set and use in model...... 63 4.1.2 Temporal Colocation of Data: Treatment of Time in model...... 64 4.1.3 Spatial Colocation of Points...... 68 4.1.4 Input Files...... 69 4.1.5 Output Files...... 72 4.1.6 Diurnal Averaging...... 72 4.2 Results of Parametric Tool application to Database ...... 76
4.2.1 Differences for SW Inversion...... 78
4.2.2 Regional Flux Differences between Mission Scenarios...... 87 4.2.3 Zonal Flux Differences...... 95 4.2.4 Global Flux Differences...... 99
5 Recommendations for Future ERB Missions 103 5.1 Summary of Results ...... 103 - vii -
5.2 Results and Definition of Optimized Mission Scenarios ...... 105 5.3 Discussion of Possible Application in other climatological Programs...... 106
References 109
Appendix 115 A.l User’s Manual...... 117 - vm - List of Figures
4.1 Definition of Pixel Boundaries...... 68 4.2 Daily cycle: Single Satellite Scenarios...... 75 4.3 Daily cycle: Three Satellite Scenarios...... 75 4.4 Daily cycle: Multiple Satellite Scenario...... 76 4.5 Absolute Differences in Instantaneous SW Fluxes: Morning Satellite. ... 80 4.6 Absolute Differences in Instantaneous SW Fluxes: Noon Satellite...... 81 4.7 Absolute Differences in Instantaneous SW Fluxes: Afternoon Satellite. . . 82 4.8 Regional Average Shortwave and Longwave Fluxes ...... 83 4.9 SW Instrument differences for Monthly Average: 1 and 3 Satellites...... 84 4.10 SW Instrument differences for Monthly Average: Multiple Satellites...... 85 4.11 SW Instrument differences for Monthly Average: Real Missions...... 86
4.12 Differences between 12 Satellite and other Mission Scenarios: Longwave . . 88
4.13 Differences between 12 Satellite and other Mission Scenarios: Longwave . . 89 4.14 Differences between 12 Satellite and Real Mission Scenarios: Longwave . . 90 4.15 Differences between 12 Satellite and other Mission Scenarios: Shortwave . . 92 4.16 Differences between 12 Satellite and other Mission Scenarios: Shortwave. . 93 4.17 Differences between 12 Satellite and Real Mission Scenarios: Shortwave . . 94
4.18 Regional Zonal Differences: Longwave...... 96
4.19 Regional Zonal Differences: Shortwave...... 98
5.1 Summary of Systematic Differences and Variances: LW...... 105
5.2 Summary of Systematic Differences and Variances: SW...... 106
ix X List of Tables
2.1 Summary of Globally observing Radiation Budget Satellites...... 18 2.2 Summary of Thresholds used for Nimbus 7 Scene Identification...... 26 2.3 Global Annual ERB parameters...... 40
3.1 Instruments to estimate ERB : Shortwave Flux ...... 45 3.2 Instruments to estimate ERB : Longwave Flux ...... 46 3.3 Instruments to estimate ERB : Incoming Solar...... 47 3.4 Summary of Lifetimes of ERB Scanners...... 47
3.5 Parameters and Instruments for ERB studies ...... 52
3.6 Data Input for ISCCP ERB...... 55 3.7 Possible Specifications for Multi-satellite ERB Mission...... 56 3.8 LW Error in ERB studies ...... 58 3.9 SW Error in ERB studies ...... 59
4.1 Time Ranges of Meteosat B2 Image Data Sets...... 65
4.2 Example of input parameters for each run...... 70 4.3 Example of parameters specific to each satellite...... 71 4.4 Data Collected at Different Program levels...... 73 4.5 Description of scenarios...... 77 4.6 Global Shortwave and Longwave Average Fluxes ...... 99 4.7 Global Albedo Averages...... 100
5.1 Average SW Flux Differences and Variability at different time and space scales...... 104
xi - Xll
A.l Example of input parameters for each run...... 119 A.2 Example of parameters specific to each satellite...... 120 A.3 Data Collected at Different Program levels...... 121 A.4 C programs...... 122
A.4 C programs...... 123 A.4 C programs...... 124 A.5 Header Files...... 125 A.6 External Data files...... 125 A.7 Output Data files...... 126 - XU1
Acronyms
ACRIM Active Cavity Radiometer Irradiance Monitor ADEOS Advanced Earth Observing System AIRS Advanced Infrared Sounder AMSU Advanced Microwave Sounding Unit AS CAT Advanced Spaceborne Thermal Emission and Reflection Radiometer ATLID Atmospheric Lidar ATSR Along Track Scanning Radiometer AVHRR Advanced Very High Resolution Radiometer CERES Cloud and the Earth’s Radiant Energy System CPR Cloud Profiling Radar EOS Earth Observing System EOSP Earth Observing and Scanning Polarimeter ERB Earth Radiation Budget GERB Geosynchronous ERB GMS Geostationary Meteorological Satellite GOES Geosynchronous Operational Environmental Satellite HIRS High Resolution Infrared Sounder IASI Infrared Atmospheric Sounding Interferometer IFOV Instantaneous field of view IMAGER Imager IR Infrared ISCCP International Satellite Cloud Climatology Project MAM Mirror Attenuator Mosaic MERIS Medium Resolution Imaging Spectrometer METEOSAT Meteorological Satellite MHS Microwave Humidity Sounder MIMR Multifrequency Imaging Microwave Radiometer MINT Michelson Interferometer MISR Multi-anlge Imaging Spectro-Radiometer MODIS Moderate Resolution Imaging Spectrometer MRIR Medium Resolution Infrared Radiometer MSR Microwave Scanning Radiometer MSU Microwave Sounding Unit - XIV
MVIRI Meteosat Visible and Infrared Imager NFOV Narrow Field of View NO A A National Oceanic and Atmosphere Administration POLDER Polarization and Directionality of the Earth’s Reflectance PR Precipitation Radar SAGE Stratospheric Aerosol and Gas Experiment SAR Synthetic Aperture Radar SBUV Solar Backscatter Ultra-Violet Instrument ScaRaB Scanner for Earth’s Radiation Budget SEVIRI Spinning Enhanced Visible Infrared Imager SMMR Scanning Multichannel Microwave Radiometer SOUNDER Sounder SST Sea Surface Temperature SSU Stratospheric Sounding Unit SWIGS Shortwave Internal Calibration Source TO VS TIROS Operational Vertical Sounder UARS Upper Atmosphere Research Satellite VHRR Very High resolution Radiometer VIS Visible WFOV Wide Field of View Chapter 1
Introduction
The goal of monitoring the Earth’s Radiation Budget (ERB ) is to observe longterm changes in the spatial distribution of the three parameters: incoming solar irradiance, emitted longwave (LW) and reflected shortwave (SW) fluxes at the top of the atmosphere
(TOA). This information can be used to validate global climate models and study in terannual climate events, such as El Nino. Although the accuracy of instruments has improved considerably over time, a persistent error source is inadequate spatial, temporal and angular sampling caused by the orbit of the satellite on which the instruments are located. We seek to quantify these sampling errors in this report. We first present a literature survey of ERB missions. The discussion focuses on the purpose of these missions and the reasons for which certain instruments were included. Also discussed are the algorithms used to convert measurements to fluxes, with special emphasis on viewing geometry problems caused by inadequate angular sampling. We then discuss the instruments used for these missions, with special emphasis on calibration procedures. We also discuss the potential uses of auxiliary instruments, such as imagers and radars, which would be helpful in reducing the errors caused by the satellite orbit. In this study, we present a general quantification of errors which can be associated with the sampling of ERB quantities from a satellite. We have designed a parametric tool, consisting of a satellite and a scanner model, which can simulate sampling charac teristics of an actual ERB mission with multiple satellite capability. We first fly a mission consisting of 12 satellites with equatorial crossing times one hour apart. Results from this
15 -16 - mission are then compared with those of a variety of other mission scenarios. We used a 31 day Meteosat 3 hour sampled database to simulate realistic atmospheric scenes. The same geographical area is observed over a time interval of one month. The advantages of such a study are that the differences in the results can therefore be directly related to the spatial and temporal sampling of the satellite. We are also able to discuss errors due to angular sampling by using different radiance to flux conversions for missions with the same orbital configuration. Chapter 2
Review of ERB Mission objectives and Requirements
2.1 Previous, Current and Planned ERB Missions
Ever since Sputnik was sent into orbit, satellites have been sent with increasing frequency to explore various ways of looking at the earth. The concept of measuring the earth’s radiation budget, ERB was first tested with the Explorer 7 satellite with a simple sphere which measured all energy emerging from the earth with a hemisphere painted black, and the infrared energy with a hemisphere painted white. There was much experimental work with many different observing systems not particularly devoted to ERB studies during the 1960 ’s. For instance, much work was devoted to studying weather patterns which require different information. These missions, in particular, the operational satellites NOAA, GOES and METEOSAT have been observing the earth for over two decades. This long term availability makes data from these missions satellites, however, their narrow wavelength and uncalibrated sensors limit their use as sources of ERB information. The lack of global coverage in the case of the geostationary satellites is also a limiting factor. The satellites and missions discussed in the following are limited to second and third generation satellites. Starting with Nimbus 2, these were the first missions that offered the capability of global coverage. Space technology had advanced to enable satellites to be launched into polar orbits [Jacobowitz et al, 1984]. Characteristics of the missions are
17 - 18 -
Table 2.1. Summary of Globally observing Radiation Budget Satellites. NFOV are narrow
FOV sensors located on scanners, WFOV and MFOV are wide and medium FOV (respectively) sensors with low spatial resolution and view from nadir only.
Satellite Equator Scanner Lifetime References Crossing Time
Nimbus 2 Local noon NFOV 16 May - 28 July 1966 1
Nimbus 3 11:30 NFOV 16 April - 15 August, 1969 2 3-17 October, 1969 21 January - 3 February, 1970 Nimbus 6 11:09 NFOV July and August 1978 3 WFOV 2 July 1975 - October 1978 Nimbus 7 11:51 NFOV 16 November 1978- 22 June 1980 4 WFOV 16 November 1978 - October 1987 ERBS 36 day cycle NFOV 5 November 1984 - February 1990 5 MFOV 5 November 1984 - WFOV 5 November 1984 - NOAA-9 14:30 NFOV January 1985 - January 1987 5 MFOV January 1985 - WFOV January 1985 - NO A A-10 07:30 NFOV 12 November 1986 - May 1989 5 MFOV 12 November 1986 - WFOV 12 November 1986 - ScaRaB Meteor 3/7 190 day cycle, NFOV 25 January 1994 - March 1995 6 prograde CERES EOS-AM 11:00 NFOV Planned 7
CERES EOS-PM 1:15 - 1:45 NFOV Planned 7
CERES TRMM Precessing NFOV Planned 7
References: 1, [Raschke, 1968]; 2, [Raschke et al, 1973]; 3, [Smith et al., 1977]; 4,
[Jacobowitz et al, 1984]; 5, [Staylor, 1993]; 6, [Kandel, 1994]; 7, [Wielicki and Barkstrom, 1991]. - 19 - listed in Table 2.1, where all times are local times (time of satellite overpass) and in 2400 hour notation. GEO refers to geostationary orbits and LEO refers to sun-synchronous or precessing sun-synchronous orbits. Not included in this list are potential sources of ERB data but not specifically ERB dedicated missions. These are the operational weather satellites NOAA, which are in sun-synchronous orbits with instruments operational since
1974, METEOSAT, a GEO satellite system operational since 1977 and GOES, also GEO and operational since 1975.
2.1.1 Nimbus 2,3,6 and 7
The first global ERB data was available from Nimbus 2. It had cross track scanner with a 5 channel medium resolution infrared radiometer MRIR [Raschke, 1968]. Nimbus 3 measurements ranged from 16 April to 15 August, 1969 and other times 3 October to 17 October, 1969 and 21 January to 3 February 1970. This allowed an approximate estimate of the annual ERB. IRIS (Infrared Interferometer Spectrometer, temperature sounding and water vapor), SIRS (Satellite Infrared Spectrometer) were also on board. Nimbus 6 was the next satellite to be equipped with an ERB package which included a sun irradiance instrument, as well as LW and SW broadband instruments. It also carried LRIR (Limb Radiance Infrared Radiometer), PMR,(Pressure Modulator Radiometer),
SCAMS (Scanning Microwave Spectrometer), HIRS (High Resolution Infrared Radiation
Sounder). Its ERB goals were: - 20 -
1. measure the solar constant over all wavelengths as well as spectrally in the ultraviolet and visible spectrum, (10 narrow wavelength channels covering all wavelengths).
2. measure earth’s flux in wavelength regions 0.2-3.8/xm and all wavelengths greater than 0.2/im with a fixed wide angle field of view radiometer (WFOV). LW ERB is obtained from the difference between total and shortwave, (4 channels, not calibrated in flight).
3. measure A < 0.7 and A > 0.7/zm for incoming solar flux and reflected shortwave flux for assessment of aerosol contribution to albedo.
4. measure reflected solar radiation for wavelengths 0.2 to 4.7^m and emit ted IR radiation for wavelengths 5-50 /xm with narrow field of view (NFOV) scanning radiometers for monthly average regional scale esti mates of the ERB, (8 biaxial channels).
The same ERB package was on-board Nimbus-6 and Nimbus- 7. Nimbus-7 also car ried on-board instruments which made measurements of atmospheric gases, particulates, and temperature profiles (LIMS, limb infrared monitor of the stratosphere, SAMS, strato spheric and atmospheric sounder, SBUV, solar backscatter ultraviolet instrument, TOMS, Total Ozone Mapping Spectrometer, THIR, temperature-humidity infrared radiometer), near-surface concentration of phytoplankton pigments (CZCS, Coastal Zone Color Scan ner) ice conditions (SMMR, Scanning Multichannel Microwave Radiometer) and ERB parameters. It should be noted that these other measurements were not specifically as sociated with the ERB parameters. The goals of the Nimbus-7 ERB are similar to those
outlined for Nimbus-6, and summarized here from [Jacobowitz et al., 1984].
1. Measurements of solar irradiance, its variation with time and the varia tion with time of its spectrum (10 channels).
2. Determine simultaneously measured ERB parameters for at least a year on both planetary and synoptic scales from both WFOV and NFOV.
3. Collect observations of the angular distribution of SW and LW radiances from the NFOV scanner, for the specific development of angular direc tional models. (5 different scan modes for a biaxial scanner). -21 -
2.1.2 ERBE
The next ERB project was NASA’s ERB Experiment (ERBE). It was the first Mission to consist of planned multiple satellite observations of the ERB , one of the satellites (ERBS) being specifically built for ERB measurements. NOAA-9 and NO A A-10 satellites carried on board the AVHRR, HIRS instruments as well as an ERB instrument. Its goals are:
1, Produce global, monthly averages of albedo and LW flux on a grid of 2.5°. 2. Separate clear sky scenes and produce global, monthly averages of albedo and LW flux for clear sky on a grid of 2.5° .
2.1.3 ScaRaB - METEOR 3/1
The most recent ERB satellite instrument is ScaRaB (Scanner for Radiation Budget) on the METEOR 3/7 satellite. In addition to the ScaRaB instrument, three other instru ments are on board the METEOR satellite, a visible and infrared high resolution scanner and an IR sounder. The goals of this mission are [CNES-CNRS-ROSHYDROMET-DARA, 1994]:
1. Study cloud radiative interactions to assess impact of clouds on ERB .
2. Study the role of variations of the ERB in association with large scale climate processes such as El Nino.
3. Add to the long term record of the ERB , for the detection and under standing of climate trends.
2.1.4 CERES - EOS
CERES (Clouds and the Earth’s Radiant Energy System) is an ERB instrument that is part of the Earth Observing System, EOS. Its goals are to further understand .the influence of clouds in the earth’s climate. In addition to ERB instruments, high resolution imagers are also planned to be on board. -22 -
2.2 Review of Algorithms for Inversion
The primary goals of ERB missions is to estimate monthly averages of emitted long wave flux, reflected shortwave flux from broadband wavelength instruments. The proce dures for inversion are as follows:
1. Convert filtered to unfiltered radiances, including narrow-band to broad band conversion.
2. Convert radiance to flux.
3. Convert instantaneous flux to daily flux.
Calibration of instruments is an important part of inversion. However, it is more connected with the instruments and subsequent discussion will be done in the instrument section. The conversion of counts to engineering units will also not be explicitly explained.
2.2.1 Satellites with ERB Instruments
2.2.1.1 Nimbus 2
The ERB instrument on Nimbus 2 was located on a cross-track scanner which neces sitated the use of AD Ms (for the SW) and AEFs (for the LW) for conversion of radiances to fluxes.
Longwave: The longwave channel was sensitive to the wavelength range 5.0/tm to 30/nn .
A theoretical unfiltered LW radiance was estimated from 60 atmospheric profiles. A linear relationship was found between this radiance and the measured radiance to unfilter the LW radiances. The angular directional characteristics of the emitted longwave are a function of viewing zenith angle, 9 and independent of relative azimuth angle, (j) . An empirical 9 dependence was derived from previous TIROS measurements and used to obtain a LW flux. The diurnal average of emitted LW flux was obtained by the mean of all measurements for one day, which for equatorial regions was one time during daylight and one time during the night.
Shortwave: The SW channel was sensitive to the wavelength range 0.2/un to 4/un and was not unfiltered. SW radiances are a function of the three angles, 9q , 9 , and (j) . The - 23 - measured bidirectional reflectance is calculated by dividing the measured radiance by the product of the solar constant and cos 9q . The ideal calculation of the directional albedo (Note: In [Raschke, 1968], directional albedo is called directional reflectance) is to obtain many bidirectional reflectances from different directions at the same time and integrate over all angles. This is not possible given the constraints of satellite orbits and scanning geometry. Therefore, the integration is approximated by multiplying the bidirectional reflectance by an anisotropic factor (a function of 9q , 9,0 ). The anisotropic factor is made from previous measurements from many angles over long periods of time for average conditions. For Nimbus 2, the anisotropic factors were derived from airplane and balloon measurements. Since albedo derived from measurements comes from only one time of day, a model directional albedo (from previous mission measurements) is used to fill in the directional albedoes at other 9q . The diurnally averaged reflected flux was then calculated by in tegrating the product of the directional reflectance, the solar constant and cosine of 9q , from sunrise to sunset. Diurnal integration is done with the assumption that there is no change in meteorology and surface state for an observed area.
2.2.1.2 Nimbus 3
The Nimbus 3 ERB instrument was on a cross-track scanner which necessitated the use of directional models in the LW and SW conversion of radiances to fluxes. Scene identification was determined for ocean, cloud-land and snow. This was done in order to apply the appropriate directional albedo models (created from Nimbus 2 data). Ocean was separated when the reflectance, irp < 0.10 and equivalent blackbody temperature in the water vapor channel was higher than 273 C. p is defined as bidirectional reflectance computed from the measured SW radiance divided by the incoming solar radiation. Snow was defined for regions above 65° latitude and irp > 0.50. All other measurements were considered land/cloud mix.
Longwave: Nimbus 3 did not have one broadband LW instrument but 4 narrow-band sen sors. Theoretical, unfiltered broadband radiances were calculated from 160 atmospheric -24 - models with varying atmospheric properties. A relation between these and the narrow- band radiances was found using a multiple least-square regression formula. This was used to estimate the measured broadband LW radiance. A LW flux was derived by us ing a limb-darkening function (determined from Nimbus-2) and assuming independence from (f>. Two different functions were used, one for measurements poleward of 70° and the other for all other areas. No cloud or cloud-free determination was performed. The diurnal average was obtained from the mean between day and night measurements.
Shortwave: The 5th channel on Nimbus 3 was sensitive to wavelengths 0.2/zm to 4.8/zm . No unfiltering is performed. The bidirectional reflectance is evaluated in the same way as for Nimbus 2 SW measurements. Therefore, new anisotropic factors were developed, using Nimbus 2, data to account for this. All data with nadir angles greater than 45° and 60 greater than 80° were excluded due to uncertainties. The assumption that average reflective properties were suitable for every IFOV was still necessary and a source of uncertainty.
The diurnal albedo was derived as already described for Nimbus 2 except that here it is defined as the ratio of the mean diurnal SW reflected flux and the mean diurnal incoming SW flux. No simultaneous measurements of solar irradiance were made so the current solar constant was used. As with Nimbus 2, the constant meteorology was assumed [Raschke et al., 1973].
2.2.1.3 Nimbus 6
The Nimbus 6 ERB experiment consisted of two methods of measuring the ERB . One is the wide field of view (WFOV) non-scanning radiometer for observation of the whole earth disk. The other is a narrow field of view (NFOV) instrument on a bi-axial scanner which would measure ERB parameters on a regional scale. Regional flux averages are obtained over a month for a region about 500 km 2 . Based on comparisons between scanner and non-scanner data, the scanner data is better at resolving synoptic ERB parameters [Smith et al., 1977].
Incoming Solar Flux: Nimbus 6 included an instrument which measured the third imp or- -25 -
tant ERB parameter, the incoming solar flux. The solar constant was measured to be 1391 W/m2 with a variation about its mean value of ± 11.4 W/m2 [Jacobowitz et al, 1979]. This value was larger than expected and attributed to calibration problems.
Longwave and Shortwave Fluxes: Obtaining LW and SW fluxes from a WFOV radiometer requires the conversion of irradiances measured at the satellite to fluxes emerging at the top of atmosphere (TOA). This conversion must take into account the fact that, although the sensor sees the entire earth disk (or a large fraction of it), most of the measured energy comes from the sub-satellite point. This is a result of using a flat plate sensor. In addition, although the target area is defined at the top of atmosphere , energy regions outside of this area reaches the sensor. This latter problem is considered to be small, especially because of the greater weighting of energy directly below the satellite. The only serious source of error is the lack of diurnal variation. The shape factor method, which assumes that the irradiance is a function only of the distance squared, was used to produce the WFOV fluxes. No scene identification was used because of the large instantaneous field of view (IFOV). A model that was a function only of 60 (time of day and latitude) was used. LW radiances from the NFOV scanner requires the use of a limb-darkening function developed from previous satellite data sets to get a LW flux at TOA. SW fluxes from the
NFOV scanner require angular directional models to produce a SW flux at TOA. Because these are NFOV, no correction for satellite distance is necessary. The Nimbus-6 scanner data was separated as with Nimbus-3 into snow, ocean and cloud/land models. Diurnal averages were obtained for the LW by averaging the day and night LW fluxes. The SW was first processed with the assumption that SW diurnal albedo was equal to the albedo of the measurement available on that day. This was afterwards corrected by fitting the Nimbus 2 diurnal albedo models to the measurement and integrating [Brooks et al, 1986].
2.2.1.4 Nimbus 7
The Nimbus 7 satellite could be called the first ERB mission satellite in that it -26 -
Table 2.2. Summary of Thresholds used for Nimbus 7 Scene Identification
Threshold Scene
> 0.5 and Lat > 67.5° N,S snow
0.15 < 7rp < 0.5 cloud
0.15 < 7Tp ocean
0.15 < -Kp and Ne > 78.2W/m2 fster land
0.15 < 7rp and Ne < 78.2W/m2/ster cloud
carried other instruments which, when used with the ERB data, aided in improving its accuracy. The conversion of measured irradiances (WFOV) and radiances (NFOV) to fluxes was largely the same as described in Nimbus 6 above. A major improvement was the presence of an instrument, THIR, on board the same satellite which made concurrent measurements (at higher spatial resolution) of the temperature within the IFOV. This enabled the ERB parameters to be further separated according to cloud cover and type. Climatological data supplied information on surface and vegetation type to aid in the threshold determinations for the THIR data. Unfortunately, the scanner lived 20 months so that a long climate data set was not possible for cloud type studies. Scene identification [ Vemury, et al. 1984] was performed with a constant threshold method. The SW radiance is first divided by the incoming solar radiation and multiplied by tt. This value is irp. The scene identification is identified in Table 2.2, where Ne is the LW radiance. Radiances measured within the 8 range 0° - 21° were excluded because of uncertainty with sunglint in the specular direction. Although not specifically designed for use in the processing of the ERB data, the TOMS instrument mounted on a cross track scanner also was capable of providing in formation. It measures solar backscatter radiation at 6 channels, two of which are not absorbed strongly by ozone and have been shown to be suitable for detecting warm, low - 27 -
altitude clouds that are difficult to detect with the THIR [Stowe et al, 1988a]. The SAM II was used to compare results from El Chichon eruption [Kyle et al., 1986].
Incoming Solar Flux: Nimbus 7 included an instrument which measured the ERB pa rameter, the incoming solar flux. The value measured by Nimbus 6 was too high, and therefore an additional self-calibrating cavity channel was included. Five years of mea surements result in an average value of 1370.8 W/m2 [Kyle et al, 1986] with fluctuations that correlate well with solar observations. There is a decreasing trend of about 1 W/m2 over the 5 years.
Longwave: The WFOV channels measure the SW and total. The difference between these channels is the LW irradiance. The LW flux is computed by adjusting the measurement to
the flux that would be measured at the top of the atmosphere, (defined to be 15 km). The average LW flux is computed from all measurements over a target area during daytime and nighttime separately. Unlike the WFOV, the NFOV LW had a separate channel and there was no total wavelength channel. NFOV LW fluxes had to be corrected for limb darkening. Limb dark
ening models were used from [Raschke et al., 1973] where different models were used for
latitudes within 20° of the earth’s poles and the remainder of the globe [Kyle et al., 1986]. The average daily flux is an average of the measurements at night and during the day, weighted by the length of daylight.
Shortwave: The SW WFOV albedo was corrected with global (no surface dependence)
factors listed in [Raschke et al., 1973]. The SW NFOV flux derivation used data taken by the satellite on which the instrument was mounted. Specifically, the availability of auxiliary data is exploited to improve scene identification (for angular correction of NFOV radiances). The THIR instrument on board (with a resolution of 7 km at nadir) was used to estimate the amount of high, middle and low cloudiness from equivalent black body temperature of the 11/rni window sensor. With this added scene classification capability and the bi-axial scanner, four new angular directional models were developed and applied to convert SW radiances to SW fluxes, snow/ice, land, ocean and cloud. The directional -28 - albedoes used to estimate the NFOV daily averages for three surfaces were still taken from [Raschke et al., 1973]. Note that daily albedo averages are derived using different diurnal albedo models for NFOV and WFOV, because the WFOV was not capable of distinguishing surfaces in its large IFOV.
Regional Average: The analysis of the Nimbus 7 data first brought up the question of averaging of pixels within a region, in this case 500km 2 regions. Specifically, should all measurements have equal weight in the averaging process[ Vemury, et al. 1984]? This question arose because the of the stepping pattern of the scanner. Each measurement was intended to include energy from the same surface area. To do this the scan rate needed to be a function of 9 . This resulted however, in more measurements coming from larger viewing angles. When these measurements are combined to obtain a regional average, they are not weighted. This would not be a problem except that the identification of cloud cover and the conversion from radiances to fluxes was known to have large errors at large 9 . These errors are therefore amplified by the equal weighting.
To solve this problem, [Vemury, et al. 1984] compared 4 different weighting proce dures: equal averaging, cosine weighting, cosine-sine weighting, and population weighting with truncation. The first averages fluxes within bins of 9 before integrating over 9 . The second and third are similar except that the fluxes are weighted by cosines and sines. The last is the method used for Nimbus 7 except that all measurements made at 9 > 70° are excluded from the averaging. It was found that the truncation had the largest effect (or improvement) on resulting albedoes, largely because it eliminates the large albedoes that occur from scene misclassification at large 9 . The recommended cutoff angle for 9 was 55° .
2.2.1.5 ERBE
The ERB Experiment (ERBE) was the first ERB mission to consist of three satel lites intended to fly simultaneously and provide the diurnal sampling that was lacking in previous ERB missions. Along with two sun-synchronous morning and afternoon satel lites (NOAA-10 and NOAA-9) flew an ERB dedicated satellite with a precessing orbit -29 - which cycles through all local times every 36 days. This increase in diurnal sampling necessitated a loss of sampling near the poles. The ERB instruments were a solar monitor, two-channel WFOV and MFOV ra diometer, and three channel NFOV radiometer, the latter being located on a cross track scanner. The MFOV covers roughly an area of 1000km [Barkstrom, 1989]. On board ERBS was also the ability of the scanner to view along track. This procedure was done a few times for limb darkening studies. The ERBE non-scanners (WFOV and MFOV) had a total radiation channel and a shortwave channel. The LW is the difference between the two. There are two techniques for converting non-scanner irradiances to fluxes: the shape factor method and the decon volution. The former assumes that one can calculate a factor which, when multiplied by the measurement, results in a flux. This factor must include the directionality of the ra diance field and the point spread function of the instrument. This eliminates the need for integration and is performed on each measurement. The latter, also known as a numerical filter, uses the fact that, although there is no scanning motion, the motion of the satellite along the orbit provides an opportunity to increase the resolution. As a result of the Nimbus-7 error analyses, the approach to the ERBE processing was improved. Instead of one cloud (overcast ) ADM, an ADM was developed for four different cloud fractions. This was possible because the TOMS on board Nimbus-7 was used to improve cloud identification. Both NOAA-9 and NOAA-IO carry an AVHRR instrument, but these were not used in the data reduction. One of the ERBE satellites,
ERBS, did not carry a comparable instrument and therefore, for consistency between all three satellites, it was necessary to use a scene identification technique based on the measured broadband LW and SW radiances. The Maximum Likelihood Estimator (MLE) technique uses the angular directional information collected from the Nimbus-7 data to estimate the probability of occurrence of the cloud classes. This information includes, for both LW and SW, the average anisotropic factor, the average broadband radiance, and standard deviation of the broadband radiances, and a LW-SW correlation coefficient [Wielicki and Green, 1989]. LW anisotropic models consist of a matrix of factors which - 30 - are a function of 9 , latitude of measurement and season of year [Swttles, et al., 1989]. Azimuthal symmetry is assumed. SW ADMs are also a matrix of anisotropic factors which are a function o^ 90 , 9 , and (j> [Suttles, et al, 1988]. Although Nimbus-7 separated overcast scenes into land and ocean, ERBE used only one overcast model for both. Mixed scenes were a combination of 50% land and 50% ocean. Day-night averaged limb-darkening models were used. Mentioned also was the need to minimize the data set for processing. Non-scanner scene fraction is obtained from scanner scene identification. Mixed land-ocean scenes are not used for non-scanners.
Incoming Solar Flux: An instrument to measure incoming solar flux was on board each satellite. This is the same as that used for the Solar Maximum Mission.
Longwave: For the LW channel, the directionality of the radiance field is based on the scene identification from the NFOV scanner within the FOV of the WFOV and MFOV instruments. The point spread function is a simple flat plate response and a function only of position. The ERBE scanners had a separate LW radiometer which measured in the wave lengths 5.0/zm to 50/zm . Radiances are converted to fluxes with the ADMs described above (Note for LW, ADMs are also referred to as AEFs, anisotropic emission factors [Stubenrauch, et al, 1991]). Differences in the day and night longwave fluxes were used to construct diurnal models to obtain the diurnal average. These differences are largest for desert and land and smallest for ocean and days which are completely overcast [Suttles, et al., 1989].
Shortwave: The SW channel WFOV and MFOV measurements were converted to albe do es in a similar way as the LW values were converted to fluxes, with the additional complication of a dependence on incoming solar radiation. Also, because azimuth de pendence must be included, the integral for the shape factor is not simple and must be evaluated with pre-calculated quadrature weights. The NFOV SW radiometer measured in the wavelength range 0.2/tm to 5 /mi . Anisotropic factors from the ADMs described above were used to convert SW radiances -31 - to SW fluxes for each measurement. There is the possibility of some discrepancy because the method of identifying the cloud classes with the Nimbus-7 data, specifically, the use of THIR equivalent temperature measurements, is different from that used by ERBE. In addition, the different orbits and equatorial crossing times of the Nimbus-7 satellite and the ERBE satellites, holds the possibility that the sampling of cloud cover for which the ADMs are made will be different for the ERBE satellites. For example, if ERBS observes the tropics late in the day (large 60 ), its SW radiances are corrected with an ADM devel oped from Nimbus-7 observations near the pole, since Nimbus-7 observes only the poles at large 60 . Directional albedo models which are used to obtain the diurnal average were devel oped from GOES and Nimbus 7 ERB data. Co-located Nimbus-7 ERB radiances were used to convert the GOES narrow-band radiances to broad-band radiances, from which directional albedoes are obtained [Minnis and Harrison , 1984].
Regional Average: Non-scanner measurements are averaged into 5° by 5° regions and 10° by 10° regions. Here, there is a problem of overlap of data because the center points of more than one measurement can fall in the same region. The size of the region also varies and therefore a different number of measurements exists for each region. There is an additional problem that the conversion to fluxes uses a different grid system from the region grid. Regions adjacent to groundtrack regions whose boundaries do not cross the groundtrack regions are not given any data.
The ERBE cross track scanners did not have a stepping mechanism to equalize all the footprints. Each pixel was treated equally, despite the changing size of the foot print. Measurements made from 6 greater than 70° were excluded. Measurements whose anisotropic factor exceeded 2.0 (ocean sunglint) were also excluded due to uncertainty in scene identification. The SW and LW fluxes were averaged into 2.5° by 2.5° regions with equal weight.
Temporal Average: The ERBE processing Algorithm is organized into a Time-Space Av eraging Matrix (TSM), in which the data from the various satellites is ordered according to local hour and day of measurement. All NFOV measurements within a local hour - 32 - are converted to the same local half hour using the directional albedo models and as suming constant meteorology within the hour. Unsampled hourboxes are filled in by time-weighting between the sampled hourboxes. Scene variation between the hours is assumed to be linear. The daily average albedo is the sum of the hour fluxes divided by
24 and corrected for the diurnal variation of incoming solar radiation.
The TSM for LW fluxes is filled in using all the hourboxes within a month, interpo lating over days when there is missing data. A linear interpolation between measurements is applied except in regions for which there exists a strong diurnal LW cycle, in particular clear land areas. In this case, a half-sine model fit through the measurements is performed.
Multi-satellite Processing: The output of the TSA algorithms is in a form such that the data can be combined from all three satellites using an unweighted average. The cloud fractions and fluxes for an hourbox with more than one satellite measurement are averaged to produce a new albedo which is evaluated the same way as for a single satellite.
Clear Sky: ERBE was the first ERB mission for which separate clear sky ERB parameters were prepared as output products. Data which were declared clear sky by the scene identification algorithms were put through the same TSA processing from which monthly regional ERB parameters were constructed. Knowledge of the clear sky fluxes are used in estimating the cloud radiative forcing and bypasses the need for estimating cloud fraction and cloud properties from satellite data. This was first attempted by [Ellis, et al, 1978] with Nimbus 3 data. Subsequent new analyses of Nimbus 7 data have also produced cloud radiative forcing estimates [Ardanuy et al, 1991].
2.2.1.6 ScaRaB on Meteor 3
The ScaRaB instrument flew on board the METEOR 3/7 satellite. It is an improve ment from ERBE because it has two broadband channels and two narrow band channels. The narrow band channels will aid in the characterizing of cloud fields and also aid in intercomparison with other narrow-band instruments on board GEO satellites. It is a scanning radiometer with a nadir field of view of 60 km. The Meteor 3 satellite has a diurnal repeat cycle of 190 days [Kandel , 1994]. -33 -
One of the goals of ScaRaB is to produce an ERB product using algorithms as similar as possible to the ERBE algorithms so that results can be compared. Therefore one part of the processing is exactly as described in the ERBE section. The ERBE NFOV ERB data set is 5 years 1985 to 1990. With a gap of 3 or 4 years, perhaps a bias-free long term ERB can be extended with the ScaRaB NFOV data. [Viollier, 1994] ERBE ADMs are to be used initially. There may be some differences due to the different scene identification capabilities of the 2 instruments. Cloud cover for the ERBE
ADMs was based on TOMS ultraviolet reflectivity whereas that for ScaRaB will be the visible and window IR channels [Kandel, 1994].
2.2.1.7 CERES - EOS
CERES is an ERB instrument to be flown on the EOS AM and PM satellites ,equa torial crossing times of 10:00 and 13:00 respectively, and TRMM. It may also fly on the future space station. AVHRR and HIRS will be present on the morning satellite and a MODIS-N (Moderate Resolution Imaging Spectrometer-Nadir) instrument will be on the Space Station. Two scanning instruments will be on each satellite, one in a cross track mode for spatial sampling and one which can scan in the azimuth direction for increased angular sampling. The field of view of the scanners will be 25km at nadir (due to improved sensor technology). This allows an increased grid resolution of 1° latitude and longitude. There will be three sensors, a SW, Total and LW window channel. The last will deliver information about the surface, such that an atmospheric profile might be derived. The LW radiance will be the difference between the SW and Total channels. Output products, in addition to the ERB parameters discussed in the previous mis sions, will be surface radiative fluxes and atmospheric radiative flux divergence. The atmospheric flux divergence calculation relies on radiative transfer calculations using pro files of temperature, water vapor, aerosols and clouds. Other instruments on-board the EOS satellites are to observe these parameters. The surface radiative flux must also use this profile information and estimates of albedo, surface temperature and emissivity as measured from the satellite. The SW TOA fluxes will be correlated with the net SW flux - 34 - at the surface. The TOA fluxes will be used to constrain the results from the radiative transfer code.
The rotating azimuth scanner data will be used to develop new ADMs appropriate for this satellite’s data. They will be a function of surface type and include cloud properties. The cloud properties will be deduced from imaging and sounding instruments, such as
MODIS-N [Wielicki and Barkstrom, 1991].
2.2.2 Non-dedicated ERB Satellites
This section pertains to satellite missions which have tried to obtain ERB parameters from narrow-wavelength channels.
2.2.2.1 NOAA-AVHRR
The NOAA satellites have been operational for a long term, compared with the other ERB missions. The package contains the AVHRR, HIRS/MSU instruments. Processing of the data has similar problems as the ERB instruments, although the higher resolution images provide better scene classification. The anisotropy of the reflected and emitted radiation must be considered as well as the diurnal variation. In addition, the narrow-band channels cannot measure the broad-band ERB parameters. These must be estimated by conversion models. The existence of this data from 1974 gave sufficient reason to attempt to derive a ERB data set for possible long term climate variation. [Ohring and Gruber, 1984]. The original purpose of the chosen wavelengths was to optimize cloud detection for weather analysis. This had the result that cirrus clouds would be less likely to be detected since they are more transparent in the IR window (10.5-12.5/zm ) than at other wavelengths. Regarding the VIS window (0.5 to 0.7/zm ), the reflection spectrum of the underlying surface becomes important. Cloud albedoes decrease for wavelengths > 0.7/rni while vegetation albedoes increase. These considerations have the result that narrow-band visible albedo overestimates the broadband albedo in the presence of clouds. This effect is reversed when the underlying surface is snow. Therefore, this data can be used to study interannual variability of the ERB , they are less useful for quantitative, long term - 35 climate studies. The NOAA-NASA Pathfinder project, in particular the AVHRR Atmosphere Pathfinder program will develop new algorithms to analyze the AVHRR data with the following goals
[NOAA-NASA Pathfinder Program, 1994]:
1. Viewing geometry corrected average, extreme, and cloud free radiance statistics.
2. A 9 day continuously climatology of observed cloud free radiances.
3. Aerosol burdens based on observed cloud-free radiances.
4. Cloud Properties (i.e. total cloud cover, number of cloud layers, average cloud cover, means and standard deviations of liquid/ice water path, hydrometeor size, and viewing geometry corrected radiances for overcast pixels in each of layers present).
5. Mean and standard deviations for the top of the atmosphere LW and SW fluxes and shortwave albedo.
6 . Mean shortwave radiative flux at the surface.
2.2.2.2 Meteosat
Throughout the previous reviews, one outstanding error source has been addressed only by the ERBE mission: the diurnal sampling. Current and future missions do not have solid plans to address this issue, except possibly by an instrument on the space station. The operational geostationary weather satellites are the best platforms from which to estimate the diurnal variation of the ERB parameters. There are two ways to approach this: derive the parameters directly from the data or use the data as auxiliary information (i.e. daily change in cloud cover) for the processing of the broad band ERB fluxes. The first approach suffers from similar problems with the AVHRR sensors. The narrow band to broad band conversion is a large source of errors. There is no polar data and there is a constant viewing geometry. The latter will tend to magnify errors due to SW and LW anisotropic correction. ERB parameters were derived from METEOSAT data by [Gube, 1982]. The resolu tion of the METEOSAT pixels at nadir is 2.5km for visible (0.4-1.1/zm ) and 5km for IR -36 -
(10.5-12.5/mi ) and Water Vapor (5.7-7.5/zm ) channels. The data from which the ERB parameters are derived is the METEOSAT Climate Data Set, which is an average of 32 by 32 pixels (area 150 to 200km). Half hour values have been reduced to 3 hour averages, starting at 0200. METEOSAT data is narrow-band and narrow field of view, requiring spectral conversion to broadband and anisotropic correction to obtain the fluxes. A ra diation transfer model is used, where the following information for the SW radiance is input:
1. Atmospheric transmission data.
2. Cloud Droplet Distribution. 3. Optical Properties and size distribution of aerosols.
4. Scattering phase functions according to Mie theory.
5. Delta-function approximation for scattering by cloud droplets.
The model outputs the TOA radiance and flux within the sensor wavelength interval.
The model was run for 240 profiles with a wide variety of ground albedoes, cloud optical depths and 60 . The ocean reflectivity was approximated by the Cox and Munk model, and land was considered a diffuse reflector. Clouds were assumed to be stratiform layers. The conversion to broadband values was produced by a regression, which is also dependent on model calculations. LW fluxes were derived using a two-stream approximation radiative transfer model. Radiances are simulated from the calculated fluxes by adjusting the optical path lengths.
This assumes no scattering. The model includes absorption by CO2, CH4, N20, O3, water vapor and water vapor polymers. The model was run for 125 atmospheric profiles with different temperature/humidity conditions and various cloud heights. The broad band LW flux was determined from the IR and WV channels and third-order polynomial fit with regression coefficients derived from model calculations. A satellite intercomparison ERB investigation was done with METEOSAT 1, Nim bus 7 and TIROS N (Television and Infrared Observation Satellite) AVHRR instrument
(precursor of NO A A satellites) by [Saunders et al, 1983]. Three different algorithms -37 -
(for each satellite data set) were applied to the METEOSAT data. The Nimbus-7 and
AVHRR algorithms have been described in previous sections. The METEOSAT data was processed in the following way. The calibration factors are a function of surface and cloud type and are therefore subject to errors based on scene identification. There are 300-400 pixels per 1° area, so the errors will average out at this resolution. The threshold for cloud identification was an albedo of 0.30. Albedo determined from the VIS channel was assumed to be the same as a broad band albedo. The Nimbus 7 AD Ms were used to correct for anisotropyof the SW radiances. The LW radiance was derived using regression relationships developed by [Gube, 1982]. The hourly METEOSAT data is first averaged over 1° grids and further to 2.5° for intercomparison with other data sets. Then the daily average is produced. The comparison with the AVHRR-algorithm data was impaired by the isotropic assumption for the albedoes. The Nimbus 7-algorithm data clearly showed a difference in the daily average that could be attributed to the poor diurnal sampling of the Nimbus-7 satellite, despite the use of a diurnal model to produce the average. A Geostationary ERB (GERB) instrument might fly on future METEOSAT systems. This will measure the diurnal variation with broad-band ERB parameters. Disadvantages remain regarding constant view geometry and lack of polar observation.
2.2.2.3 GOES
Hourly radiation fields at the top of atmosphere using GOES narrow-band VIS (0.55- 0.75/xm ) and IR (10.5-12.5/zm ) radiances have been derived [Minnis and Harrison , 1984]. The data period was one month. The data processing followed the following steps:
1. Convert narrow to broadband radiances.
2. Correct for anisotropy.
3. Monthly average.
Instead of using averages of radiation models to do the conversion of narrow band radiances to broad-band radiances,a regression relationship was derived from co-located Nimbus-7 NFOV calibrated radiances with the GOES radiances . The advantage is that -38 - one is using measured values to do the regression instead of model average atmospheric conditions. Scenes are colocated in time within 15 minutes of each satellite. In angular space, the differences are: for 60 < 2.5° , 8 < 7.5° and (j) < 15.0° . Spatially, the GOES data were colocated within a square defined by the Nimbus-7 pixel. For SW, a regression equation was created for land and ocean surfaces. Regres sions for the LW were more complex because of differing effects of limb-darkening on the measurements from the two satellites. Nimbus-7 data was normalized to nadir us ing [Raschke et ai, 1973] and GOES was normalized using a theoretical model. Scene identification is based on a hybrid bispectral threshold technique during daytime and one-threshold technique during nightime. Clear skies are identified using a minimum re flectance model. Cloud amounts for each hour are estimated from a threshold technique that depends on the IR histogram, clear sky temperature and constant lapse rate of -
6.5 I
(z > 6 km), where z is defined to be the difference between the measured temperature and the cloud-free temperature, divided by the lapse rate. Cloud emissivity is assumed to be 1, the cloud fills the field of view and measured temperature defines cloud top. Anisotropic factors were developed using the GOES data for clear ocean, clouds and land. The land was identified in combination with other aircraft measurements. The monthly average albedo for each hour is calculated. Then the monthly average albedo is calculated by integrating this value over increments of time between sunrise and sunset, and dividing by the integrated solar incident flux. The monthly LW flux is a daily average which is then integrated over the month.
2.2.3 ERB Data by Synergy of Different Satellites
The above description of the GOES and Nimbus-7 showed that there are advantages to using different platforms to derive ERB parameters. The diurnal variation of the GOES data provided temporal resolution and the Nimbus-7 data provided spectral resolution. ERBE was the first “mission” to combine results from different satellites and great care was made to intercalibrate the instruments. Although the same ERB instrument was on each satellite, the heights of the satellites differed, causing a difference in the size of the -39 - field-of-view. Overlap of ERBE and Nimbus-7 data was also used to study the ERB , the intent being to extend the length of time for ERB data. Despite large differences in data reduction, the errors for a four month overlap period were 0.16% for LW and 0.3% for
SW. Nimbus-7 has only long-term WFOV data available but a combination of the two data sets is suitable for studying large scale features of the earth-atmosphere system, such as El Nino [Kyle et ai, 1990]. The most comprehensive and thorough satellite data set is being done currently with ISCCP (International Satellite cloud and climatology project). This project is not an ERB project and is specifically focused on cloud radiative properties. However, it is an example of how data from multiple satellites can be collected and synthesized to produce one complete data set. For diurnal variation and global coverage, five GEO satellites are used: GOES-East, GOES-West, GMS, INSAT, and METEOSAT. To complete coverage of the poles, at least one NO A A satellite is also included. Presence of sea ice and snow is determined from US Navy/NO A A Sea Ice Product and the NO A A Snow Data Product. [Schiffer and Rossow , 1985]. The largest problem is that measurements (in engineering units) between different instruments will differ due to inadequate calibration, rather than changes in the underlying field. A sophisticated scene identification procedure is used for ISCCP and is summarized here [Rossow and Garder, 1992]:
1. Space Contrast test only on a single IR image
2. Time Contrast Test on three consecutive IR images at constant diurnal phase. 3. Cumulation of Space/Time Statistics for both IR and VIS images.
4. Construction of Clear-Sky Composites for both IR and VIS, once every 5 days at each diurnal phase and location.
5. Radiance Threshold for both IR and VIS for each image pixel.
As can be seen from the above, the GEO satellite time resolution is exploited in the scene classification. Thresholds are only part of the procedure. -40 -
Table 2.3. Global Annual ERB parameters. SF = Shape Factor, NF = Numerical Filter.
Source Albedo LW References % (W/m2 )
Nimbus 3 28.4 240.7 1 Nimbus 6 ERB WFOV 31 234 2 Nimbus 7 (1978-79) ERB NFOV 32.9 232.7 3 Nimbus 7 (1978-79)ERB WFOV SF 30.5 228.8 3 Nimbus 7 (1979-80)ERB WFOV SF 30.1 228.9 3 Nimbus 7 (1980-81)ERB WFOV SF 29.9 227.5 3 Nimbus 7 (1981-82) ERB WFOV SF 30.3 226.0 3 Nimbus 7 (1982-83) ERB WFOV SF 30.1 226.2 3 ERBS/NOAA-9 (4 month) NFOV 29.89 234.5 4 NOAA-9 (4 month) NFOV 29.72 234.4 4 ERBS/NOAA-9 (4 month) WFOV NF 28.25 235.25 4 ERBS/NOAA-9 (4 month) WFOV SF 28.83 234.11 4 NOAA-9 (4 month) WFOV NF 28.13 237.1 4 NOAA-9 (4 month) WFOV SF 28.75 235.43 4 NO A A Scanning radiometers 31 244 5
References: 1, [Raschke et al., 1973] 2, [Jacobowitz et al., 1979] 3, [Kyle et al., 1986] 4, [Kyle et al., 1990] 5, [Hartmann et al., 1986]. -41 -
2.3 Summary
Table 2.3 shows the globally, annually averaged albedo and LW flux for the various satellite systems discussed above. Most of the albedoes are comparable, although the NO A A ERB stands out especially because SW anisotropy was not corrected. Nevertheless, the values of albedo and LW flux are very similar in the global, annual mean.
This review has shown that each satellite mission was built on knowledge gained from the experience of the previous mission. Nimbus 3 accuracy was increased by the use of different diurnal models for different surfaces developed from Nimbus 2. Nimbus
6 and 7 made further improvements by measuring simultaneously the incoming solar radiation, thus making derived albedoes more accurate. A biaxial scanner was included to measure the earth’s anisotropy which led to the development of ADMs and AEFs for future missions. Additional instruments on Nimbus-7, i.e. TOMS and THIR, aided in cloud classification. The ERBS satellite in the ERBE mission sampled the diurnal cycle over a period of 36 days, while NOAA-9 and NOAA-IO provided observations at the poles. The ScaRaB mission flew instruments with narrow band visible and infrared sensors in order to address the cloud identification problem. It is only one satellite, however, and has poor diurnal sampling. CERES has opted for sun-synchronous satellites in the morning and afternoon which carry, in addition to a SW and Total broad band sensor, one narrow band IR sensor for scene classification. CERES will also be located on TRMM which will allow daily sampling over a few days but is limited in latitude to ± 30° . CERES instrument will also be on on board the Space Station. Colocated measurements with GOES may also supply the needed diurnal variation information [Charlock et al., 1994].
Uncertainties in the applicability of Nimbus-7 derived ADMs to a satellite with a different orbit led to the inclusion of another bi-axial scanner for the planned CERES project. The CERES satellites are to have imagers on board as well as ERB instruments. The importance of regional studies has lead current and future missions to drop the use of WFOV instruments. It has been shown, in fact, that the ERBE scanners worked better than the non-scanners. The average lifetime of scanners is about 2 years, the ERBS scanner is the exception and operated for 5 years. A long term ERB data set - 42 - can be compiled from different mission satellites if the data is carefully calibrated and processed. Using GEO satellites as a source of ERB parameters has been attempted but are more complicated and uncertain due to the necessity of the narrow band to broad band conversion. It would be better to have this facility on board a geosynchronous satellite, i.e. the future GERB instrument on METEOSAT. A topic that has not been addressed by past missions, but is only beginning with the
ScaRaB Meteor 3 mission and planned for the CERES - EOS mission, is the separation of clouds according to type, height and thickness. This needs to be done in order to expand the ERB studies from two dimensions to three dimensions. Chapter 3
Review of existing and Planned ERB Instruments
3.1 Introduction
The goal of this section is to describe instruments which are used to estimate the earth’s radiation budget. Included will be instruments which supply necessary information to be used in the processing and interpretation of the ERB data. This includes, for example, those aiding in cloud detection and surface properties and those atmospheric properties that are necessary to calculate the radiation budget at the surface and within the atmosphere.
3.1.1 Top of Atmosphere Radiation budget
The TOA ERB is perhaps the easiest to measure globally since instruments in orbit around the earth can measure directly the incoming and reflected solar energy as well as the outward emitted longwave. However, the measurements made by instruments in space are subject to the uncertainties in the calibration, conversion of filtered to unfiltered radiances, conversion of radiances to fluxes and spatial sampling. Necessary for this correction are correct scene identification, including knowledge of cloud type and field being observed.
3.1.2 Atmospheric Radiation budget
The purpose of knowing the atmospheric budget is to understand where the reflected or emitted energy is absorbed within the atmosphere. This affects the dynamics of the
43 - 44 - climate. The atmospheric radiation budget must be calculated using radiation models. These models incorporate measured information to calculate flux divergences at different atmospheric levels. This information is: atmospheric profiles of temperature, pressure, water vapor, ozone and infrared absorbing trace gas species. Techniques for estimating optical depth and scattering processes require information about: cloud water content and phase, cloud particle type and size. The presence of aerosols is also important because they reflect SW radiation and also affect cloud albedoes.
3.1.3 Surface Radiation Budget
A global surface radiation budget is also estimated by using radiation models. The surface information necessary to calculate surface fluxes of SW and LW is (ideally): snow cover, age of snow, sea ice cover, vegetation, sea roughness, topography, soil moisture, surface temperature, emissivities, reflectivities. In order to estimate reflectivities and emissivities from radiation measured above the atmosphere, the properties of the inter vening atmosphere must be removed. The data necessary to subtract the atmospheric effects are very close to those needed to calculate the atmospheric radiation budget.
3.2 Summary of ERB Instruments
The approximate nadir IFOV size of various scanning ERB instruments, their wave length ranges of observation and methods of calibration are listed in Tables 3.1, 3.2, and 3.3. In order to obtain a long term ERB data set, all sensors must be properly calibrated. Optimally, one has a pre-launch ground calibration, on-board calibration, inter-instrument calibration (between instruments on same satellite as well as on different satellites), and simultaneous satellite overflight and surface measurements for the duration of the flight.
These precautions allow detection of sensor degradation and differences in sensor response between successive satellites. Optimally, ground calibration is done using a vacuum cham ber and relates the measurements to standard units (i.e. Longwave for ScaRaB). Methods of on-board calibration typically use the sun and space as reference sources, as well as lamps and black bodies inside the satellite. Identical shuttered sensors which are opened - 45 -
Table 3.1. Instruments to estimate BRB : Shortwave Flux.
Name Satellite Wavelength IFOV Calibration Mission Range at Nadir
MRIR Nimbus 2 0.2-4.0pm 50km Consistency by observation over desert areas MRIR Nimbus 3 0.2-4.8pm 50km Consistency by observation over desert areas
BRB Nimbus 6 0.2-4.8/zm 100km Pre-flight, On-board Calibration problems
BRB Nimbus 7 0.2-4.8/tm 100km Pre-flight BRB ERBE 0.2-5. Opm 35km,48km On-board, Three satellite inter-comparison calibration necessary ScaRaB ScaRaB - 0.5-0.7 pm 60km On-board METEOR 0.2-4.0pm CERES EOS 0.3-5.0pm 25km On-board
AVHRR “ NOAA 0.58-0.68pm 1.1km None 0.725-1. 1pm 3.55-3. 93pm MVIRI METEOSAT 0.4 -1.1pm 2.5km Absolute Calibrated instruments on aircraft co-located with Measurements VISSR GOES 0.55-0.75pm .9km None GERB MSG 0.35-4.0pm 48km On-board 0.35-30pm SEVIRI MSG 0.56-0.71pm 3km Vicarious 0.74-0.88pm 1.50-1.78pm
“Numbers vary slightly with different NO A A missions [Kramer, H. J., 1994]. - 46 -
Table 3.2. Instruments to estimate ERB : Longwave Flux.
Name Satellite Wavelength IFOV Calibration Mission Range at Nadir
MRIR Nimbus 2 5.0-30.0/zm 50km On-board MRIR Nimbus 3 4 IR channels 50km On-board ERB Nimbus 6 4.-50pm 100km On-board Calibration problems ERB Nimbus 7 4.5-50pm 100km On-board ERB ERBE 4-50pm 35,48 km On-board, Three satellite inter-comparison calibration necessary
ScaRaB ScaRaB - 0.2-50pm 60km On-board METEOR 10.5-12.5pm CERES EOS 0.3-200pm 25km On-board 8.0-12.Opm AVHRR “ NO A A 10.3- 11.3pm 1km On board 11.4- 12.4pm MVIRI METEOSAT 10.5-12.5pm 5km 5.7-7.5pm VISSR GOES (Spinner) 12 IR Channels 7/14 km On-board Imager GOES (3-axis) 3.8- 4.0pm 4/8 km On-board 6.5- 7.0pm 10.2- 11.2pm 11.5- 12.5pm GERB MSG Total-SW 48km On-board SEVIRI MSG 3.40- 4.20pm 3km On-board 8.30- 9.10/^m 9.80-11.80)Lim 11.00-13.00/zm 5.35- 7.15pm 6.85- 7.85pm 9.46- 9.94pm 13.04-13.76pm
“Numbers vary slightly with different NO A A missions [Kramer, H. J., 1994]. -47 -
Table 3.3. Instruments to estimate ERB : Incoming Solar.
Name Satellite Wavelength Calibration Mission Range
ERB Nimbus 6 10 narrow channels On-board calibration problems
ERB Nimbus 7 10 narrow channels On-board self-calibrating OHIO ERB ERBE On-board ACRIM EOS, BARS On-board SMM On-board
Table 3.4. Summary of Lifetimes of ERB Scanners
Satellite Eq. Crossing Time Operation
Nimbus 2 Local noon 16 May to 28 July 1966
Nimbus 3 11:30 16 April to 15 August, 1969 3 to 17 October, 1969 21 January to 3 February, 1970
Nimbus 6 11:09 July and August 1978 Nimbus 7 11:51 16 November 1978- 22 June 1980 ERBE: ERBS 36 day cycle 5 November 1984 - February 1990 NOAA-9 14:30 January 1985 - January 1987 NOAA-10 07:30 12 November 1986 - May 1989 ScaRaB 190 day cycle,prograde 25 January 1994 - February 1995 -48 - over different lengths of time are used to test the stability of the sources. Table 3.4 lists the equatorial crossing times and lifetimes of past ERB satellites.
3.2.1 Nimbus 2 and 3
The Nimbus-2 ERB instrument was a Medium Resolution Infrared Radiometer (MRIR) with measurements in spectral ranges 0.2-4.0 /mi and 5.0 to 30.0 /tm . The LW measure ments were calibrated on board. The SW measurements were uncalibrated. However, observations over deserts were used to obtain some estimate of the instrument ’s stability.
Nimbus 3 also used a similar MRIR with 4 narrow band channels (6 .0-7.0 /tm , 9.1-
12.1 /tm , 14.0-16.3 /tm , 20.2- 23.9 /tm ) and one SW channel (0.2/tm to 4.8/tm ). The 5th channel was uncalibrated whereas the 4 infrared channels were calibrated with an internal calibration source and space. [Raschke et al, 1973].
3.2.2 Nimbus 6 and Nimbus 7
Nimbus 6 and 7 had identical ERB packages. However, the calibration was improved for Nimbus 7 when problems with the Nimbus -6 measurements were discovered. Calibra tion of instruments consisted of a pre-launch calibration. There was no inflight calibration for the incoming solar channels. For the NFOV instruments, the SW sensors were exposed to a diffuser plate which is illuminated by the sun. The LW sensors were calibrated by pointing towards space and an internal blackbody source. Additional checks were pos sible by comparing fluxes between the collocated WFOV and NFOV instruments. (See [Jacobowitz et al, 1984] for more detail). The Nimbus-7 ERB instrument was located on a scanner which was able to rotate and therefore collect information on the angular characteristics of the reflected SW radiation. Despite this improvement, the sun-synchronous orbit and noon equator crossing time of Nimbus-7 placed some limits on the use of these models for satellites in different orbits.
3.2.3 ERBE
The calibration of the scanning ERB instruments was performed along each scan and bi-weekly. The pattern for the scan was: space view, earth view, and then internal -49 - calibration source view. The separate channels were calibrated as:
1. Total: Space, black body, Sun with MAM (command from surface)
2. Longwave: Space, black body, night comparison with Total
3. Shortwave: Space, SWIGS (command from surface), dark side earth (daily), intercomparison with non-scanner, Sun with MAM (command from surface).
The calibration for the non-scanners was performed bi-weekly and consisted of an internal calibration source, space view and sun view [Langley DA AC, 1995]. ERBE consists of a three satellite system, for which all the instruments had to be intercalibrated. The two sun-synchronous satellites, NOAA-9 and NOAA-IO provided global sampling, including the poles. ERBS, in a precessing orbit, provided diurnal sam pling over the scale of approximately one month A fact also to be mentioned is the presence of SAGE instrument on ERBS which enabled ozone limb sounding measurements to be made at all latitudes, due to the satellite’s precessing orbit.
3.2.4 ScaRaB
The ScaRaB instrument measures radiation in four spectral bands:
1. visible 0.55-0.65 /zm
2. solar 0.2-4 /zm
3. total 0.2-50 /zm 4. window 10.5-12.5 /zm
The ScaRaB visible and window channels are intended to aid in cloud identification. Pre-flight calibration is done in a vacuum facility. Once in flight, calibration is done during each scan which includes a view of space not in the direction of the sun, earth view and view of internal source. Black body simulators are used for the Total and IR channels and a series of lamps are used for the solar and visible channels [Kandel, 1994]. Deterioration of lamps is checked by having extras which are periodically (i.e. once a month) measured. -50 -
3.2.5 CERES
The CERES sensor has approximately half the size of previous ERB dedicated in strument IFOV. High spatial resolution means that a sensor must be able to respond to changes in radiation within each IFOV. The higher resolution does not alleviate the prob lem of what defines a clear and cloudy scene, however, due to the variable scale of clouds [Wielicki and Parker, 1992]. CERES is intended to have improved ground and on-board calibration by a factor of 2 from ERBE. The internal calibration sources consist of a solar diffuser plate (MAM), blackbody source for the LW and a SWIGS SW source (Tungsten lamp) [Lee, R. B. et al., no date]. The CERES instrument will be located on a scanner with capabilities of rotating, such that data can be collected for improvement of angular directional models. The applicability of these models to instruments on satellites with different crossing times may suffer the same problems as the models for Nimbus-7. For this reason, cloud type is an important parameter to measure. Co-located imager data will enable more accurate estimates of cloud fraction and type.
3.2.6 ISC CP: NOAA, METEOSAT, GOES, GMS
Although ISCCP is not a project devoted to the ERB , it has produced ERB prod ucts based on the synergy between instruments on board sun-synchronous satellites and geostationary satellites. To do this, all instruments flying at the same time were made consistent with one NOAA satellite. This satellite was then made consistent with NOAA- 7, which had based its “calibration” based on coincident surface observations over a desert area.
3.2.7 GERB
GERB is a ERB instrument designed to operate on board a geosynchronous satellite.
Aside from the precessing satellite ERBS, GERB will be the only ERB instrument to address the diurnal variation of the ERB with a calibrated, broad band instrument. The on-board calibration is performed with an internal black body, space view and Solar input (diffuser). It is intended to be spatially and temporally colocated with high resolution -51 - imaging data from SEVIRI, which is one of the operational instruments on the satellite.
3.3 Instrument Types for multi-satellite ERB Missions
Table 3.5 lists the measurements which are desired for a successful ERB mission and examples of current instruments which may be able to fulfill these requirements. The instruments for the Top of Atmosphere study, aside from the broad band radiometers, are used for scene identification which is necessary for the unfiltering of the radiances and the radiance to flux conversion. All other instruments measure parameters and properties of the atmosphere which will be used in radiative transfer models to calculate fluxes in the atmosphere and at the surface.
3.3.1 Measured Parameters
Cloud information is derived from high spatial and spectral resolution imagers with typically one visual channel and one infrared. Although ERB instruments were on board the NOAA-9 and NOA A-10 operational satellites, no attempt has been made to use co-located AVHRR data to obtain an ERB data set, although it has been used for exper imental studies. The temperature detecting capabilities of the IR window channel gives some indication of the underlying surface being observed by the ERB instrument. Espe cially important in this respect is the determination of cloud top height and the presence of snow and ice on the surface. This is important in polar regions where cloud discrimi nation from the surface is very poor. The visible channel aids in areas where cloud top temperature differs little from sea surface temperature. Atmospheric information, particularly profiles of temperature, pressure, water vapor and ozone, can be measured using sounders which consist of sensors which can measure at many wavelengths (for example, HIRS has 20 different channels). Absorbing character istics of greenhouse gases are used to estimate the temperature and pressure at different levels in the atmosphere. This information becomes less accurate when clouds are present because the derivations rely more heavily on the microwave measurements. Although these measurements are not affected by the clouds, there are only four channels. These -52 -
Table 3.5. Parameters and Instruments for ERB studies.
Desired Information Type of Instrument Example
Top of Atmosphere Broad Band SW, LW Flux Broad Band SW, LW Radiometer ERB, ScaRaB, CERES,GERB Incoming Solar Radiation Solar Spectrometer, Pyrheliometer ACRIM
Cloud Fraction High Spectral and Spatial AVHRRjSEVIRI (SW Anisotropy correction) Resolution Radiometer Cloud Optical Depth -High Spectral and Spatial AVHRR, SEVIRI (SW Anisotropy correction) Resolution Radiometer -High Spectral MINT Resolution Interferometer Sea Surface Wind Velocity Microwave radiometer, microwave SMMR, ASCAT, (SW Anisotropy correction) scatterometer,altimeter SAR
Sea Surface Temperature Multispectral Microwave radiometer, IASI, MIMR, HIRS, (Scene Identification) IR radiometer ATSR Snow and Ice cover Multispectral IR ATSR, SMMR (Scene Identification) and Microwave radiometer Land vegetation Multispectral Mapper SEVIRI, MERIS, (Spectral unfiltering) ATSR, MODIS
Atmosphere and Surface Atmosphere profiles of Infrared and microwave spectrometer HIRS, SSU, AIRS, T(z), P(z), 03, H20 AMSU, SAGE, SOUNDER Cloud Top height High Spectral and Spatial CPR, AVHRR, Resolution IR Radiometer, ATLID Lidar, Cloud Profiling Radar Cloud Base Height Cloud Profiling Radar, CPR, ATLID Lidar for thin clouds Cloud Microphysics Cloud Profiling Radar, Lidar, ATLID, CPR, Polarization Instruments POLDER Cloud water content,phase Multi-Spectral Microwave radiometer MIMR, MSR Aerosol loading and Lidar, multi-angle high spatial and ATLID, MISR, characteristics temporal resolution radiometer POLDER,EOSP Rainfall Latent heat Rain Radar MIMR Surface albedo High Resolution VIS Radiometer SEVIRI, IMAGER, AVHRR Soil Moisture Microwave radiometer, Radar MIMR, SAR -53 -
have a smaller vertical resolution and lead to a less accurate profile [Stephens, G. L., 1994]. Continuous wavelength spectrometers and interferometers also can be used for profiling as well as determining trace gas concentrations. Information on surface characteristics such as angular reflectance, ice age and veg etation can be derived from various instruments. Microwave instruments need to be distinguished by the frequency at which they measure. Certain frequencies (less than 19 GHz) allow the instrument to view the surface unimpeded by the presence of clouds, thus enabling complete coverage of surface characteristics such as sea surface wind speed and direction, wave heights, snow, ice and vegetation [Wu, Jin, 1995]. Measurements of these parameters along with the ERB measurements should enable improvement in scene identification. Radar instruments operating at frequencies greater than 19GHz have the capability to measure clouds and cloud properties as the frequency increases. Currently being discussed are those at 94 GHz, which, if operating from a satellite, will provide data about cloud base height, a measurement so far unreachable by current methods [IGPO, 1994]. Lidars will also have superior cloud microphysics detecting abilities which can complement the radar measurements, especially in the case of thin clouds. Aerosols have been found to affect the albedo of clouds as well as contributing to the scattered visible radiance being measured by satellites [Kim and Cess, 1993]. The reflec tive characteristics of aerosols require that instruments be used which are able to measure polarization of reflected light. These are only recently being implemented on missions such as ADEOS, which carries the POLDER instrument designed for the measurement of polarization. As can be seen from Table 3.5, several instruments have overlapping abilities. SST can be measured by both IR and microwave radiometers, provided that there is informa tion about the intervening atmosphere for the measurements. The suite of instruments AVHRR, HIRS, MSU and SSU, which currently fly on board NOAA’s operational satel lites, are limited by the poor resolution of the microwave sounder. It can be improved by carrying a microwave radiometer with a higher resolution [Kornbleuh, L.]. Cloud radar is the best instrument type for measuring the bottom height of clouds. -54 -
However, this instrument is less accurate when observing optically thin clouds, especially cirrus. A combination of cloud radar and lidar to observe all cloud situations has been recommended [IGPO, 1994]. A Michelson interferometer has been recommended to esti mate cloud optical depth for the CLIMSAT mission [Hansen, J. et al, 1992] in the place of a high spatial and spectral resolution VIS, IR radiometer. When deriving aerosol prop erties, a choice between instruments such as MISR and a lidar such as ATLID needs to be based on several factors. MISR consists of nine cameras which view the earth at nine angles in four spectral bands [Wang and Gordon, 1994]. ATLID, with a spatial resolution of about one kilometer, would be located on a scanner, but due to technical considera tions, would have a gap of about 10km between measurements [Ackermann, J., 1995]. So considerations of spatial resolution and sampling capabilities must be included as well as capabilities of measuring the aerosol properties themselves.
3.3.2 ISCCP as a Model ERB
Although there have been several ERB missions during the past 20 years, as described in Chapter 2, none were able to quantify the error sources for the evaluation of a complete three dimensional ERB data set. The ISCCP ERB , although containing errors due to narrow band to broad band flux conversion, is able to quantify the uncertainty in the available data. Although clouds play a major role in radiation studies, they need to be removed from a signal that comes from the surface. Land surface albedo is the largest uncertainty in upwelling SW Flux and cloud detection errors are largest sources of error for downwelling SW at the surface [Zhang, Y.-C., et al, 1995]. Errors in upwelling LW fluxes depend strongly on land surface temperature accuracies and the largest errors in downwelling LW fluxes are atmospheric temperature and humidity. In general, largest errors occur over land and are attributed to lack of spectral information of surface albedo, emissivity and temperature [Zhang, Y.-C., et al, 1995] . ISCCP is the only satellite-based project which attempts to provide estimates of the three dimensional ERB and which also involves the synergy between different satellites which is necessary for future successful ERB missions. Since it addresses all the problems that will be necessary to solve for a future ERB dedicated mission, it is used here as an - 55 -
Table 3.6. Data Input for ISCCP ERB .
Parameter Instrument/ Time Space Comments Source of Scale Scale Information
Broad Band AVHRR VIS and IR, 3 Hours 30 km Narrow to Broad SW, LW Fluxes METEOSAT, Band Conversion GOES, GMS
T(z), H20(z) NO A A HIRS analysis Twice Daily 15 km Presence of Clouds increases Error
Snow, Ice NOAA/NESDIS Weekly 100 km MSU Surface T and CS radiance 30 km Cloud Reflectivity composites Contamination of CS Pixels Single Layer Cloud: VIS/IR Analysis 3 Hours 30 km - No Cloud Cloud Top T Base Height, Cloud amount, - Plane Parallel Optical depth Radiation Models Cloud Layer Rawinsonde Season Lat Thickness and surface observations Aerosols Climatology Ozone Column Climatology Month Lat Abundances from SBUV
Vegetation Climatology 100km 8 surfaces Upper Strat T Climatology Upper Trop H20 Climatology -56 -
Table 3.7 Possible Specifications for Multi-satellite ERB Mission.
Parameter Instrument Type Example Further Research
Broad Band Radiometer ERB, ScaRaB, Orbital Sampling SW, LW CERES,GERB Tz, HgOz Infrared and HIRS,SSU, Development of microwave spectrometer SOUNDER,SAGE Algorithms Snow, Ice Multispectral IR and ATSR, SMMR Sampling Microwave radiometer Surface Temperature Multispectral IASI, MIMR, Removal Of microwave radiometer, HIRS, ATSR Intervening IR radiometer Atmosphere Surface Reflectivity High Resolution AVHRR,SEVIRI Removal Of VIS Radiometer IMAGER Intervening Atmosphere Cloud Information: Cloud Profiling Radar, ATLID, CPR Multiple Layer Top T, Width Lidar, Polarization POLDER Clouds, Sampling Amount, Instruments Microphysics Aerosols Lidar, multi-angle ATLID, MISR, Temporal and high spatial POLDER, EOSP Spatial Variation resolution radiometer Ozone Column Limb Sounder, UV SAGE, TOMS, Processing Orbit Abundances Backscatter SBUV for Limb sounders, Processing Algorithms Vegetation Multispectral Mapper SBVIRI, MERIS, Removal Of ATSR, MODIS Intervening Atmosphere Upper Stratosphere T Microwave sounders SSU
Upper Troposphere H2O Microwave and Infrared HIRS, MSU, radiometers Meteosat Precipitation Rain Radar PR Needs Combination of Instruments Surface Wind Speed Microwave radiometer SMMR, ASCAT, Synthesis with and Direction and scatterometer, Other Data altimeter SAR - 57 - example of the shortcomings ERB missions currently face. Table 3.6 shows the parame ters used in the processing algorithms for ISCCR These include satellite measurements colocated in space and time. Also, due to lack of information, data sets based on past measurements and those that cover large spatial and temporal regions are also included. These are referred to as climatologies since there is poor temporal and spatial colocation with satellite measurements used to derive the ERB fluxes. For example, latitudinal, sea sonal averages of cloud thickness are used in the calculation of radiance measurements on a kilometer scale. Although the diurnal sampling has been adequately addressed by the use of a combination of GEO and LEO satellites, the narrow to broad band conversion of radiances is still a source of error. Temperature and humidity profiles can only be derived twice a day for a particular region (depending on the number of satellites flying with the instruments on board). Surface characteristics are affected by unresolved clouds. Important parameters such as cloud thickness, aerosols, ozone abundance, vegetation, stratosphere and upper troposphere temperatures are all based on climatologies. Table 3.7 repeats the parameters listed in Table 3.6 and provides examples of in struments which may be used to supply the information needed. (Note that this is not a listing of all instruments). Also added is information which ISCCP does not include in its processing, such as surface wind speeds. Especially notable is the increased avail ability of instruments which will aid in cloud property measurements. These instruments are available for both LEO (for example, AIRS, ATLID, OPR) and GEO (for example,
SOUNDER, IMAGER, SEVIRI).
3.4 ERB Monitoring at the Top of Atmosphere
The previous discussion included all aspects of the earth’s radiation budget which includes a measurement of surface, atmosphere and top of atmosphere fluxes. This type of measurement system is suited for studies of atmospheric energetics. It is the object of this study to determine a satellite system which is optimally capable of monitoring the Top of the Atmosphere radiation budget. The errors inherent in the long term monitoring of the TOA ERB using instruments on satellites are both random and systematic. -58 -
Table 3.8 LW Error in ERB studies.
Error Source ERBE ScaRaB CERES METEOSAT SEVIRI
Noise 2.9 2 2 2.7 1.7 Spectral 2.9 2 2 4 1.4 Correction Anisotropy 2.3 2.3 2.3 2.3 2.3 Correction Inversion Process 3.7 3.1 3.1 5.3 3.2 Scene 0.5 0.2 0.3 - - Misidentification Total Pixel Error 3.7 3.1 3.1 5.3 3.2 Time Sampling 1.8 3.4 3.4 - - Daily Mean 2.6-4.1 4.0 -4.6 3.7 - 4.0 2.7-S.3 1.6-3.2
3.4.1 Random Error
An estimate of the random errors for the ERB instruments ERBE, ScaRaB , CERES, METEOSAT and SEVIRI have been compiled by [Standfuss et al., 1993] and are shown in Table 3.8 for LW and Table 3.9 for SW. All units are in W/m2 . METEOSAT has larger errors which are due to the narrow wavelength channels. Noise can be reduced by improved sensors and calibration techniques. Spectral corrections can be improved by including more wavelengths of observation, as is planned for SEVIRI. Anisotropy is still a subject of ongoing research and is complicated when models developed by one satellite are used to invert radiances measured by another satellite with different viewing characteristics. The errors are caused by the viewed scenes not matching the average characteristics of the models and by the limited angular sampling of satellite scanners. Scene identification can be improved by including a high resolution visible and infrared narrow wavelength channel on board the satellite. For geostationary weather satellites, scene identification and diurnal sampling are considered to be perfect. The errors for SEVIRI are less than for METEOSAT because the former has more channels - 59 -
Table 3.9 SW Error in ERB studies.
Error Source ERBE ScaRaB CERES METEOSAT SEVIRI
Noise 9.2 6 6 27.4 17.7 Spectral 9.2 6 6 27.4 7 Correction Anisotropy 14.6 14.6 14.6 14.6 14.6 Correction Inversion Process 17.3 15.8 15.8 31.0 24.0 Scene 6.2 2.5 3.5 - - Misidentification Total Pixel Error 18.3 16.0 16.2 31.0 24.0 Time Sampling 5.5 11.3 11.3 - - Daily Mean 14.1 - 19.1 19.6 16.1 21.9-31.0 17.0-24.0
with which to perform the narrow to broad band conversion.
3.4.2 Systematic Error
In order to address systematic errors that can occur in an ERB data set, one can test several orbit combinations for a satellite mission to monitor TOA fluxes. Sampling of the entire earth with measurements made at least twice a day for regions at the equator requires a sunsynchronous satellite. Sampling of the diurnal variation of the ERB would require geosynchronous satellites spaced above each quadrant of the earth’s surface and would exclude observation of the poles. Monthly averages have been the typical time scale for ERB monitoring missions. One satellite in an orbit which samples a region every day but at a different local time throughout the month, called a precessing orbit, will include the diurnal cycle of a region for the month. This assumes that a region has a typical diurnal cycle which does not vary from day to day. This type of orbit, because of its inclination near 35° , will not have information near the poles. The following are samples of combined orbits which can address the spatial and temporal scales of the system being measured: - 60 -
1. Several LEO Sun synchronous with different equatorial crossing times.
2. Several GEO with narrow band instruments to cover the entire globe plus one sun-synchronous broad band sensor LEO for global sampling.
3. One Sun-synchronous LEO and one Processing LEO both with Broad band sensors. 4. Several GEO with broad band instruments to cover the entire globe plus one sun-synchronous broad band sensor LEO for polar sampling.
For TOA monitoring of the ERB , cross-track scanners have been found to be the best for uniformly mapping the earth’s surface but are subject to angular sampling errors. A scanner which can measure in many azimuthal directions, as well as viewing directions, produces the lowest angular sampling errors but has poorer spatial coverage than cross track scanners. Items to consider regarding scan type are the sampling rate (number of measurements per scan), the overlap of pixels, and the IFOV. The sampling rate means the number of measurements made within one scan divided by the time of the scan cycle [Stowe, et al, 1991]. The mathematics of sampling requires that the scale at which one is making measure ments must be half the scale at which the observed system is changing. This concerns the asynoptic nature of observations from satellites and is due to the fact that although one has global coverage, one does not have a global snapshot of the earth’s system taken at one moment. The measurements are all taken at different times and assumed to represent the static situation. In fact, the fact that the atmosphere is circulating and changing at every moment places limitations on the scales of the property being observed. In partic ular, a longterm data set with high spatial resolution but poor temporal resolution does not provide accurate information about the data [Salby, 1989].
This so called “aliasing” problem has typically been studied by comparing monthly average values made from complete scanner sampling to monthly averages determined from nadir sampling only. Several studies have used this sampling problem to justify the use of non-scanners with small IFOV (less than 10km) for measuring atmospheric properties over one month (See, for example, [Hansen, J. et al, 1992] and [IGPO, 1994]). The spatial and temporal scales of ISCCP were chosen with this in mind as well. -61 -
3.4.3 Conversion to TOA Fluxes
As was mentioned above, the choice of scan pattern is highly dependent on the correction of anisotropy of the SW radiances with AD Ms. One has the choice of either using a biaxial scanner and improving the spatial sampling or using a cross track scanner with accurate ADMs. In the case of a geostationary broad band instrument, the ADMs are even more important because there is a constant viewing geometry (except for seasonal variation of solar zenith angle) for each observed region. The next generation ADMs are being developed with the CERES instrument on a bi-axial scanner. There may remain some errors due to the location of the instrument on only one or two sun-synchrous satellites, however. Regarding supplementary information for the TOA instruments, scene identification is most important because this determines which ADM is applied to the radiance. If one wishes to characterize the effect of clouds on the ERB based only on the TOA fluxes, the only purpose of identifying them is to apply the correct anisotropic correction to the SW radiance.
3.4.4 Coverage of the Diurnal Cycle
For daily diurnal variation, a GEO satellite is necessary. It is yet to be determined whether broad band sensors are necessary or whether information such as cloud fraction from the narrow channel instruments is sufficient. A combination of GEO and LEO data is very good [Standfuss et aln 1996], but it is necessary to have a GEO satellite over all hemispheres of the globe, with sufficient overlap to avoid large viewing angle problems. For ERB monthly averages, a precessing satellite with a sun-synchronous satellite seems the best balance between diurnal sampling and global coverage. This assumes that the daily variation is the same for each day of the month. - 62 -
3.5 Summary
Radiation budget instruments were reviewed from past, current and planned missions. Also discussed were types of instruments which are conceptually able to provide auxiliary information that would help improve the accuracy of radiation budget data. A review of the random and systematic sources of error affecting long term monitoring of the ERB was also presented. These error estimates did not include systematic errors related to orbit and instrument sampling characteristics. In the next chapter, we present a parametric tool which was developed to investigate diurnal and angular sampling for various satellite combinations. Chapter 4
Parametric Tool to model ERB Mission Scenarios
4.1 Development Strategy
The scenarios modelled were chosen primarily to demonstrate the time sampling abilities of various satellite combinations crossing the equator at specified local times. We present differences from an ideally measured flux field, which is described by a 12 satellite ensemble with equatorial crossing times one hour apart. This number was chosen because every hour is a reasonable time range and and allowed 24 hour sampling in the LW.
We look at differences ranging from single sun synchronous satellite to 6 sun-synchronous satellites at different time and space levels: instantaneous fluxes at satellite altitude (for single satellie scenarios only), regional daily averages , zonal averages and global averages over one month.
The development strategy used here is to simulate, as much as possible, the processing steps of an actual ERB mission. Therefore, output will be in a form analagous to the
ERBE, such as a simulated S8 file and S9 record 2 file. For S8, the simulated data include scanner viewing geometry and geographic location with the SW and LW radiances at satellite altitude. Specific descriptions of modules, written in C, are described in the User’s Manual which is located in the Appendix of this report.
4.1.1 Data Set and use in model
The METEOSAT ISCCP B2 data set [Rossow and Schiffer, 1991] consists of an array of 416x416 values of longwave and shortwave counts. These are converted to LW and SW
63 - 64 - fluxes and effective cloud cover by a method described by [Standfuss et al, 1993].
The final data set used for this study consists of a maximum of 8 time slots which are a subset of the 48 half hour slots of METEOSAT. Data is therefore available at 3 hourly intervals. First slot of UT day starts at 0000 GMT slot (2230 - 0130), last slot of the UT day 2100 GMT (1930 - 2230). The time of actual measurement is therefore at the center of these 3 hour interval: 0000, 0300, 0600, 0900, 1200, 1500, 1800, 2100. Three Geostationary images nearest UT midnight have no visible channel data. (WCRP) Note that the data is not an average but consist only of data measured from the half hour of the center of the three hour range. The correct data set to be “observed ” by the model satellite is determined in the following way. Referring to Table 4.1, the universal time fraction of day of the Meteosat time slots (ranging from 1 to 48) is calculated and stored at the beginning of the model run. The universal time fraction of day (UT_fod) of the nadir point of the model scan must lie between two of these slots (column 1). When the correct time range is found, the data for the B2 slot index is read and stored in a 416 x 416 dimension array. Note that first index 48 is for the previous UT day. There is no interpolation between two adjacent B2 image data sets. The data used is that belonging to the earlier slot. Future versions of the model will incorporate an interpolation routine. This data array remains open until the nadir time is later than the time of the current Meteosat slot. When this happens, the array is emptied and filled with the data from the next time slots. The last column
refers to the actual universal time at which the data was measured. A limitation of the data set occurs when the Greenwich Meridian is in darkness. Although the ISCCP B2 region covers several time zones, only those at the midpoint of the 3 hour intervals which experience daylight at the Greenwich Meridian have data. Limitations of the dataset are also discussed in [Standfuss et al, 1996].
4.1.2 Temporal Colocation of Data: Treatment of Time in model
The concept of time must be very carefully outlined and defined within the model in order for all subroutines to operate successfully together. Wherever time of day is used, conversion to Universal Time is done. Universal time is defined as 0 at midnight -65 -
Table 4.1. Time Ranges of Meteosat B2 Image Data Sets.
Meteosat Time Slots Slot Index UT Time
Slot 3 > UT-fod > 0.0 48 0000
Slot 9 > UT-fod > Slot 3 6 0300
Slot 15 > UT_fod > Slot 9 12 0600 Slot 21 > UT_fod > Slot 15 18 0900
Slot 27 > UT_fod > Slot 21 24 1200 Slot 33 > UT_fod > Slot 27 30 1500 Slot 39 > UT_fod > Slot 33 36 1800
Slot 45 > UT_fod > Slot 39 42 2100 1.0 > UT-fod > Slot 45 48 2400
and defines the beginning of the day at the Greenwich Meridian. Fraction of day is the fractional part of the Universal time and is calculated by dividing the hour of the day by
24. Julian day is the same as Universal time except that it is offset by 12 hours. This has the advantage of describing the motion of the sun over one day since it is symmetric about noon. The output of the nadir coordinates in the orbital model is in Julian units but these are converted to universal time for the remainder of the model.
Local Solar Zenith Angle Calculation
The local solar zenith angle for a model pixel is determined from the longitude of the observation and the time (in units of universal time fraction of day converted to Julian time for symmetry about the local normal). Every 15° of longitude is one hour in time or a fraction of day value of 1/24. - 66 -
Equatorial Crossing Time and Monthly Variation
The time of equator crossing is often defined in terms of local clock time. For example,
NOAA-9 has an ascending orbit equatorial crossing time of 14:30 local time, Nimbus-7 was 12:00 and ERBS had a variable local time. The variable equatorial crossing time enabled ERBS to observe the same region under different solar zenith angles over the length of the month. This information would be complete if each day of the month had a similar if not equal variation of cloud cover, for example, consistent mid-afternoon convective clouds. This would not provide a good diurnal sampling of a region with random cloud cover, i.e. region affected by frontal systems for one or two days but is relatively cloud free at other times. The importance of equatorial crossing time has been emphasized with reference to the angular directional models used by ERBE and ScaRaB. These models were developed from Nimbus-7 data and therefore have a bias based on the types of clouds observed at different latitudes. Noon ERBS observations at midlatitudes, for example, will be corrected with anisotropic factors which were based on equatorial cloud systems, since, at this , the Nimbus-7 satellite was crossing the equator. In the orbital model, the importance of the equatorial crossing will appear in the calculation of the monthly averaged fluxes. Differences in the observed data set and the truth set will be attributed to the frequency at which a region is sampled under different solar zenith angle conditions. The varying local time of a processing satellite is expected to give a result closer to the truth than a sun-synchronous satellite for the monthly average.
The equatorial crossing time for the satellite flying at any particular moment is output during the run. When designing a mission scenario, the input of the equatorial crossing time is very important. However, it is not an orbital element which serves as the input to the orbital model used in this scenario. We have therefore fitted a curve between the right ascension of the ascending nodes and the longitude at which a satellite crosses the equator in ascending node. This provides an equation by which we can input the desired crossing time and the right ascension is calculated by the model. -67 -
4.1.2.1 Time Colocation of Data and Shortwave Flux Adjustment
The third use of time in this model is when the data is colocated with the model pixels. The model satellite observes an area at a time within the three hour interval of this data set. It is assumed for this study that the meteorology for this time range remains constant. The variable that will change, however, is the albedo. Even though the cloud system is not changing, the measured albedo must be dependent on the local solar zenith angle (or local time) of the satellite model measurement and not the time of the data set. The observed albedo will be corrected by applying a shortwave flux correction to the B2 shortwave flux. This is done in the following way. The known values are shortwave flux, time of data and effective cloud cover. The albedo is corrected using the normalized directional models of ERBE for 5 surfaces (ocean, land, snow, desert and coast) and one overcast. Effective cloud cover will be included as described in the following formula:
M(fq s = M(fA * Ho{ts){n{ts)a ov(G)5 ov{ts) + (1 - n(ts))ageo(0)Sgeo(ts)) ^(u(td)^(0)^(W) + (1 ^
a(ts) = M(ts)//j,0(ts)S0 (4.2)
M is the shortwave flux and n is the effective cover from the ISCCP B2 data, is is the model satellite sample time and td is the sampled time of the ISCCP B2 data. ageo and aov are the normalized albedoes at noon. These are climatic averages and do not change.
80v and 5 geo are the climatic albedoes corrected for local time of model observation. S0 is the solar constant, 1368. W/m2 for this study.
4.1.2.2 Albedo Adjustment for Regional Averages
In order to present a 2.5 degree regional average albedo for a particular hourbox, all TOA albedoes must be corrected to the solar zenith angle at the center of the region and midpoint of the hourbox. This is a small correction for the center of the region but can be large for the center of the hourbox depending on the time of day and the nearness to the local half hour. - 68 -
Cross TrackScan X
X
Figure 4.1 Find four corners of the modelled pixel which will be the looping indices for the colocation algorithm.
4.1.3 Spatial Colocation of Points
Spatial colocation of the ISCCP B2 pixels within the model satellite pixel is performed by testing whether the center of the B2 pixel (defined by the longitude and latitude) lies within the larger modelled pixel. The 416x416 data array which is being observed by the model is too large to be searched for each model pixel. Therefore, the model calculates the corners of a region which includes the model pixel. Referring to Figure 4.1, the earth center angles ip are calculated for points A and B. The longitude (9) and latitude (A) coordinates of this region are calulated from the ip. Subscript M indicates the model pixel coordinates. The geographical coordinates defining the boundary are then converted to (x,y) coordinates for the two dimensional data array. These coordinates define the indices of two searching loops. The corner geographical coordinates are calculated from the earth center angles ip in the following way. - 69 -
O' = 0M- ipAi
9" = 9m + $A"
X1 = Xm — ipB' X" = X M + IpB"
The algorithm for spatial colocation is shown below. The tp shown are calculated from the definition of the model pixel ellipse and the distance of the pixel from nadir. The data pixel is defined to be within the model pixel when the left side of Equation (4.7) is less than 1. The variables latm and ZonB2 refer to the geographical coordinates of the
B2 pixels.
ipq = acos(sin(latB2)cos(lonB 2)) (4.3)
ip0 = acos(sin(XM)cos(dM)) (4.4)
ipr = o,cos(cos(Xm — latB2)cos(lonB2 — 9m)) (4.5)
Q _ Mq - Mr - Mo (4.6) fpr - 2tpo
Rearth^Pr < 1 (4.7) bminoramajor ~ COs(f - aCOs(/3))
4.1.4 Input Files
Information that is independent of the type of mission scenario runs is contained in header files. This includes earth physical constants, 1r, definitions of file names and prototypes of the subroutines which make up the model. There are two input files which need to be changed to define the type of mission scenario one wishes to run : run_parameters.list and elements.satellite. The first contains the information relevant to the entire run, as outlined in Table 4.2. The second contains the information relevant to each satellite, as outlined in Table 4.3. -70 -
Table 4.2. Example of input parameters for each run.
Parameter Value Comments
Version number 8 Version of Model Initial date (yyddd) 94121 Data Available
Save s8 files 1 = yes,0= no 1 Space limitations on Disk
Number of satellites 2 For entire run
Scanner status 0 = off, 1= on 1 Testing
Instrument status 0 = off, 1= on 1 Instrument Errors
Adm.correction 1 ADM errors
Include random adm errors 1 = yes,0= no 0 Testing
Output run information to screen l=yes, 0=no 0 Information on Screen
First ut day (inclusive) 1 First Day Last ut day (inclusive) 30 Last Day Left Ion (-180) -50.0 West Longitude Boundary Right Ion (+180) 50.0 East Longitude Boundary Lower lat (-90) -50.0 South Latitude Boundary Upper lat (+90) 50.0 North Latitude Boundary -71 -
Table 4.3. Example of parameters specific to each satellite.
Parameter Value
Start time 0000 End time 2400 Inclination [° ] 98. Local equator crosssing 0900 Height of satellite in kilometers. 800. Sensor a (along track) 4.0 Sensor b (cross) 3.5 Scanner step number 63
Scan step resolution (degrees) 2. Scan step time between nadir points (seconds) 6. -72 -
4.1.5 Output Files
Final output of a mission run is in the form of 2.5° regional averages for each local day of observation. (This grid is defined in a header file) Intermediate output files are the radiance data. This data is not retained for the entire run because they are too large (20 MBytes per local day). The format of the data files which contain the radiance data has been designed with the production of a time-space averaging matrix in mind. A time/space averaging matrix for each region is created with the size 31x24. The 24 hours for the day are defined in the local time reference. Data handling is ideally to create an array of size longitude, latitude, hourbox and days. This size (144 x 72 x 24 x 31) is too large for current SUN computers. We therefore remove one dimension by calculating the local day for each region and write the data to a file numbered by the local day. In this case, all radiances for a local day are written to the same data file. There remains then only a three dimensional array based on longitude,latitude and hourbox. After the timespace averaging matrix is complete for a region, the radiance data file is deleted. There are different levels at which averages are made:
1. Scanner Radiances
2. Hourly Averages (Regional) 3. Daily Averages (Regional)
4. Multiple Day Averages (monthly) (Regional)
Regional means that all averages are within one GRID region. Table 4.4 shows what information will be available for each level.
4.1.6 Diurnal Averaging
The diurnal averaging is performed on the time/space averaging matrix for each region and local day. For the LW, a simple average of all available hourboxes with data is done. The SW is calculated in the following way. The data in each hourbox is separated and averaged for each cloud classification. In this study there are four cloud classifications: clear (0 % to 5 % cloud cover), partly cloudy (5 % to 50 % cloud cover), mostly cloudy - 73 -
Table 4.4. Data Collected at Different Program levels.
Level 1: Scanner Level 2: Hourbox Level 3: Daily Level 4: Monthly
LW B2 pixels LW Model pixels LW hours LW Days SW B2 pixels SW Model pixels SW hours SW Days Hourbox Index Hourbox Index Day Index Day Index Day Index Day Index Row Index Row Index Row Index Row Index Column Index Column Index Column Index Column Index Orbit number Longitude Longitude Longitude Longitude Latitude Latitude Latitude Latitude
Anisotropic Factor 0
do Avg 9q 8 Avg 9
4> Avg (j>
LW Radiance W/m2/ster LW Flux W/m2 LW Flux 6 W/m2 LW Flux W/m2 SW Radiance W/m2/ster SW Flux W/m2 SW Flux c W/m2 SW Flux W/m2 Scene Albedo Albedo Albedo Cloud Cover Scene Fraction Julian date
“Average of Anisotropic factors used to convert B2 SW fluxes to radiances.
6 Daily Average over day with no Diurnal model. “Daily Average over day with fitting performed using scene dependent BRBE direc tional models. -74 -
(50 % to 95 % cloud cover), and overcast. In order to fill in the hourboxes which have no data, we fit the diurnal albedo model for each cloud class to the measured value within each cloud class. The value of the hourbox data essentially raises or lowers the directional albedo curve without changing the shape of the curve. In this study, the ERBE directional albedo models are used [Swttles, et al, 1988]. The average SW flux for each hourbox is then derived using the following equation [Brooks et al., 1986]:
Msw{t) = S0(t)[i0{t) Y, (4.8) i=l where t is the time of measurement of all the pixels and N is the number of cloud classes
(from 1 to 4). Note that each scene type is assumed to contribute linearly to the flux. The SW flux listed has been time adjusted to the local solar half hour assuming that ct%(£) is known and /*(£) is constant within the half hour. An example of the diurnal averaging algorithm is given in Figures 4.2, 4.3, and 4.4 for single and multiple satellite scenarios. Table 4.5 lists the characteristics of the mission scenarios. s6 am and s6 pm (not listed in the table) refer to a mission scenario with 6 sun-synchronous satellites, where all 6 fly in the morning (s6 am) and afternoon (s6 pm) at one hourly intervals. The equatorial crossing times are 0700,0800,0900,1000,1100,1200 and 1300,1400,1500,1600,1700,1800, respectively. The symbols (r2) refer to the hourbox value with measurements and the lines (rl) are the average- between hourbox data for the LW and fitted albedoes for the SW. These are results for only one region which is an ocean region near the equator. The LW simple averaging scheme overestimates the daily LW flux. The actual LW cycle can be seen in Figure 4.3. The SW values are more consistent with each other over all the scenarios. There is still a lot of structure in the daily cycle as measured by the s!2 scenario. Note that the LW and SW peaks and troughs are consistent with one another. -75 -
350
g 300 u. S I 250 eO) ° 200
860 4 700 g 6oo « 500 | 400 S 300 « 200 100
0 0 3 6 9 12 15 18 21 24 Local Hourbox Index
Figure 4.2 Daily cycle for one region: Single Satellite Scenarios. r2 refers to data for one hourbox, rl refers to curves fitted to the hourbox datum. Solid symbols on the left axis indicate the daily average.
700 -
;3cr2 |s3a r2
s3cr1 s3ar1 s3b rl
Local Hourbox Index
Figure 4.3 Daily cycle for one region: Three Satellite Scenarios. Symbol definitions are same as Figure 4.2. - 76 -
300 -
C>12r2 LJs6am r2 400 - I>s6pm r2 ■ s12r1 ■ ------s6am r1 s6pm r1
Local Hourbox Index
Figure 4.4 Daily cycle for one region: Multiple Satellite Scenarios. Symbol definitions are same as Figure 4.2.
4.2 Results of Parametric Tool application to Database
The scenarios studied here are listed in Table 4.5. si, s2, s3, s4, s5, s6 , sl2 indicate 1,
2, 3, 4, 5, 6 , 12 satellites flown at a time, respectively. Further indices are to distinguish different arrangements of equatorial crossing times. These times were chosen primarily to demonstrate the time sampling abilities of various satellites crossing the equator at specified local times. Scenarios based on parameters from previous, current and future ERB missions are also studied. We present differences from an ideally measured flux field, described by a 12 satellite ensemble with hourly observations.
We look at differences ranging from single satellite sun synchronous orbits to 6 satel lite sun-synchronous at different time and space levels: instantaneous fluxes at satellite altitude, regional, zonal and global averages over 31 days. The underlying ISCCP B2 database is that of METEOSAT and restricted to ±50° longitude and latitude. There fore, the term global refers to this area only. The database observed is for the month of May 1994. - 77 -
Table 4.5. Description of scenarios.
Scenario Equator Crossing Time (Local Hour)
sla 9 sib 15
sic 12 s2 9,15 s3a 6,15,18 s3b 6,9,18 s3c 9,12,15 s4 6,9,15,18 s5 6,9,12,15,18 s6 5,7.5,10,12.5,15,17.5
sl2 6,7,8,9,10,11,12,13,14,15,16,17 CERES 10,1330 ScaRaB processing ERBS processing ERBS + NOAA-9 processing, 1430 ERBS + NOAA-9 + NOAA-IO processing, 1430, 0730 - 78 -
4.2.1 Differences for SW Inversion
Instantaneous SW Flux Differences There are four types of instrument status specified at the beginning of each run which affect the TOA fluxes measured by the model satellites.
• Case A: Perfect instrument, no Changes to Flux field.
• Case B: Radiances converted to fluxes assuming Lambertian anisotropic factor (worst case instrument error).
• Case C: Radiances converted to fluxes using ERBE binned anisotropic models and random error.
• Case D: Radiances converted to fluxes using ERBE binned anisotropic models with no random error.
Colocated B2 SW fluxes are colocated into a model pixel and corrected to the time of measurement using ERBE directional models. Each of these fluxes has associated with it an effective cloud cover. This information is used, along with the viewing geometry, to find an anisotropic factor which will be used to convert the SW fluxes appropriate for the time of observation, to radiances. The anisotropic factor is derived from the ERBE ADM tables but interpolated to provide a smooth transition between different angular bins, (from [Standfuss et al, 1996]). These radiances are averaged together to provide the true measurement with units of W/m2/ster .
Case B above converts the radiances to fluxes only by multiplying by tt . This assumes that every surface is isotropically reflecting (Lambertian). Case C applies the state of the art ERBE ADMs to the radiances and uses a random number generator to simulate random errors based on the ADM errors of [Standfuss et al, 1993]. Case D is similar to C but with the random generator turned off. This helps to identify circumstances for large errors based only on the anisotropy values. Observations on a pixel by pixel differences are here presented and discussed which relate errors at the instrument level to errors after averaging. Patterns are similar for relative and absolute errors so only absolute errors are here presented. Figures 4.5, 4.6 and 4.7 show instantaneous SW flux absolute differences for one local -79 - day. Gaps are due to orbit crossing the local day for different regions. Comparing morning with noon allows us to see differences which are attributable to the 9q dependence in the ADMs. We expect Case B to show strong differences according to viewing geometry. This can be seen in the noon orbit, Figure 4.6, where the 6q is close to zero and the anisotropic factors typically greater than one at the nadir. The assumption of a Lambertian reflection would lead to an overestimate of the shortwave flux from the true flux, which is what we see here. Similar overestimation occurs at large viewing and solar zenith angles where limb brightening and sunglint cause strong anisotropy. This is seen in the morning figures. The ADM errors are reduced substantially in Case C, although there are regions where the anisotropic factors are insufficient to account for the anisotropy. One can also discern the nature of the binning of the ERBE anisotropic models in the Case C absolute differences, where the variation of solar zenith angle with latitude is apparent.
Monthly Average Regional Flux Figure 4.8 shows the average longwave and shortwave fluxes for the 31 day average made from the sl2 scenario. This is the reference data set from which all scenarios are compared. It also shows the monthly averaged meteorology.
Results of monthly average differences between mission runs with an ideal and ERBE ADM corrected SW fluxes are shown in Figures 4.9, 4.10 and 4.11 show that differences range between ± 5 W/m2 . They are close to zero in relatively clear land and ocean areas but are very large over coastal areas and areas with persistent cloud cover. Especially interesting is that these differences reduce in magnitude as the number of satellites is increased but the location of these differences remains the same. Therefore the ADM source of error is reduced but not removed by increasing the number of satellites. - 80 -
True Shortwave Flux
•60 -40 -20 0 20 40 60 Longitude
(B-A)
Wm "2 220 200 •60 -40 -20 0 20 40 60 180 160 Longitude 140 120 100 80 60 40 (C-A) 20 0 •20 -40 •60 -80 -1 00 •1 20 •140 *2 •1 60
Longitude
Figure 4.5 Absolute Differences in Instantaneous SW Fluxes: Morning Satellite. Top: True fluxes at the time of measurement; middle: Difference between results obtained assuming Lambertian radiance (B) and the true fluxes (A); bottom: Difference between results obtained using the ERBE ADMs (C) and the true fluxes (A). -81
True Shortwave Flux Wm ‘ ---- 1-1200
-1100 Uif-iooo 9-900
-800
-700 600
500
400
300
200
100 0 -60 -40 -20 0 20 40 60 Longitude
(B-A) 1
Wm*2
•60 -40 -20 0 20 40 60 Longitude
(C • A)
-40 -60 -80 -100 -1 20 -140 -160
Longitude
Figure 4.6 Absolute Differences in Instantaneous SW Fluxes: Noon Satellite. Top: True fluxes at the time of measurement; middle: Difference between results obtained assuming Lambertian radiance (B) and the true fluxes (A); bottom: Difference between results obtained using the ERBE ADMs (C) and the true fluxes (A). -82 -
True Shortwave Flux Wm ‘ 1200
m-1100
-1000
-900
-800
'j4 -700 600 500 400
300
200
100
•60 -40 -20 0 20 40 60 Longitude
(B-A)
Wm'2 350
•60 -40 -20 0 20 40 60 300 Longitude 250
200 ISO (C - A) 100 -50
A‘-T
Longitude
Figure 4.7 Absolute Differences in Instantaneous SW Fluxes: Afternoon Satellite. Top: True fluxes at the time of measurement; middle: Difference between results obtained assuming Lambertian radiance (B) and the true fluxes (A); bottom: Difference between results obtained using the ERBE ADMs (C) and the true fluxes (A). satellite Figure
4.8 scenario.
Regional Latitude
Average
Shortwave SW
Flux Longitude in
W/m -83
2 (top)
and
Longwave
Fluxes
(Bottom)
for
12
-84 -
S1a A-S1a C S3b A-S3b C
-05 •4% -15 -20
S1b A-S1b C S3a A-S3a C
-)0 -25 -25 -20 •20
asi i ■20 -10 0 n 20 JO
S1c_A-S1c_C S3c A - S3c C
3 j If ir'3“ if*-" re
->5 fSrLsrTa-iSl
-2 5 ^ u T . -20
Figure 4.9 SW Flux Differences ( W/m2 ) between Case A and Case C for various Mission Scenarios (see Table 4.5 for explanation of scenarios): 31-day average. -85 -
S2 A-S2 C S4_A - S4_C
S5 A-S5 C S6 A-S6 C
-26 -15-20
h
Figure 4.10 SW Flux Differences ( W/m? ) between Case A and Case C for various Mission Scenarios (see Table 4.5 for explanation of scenarios): 31-day average. 86
ERBS+N9+N10_A - ERBS+N9+N10_C
Figure 4.11 SW Flux Differences ( W/m2 ) between Case A and Case C for real Mission Scenarios (see Table 4.5 for explanation of scenarios): 31-day average. -87 -
4.2.2 Regional Flux Differences between Mission Scenarios
Regional LW flux differences between the sl2 scenario and the other mission scenarios'
are presented in Figures 4.12 and 4.13. The longwave differences are very striking over desert regions for the single satellite scenarios sla (0900), sib (1500) and sic (1200).
The afternoon satellite underestimates the longwave while the noon satellite strongly overestimates the longwave. In this instance, the morning satellite is closer to the true flux. Differences over the oceans are due largely to clouds and occur for all three scenarios.
The s2 scenario difference has the smallest differences of all the other scenarios. The three satellite scenarios show improvement from the single satellite scenarios (s3c shown here). The minimum and maximum of LW flux differences reduces from 30 W/m2 to 5 W/m2 . Overall, an increase in satellite number decreases the differences from the truth. The LW differences for the real missions are shown in Figure 4.14. The scenario with the CERES instrument has larger differences than the s2 scenario and illustrates the importance of equatorial crossing time. ScaRaB also has larger errors than sla and are largest over ‘ desert regions. Note that adding NOAA-9 to ERBS did not affect the errors. However, adding NOAA-10 to NOAA-9 and ERBS reduced differences to 5 W/m2 , consistent with the three satellite scenarios. 88
S12 - S1a S12 - S3b r
*5 •0
S12-S1b S12-S3a
-500) JOO) .>00) *204) >10 03 00) 10 00 20 00 000) 4000 5000
S12-S1C S12-S3C
I
t•ISO
•5) -40 -» -20 .10 0 10 23 00 *0 50
Figure 4.12 Differences in Longwave Flux between 12 Satellite and other Mission Scenar ios, 31-day average, units in Wm~2. (see Table 4.5 for explanation of scenarios). - 89 -
S12-S2 S12-S4 I
1
S12-S5 S12 - S6 I
1
ajw .»» -2J» 21 >j wjj «m v>v)
Figure 4.13 Differences in Longwave Flux between 12 Satellite and other Mission Scenar ios, 31-day average, units in Win-2, (see Table 4.5 for explanation of scenarios). Note that scales are different from Figures 4.12 and 4.14. 90
S12-CERES S12 -ScaRaB
-IS -10 -10
e:
S12-ERBS S12 - (ERBS + NOAA 9)
•2) • IS
it
S12 - (ERBS + NOAA 9 + NOAA 10)
IT
I
Figure 4.14 Differences in Longwave Flux between 12 Satellite and other Mission Scenar ios, 31-day average, units in Wm'2. (see Table 4.5 for explanation of scenarios). - 91 -
The shortwave differences, shown in Figures 4.15 and 4.16, show different patterns from the longwave. The noon satellite (sic) has smaller differences than either the morning or the afternoon satellites. These differences seem to more associated with changing meteorology. The s2 scenario has good results. Comparing s2 and s3c, one can see the slight help a noon satellite can provide. The symmetric scenarios s3c, s5 and s6 show the least differences. s4, which lacks the noon satellite, shows larger differences. To see how real missions fair, we present differences for CERES, ScaRaB, ERBS, ERBS+NOAA-9, ERBS+NOAA-9+NOAA-lO. type scenarios in Figure 4.17. The mission carrying CERES is different from the s2 for some regions. It's afternoon crossing time is closer to noon which may have an affect on the results. Here, as in the LW, the addition of NOAA-9 to ERBS does not show vast improvement. When NOAA-9 and NOAA-IO are added to ERBS the results improve. ScaRaB resembles the sla scenario. - 92 -
S12-S1a S12 -S3b
IE
e
S12-S1b S12 - S3a
S12-S1C S12-S3C
Figure 4.15 Differences in Shortwave Flux between 12 Satellite and other Mission Sce narios, 31-day average, units in Wm"2. (see Table 4.5 for explanation of scenarios). -93 -
S12-S2 S12-S4
S12-S5 S12-S6 nr”
Figure 4.16 Differences in Shortwave Flux between 12 Satellite and other Mission Sce narios, 31-day average, units in Wm'2. (see Table 4.5 for explanation of scenarios). - 94 -
S12-CERES S12 - ScaRaB
-25 -23 -15
S12-ERBS S12 - (ERBS + NOAA 9)
S12 - (ERBS + NOAA 9 + NOAA 10)
K
Figure 4.17 Differences in Shortwave Flux between 12 Satellite and other Mission Sce narios, 31-day average, units in Wm'2. (see Table 4.5 for explanation of scenarios). -95 -
4.2.3 Zonal Flux Differences
Longwave
The LW flux differences averaged over longitude are shown in Figure 4.18. The largest
LW error for the noon and afternoon single satellite, -6 W/m2 and -14 W/m2 respectively, occurs at latitudes where the Sahara and Saudi Arabian desert are located. These differ ences are due to inadequate diurnal sampling of the surface. Scenario s2, a combination of sla and sib, shows small differences, indicating that the average between the cool morning measurement and warm afternoon measurement is sufficient for calculating the diurnal average. The three satellite scenarios, which add to the s2 scenario, an early morning (s3b), late afternoon (s3a) and noon (s3c) satellite, show smaller differences from the single satellite scenarios. S3b is worse than sla because its LW values are weighted towards morning hours. s3a and s3c differences are comparable ( both around 2 W/m2 ) but of different sign. Adding a noon satellite to s2 causes larger differences. However, s4 shows differences similar to the unsymmetric scenarios s3a and s3b, even though it lacks a noon satellite. s5 and s6 perform very well, with zonal differences less than 0.5 W/m2 . Regarding the real simulations, results are similar to the regional discussion. CERES shows larger differences than s2 by a factor of 10. ScaRaB differences are similar to those of the sib scenario. The ERBS, NOAA-9, NOAA-10 combination is very good and consistent with the s3b and s3c scenarios.
Shortwave The satellite scenarios showing SW differences are shown in Figure 4.19. In this case, the noon satellite shows smallest differences. Scenarios sla and sib have opposite signs which illustrates the effect of meteorology on single satellite missions. The s2 and s3c have comparable differences (less than 1 W/m2 ). The addition of early morning and late afternoon satellites to the s2 scenario (s3b and s3a respectively) causes an increase of differences. This is due to the large 6q at these times. The roughly equal differences for the s2 and s4 scenarios suggest that the presence of a noon satellite affects the SW results more than the LW. Wm" Table Figure
2
. 4.5 4.18 Q for Regional explanation -45 -45 -30 -30 zonal Scenario Scenario Scenario -15 -15 Latitude Latitude Scenario Scenario Scenario Scenario of 0 0 differences 12 12 12 scenarios): 15 15 12 12 12 12 - - - Scenario Scenario Scenario - - - - Scenario Scenario Scenario Scenario 30 30 45 45 1c 1b 1a 6 5 4 2 between - Differences 96 - 12 satellite of -45 -45 31-day S12 Scenario Scenario Scenario Scenario -30 -30 - and (ERBS Scenario Scenario Scenario -15 -15 Latitude Latitude 12 12 12 12 average Mission - - - - + 0 0 ScaRaB (ERBS CERES ERBS NOAA 12 12 12 r 15 15 - - - | Scenario Scenario Scenario + r 9 NOAA 30 30 + of Scenarios NOAA longwave 45 45 3c 3b 3a 9) 10) (see in - 97 - The real scenarios show similar results. The CERES mission combination is compa rable to the ERBS, NOAA-9, NOAA-IO combination. ERBS alone does better than in the LW. However, this seems more related to the scatter of the ERBS differences about zero which average out zonally. For example, the differences for ScaRaB are mostly negative (See Figure 4.19) and this shows in the zonal averages. - 98 - Scenario 12 - Scenario 1a j Scenario 12 - Scenario 3a Scenario 12 • Scenario 1b ! i Scenario 12 - Scenario 3b Scenario 12 - Scenario 1c | Scenario 12 - Scenario 3c -45 -30 -15 0 15 30 45 -45 -30 -15 0 15 30 45 Latitude Latitude Scenario 12 - CERES Scenario 12 - Scenario 2 Scenario 12 - EBBS Scenario 12 - Scenario 4 Scenario 12-(ERBS +NOAA 9) Scenario 12 - Scenario 5 S12 -ScaRaB Scenario 12 - Scenario 6 S12 - (ERBS + NOAA 9 + NOAA 10) 5 "=! E 3 J 2 — c 0) o c CD -45 -30 -15 0 15 30 45 -45 -30 -15 0 15 30 45 Latitude Latitude Figure 4.19 Regional zonal differences between 12 satellite and Mission Scenarios (see Table 4.5 for explanation of scenarios): Differences of 31-day average of shortwave in Wm-2. - 99 - Table 4.6. Global Shortwave and Longwave Average Fluxes (W/m2 ). Scenario A: SW Mean dbcr C: SW Mean ± sla 86.67 ± 30.42 87.52 ± 30.56 289.97 ± 61.85 sib 76.36 ± 24.37 76.85 db 24.81 291.24 ± 63.64 sic 80.24 ± 27.59 80.54 ± 27.68 294.19 ± 65.04 s2 80.12 ± 26.42 80.71 ± 26.67 289.91 ± 61.95 s3a 76.36 ± 24.25 76.80 ± 24.65 288.71 ± 61.67 s3b 84.49 ± 28.74 85.23 ± 28.77 288.35 ± 60.98 s3c 79.75 ± 26.67 80.22 ± 26.83 290.30 ± 62.21 s4 79.85 ± 26.21 80.42 ± 26.43 289.10 ± 61.51 s5 79/50 ± 26.49 79.94 ± 26.63 289.54 ± 61.78 s6 79.32 ± 26.18 79.55 ± 26.41 289.44 ± 62.02 ceres 80.40 ± 26.99 80.84 db 26.70 291.38 ± 62.77 scarab 81.85 ± 27.88 81.94 ± 27.96 292.33 ± 63.35 erbs 81.30 ± 25.58 81.97 ± 25.45 274.84 ± 74.70 erbs_n9 76.59 ± 26.07 76.89 db 26.42 275.14 ± 74.39 erbs_n9_nl0 77.96 ± 26.84 78.56 ± 27.27 273.26 ± 73.06 sl2 79.24 ± 26.29 79.82 db 26.33 289.74 ± 62.00 4.2.4 Global Flux Differences Table 4.6, and 4.7 show global regional averages for the specified scenarios. Global longwave average for the Truth scenario is 289.7 W/m2 . This overestimates global annual values from previous missions which are roughly 230 W/m2 . (Compare with values listed in Chapter 2 Table 2.3). This is due to the area of observation which excludes latitudes near the pole, and lack of seasonal variation. (For one day, the average reduced to 250 W/m2 when the regional average included latitudes up to ± 70° ) The global averages - 100 - Table 4.7. Global Albedo Averages. Scenario A: Mean ±cr C: Mean ±a sla 0.28 ± 0.07 0.29 ± 0.07 sib 0.26 ± 0.06 0.26 ± 0.06 sic 0.27 ± 0.06 0.27 ± 0.06 s2 0.27 ± 0.06 0.27 ± 0.06 s3a 0.26 ± 0.06 0.26 dt 0.06 s3b 0.28 ± 0.07 0.28 ± 0.07 s3c 0.27 ± 0.06 0.27 ± 0.06 s4 0.27 ± 0.06 0.27 ± 0.06 s5 0.27 ± 0.06 0.27 ± 0.06 s6 0.27 ± 0.05 0.27 ± 0.05 ceres 0.27 ± 0.06 0.28 ± 0.06 scarab 0.28 ± 0.06 0.28 ± 0.06 erbs 0.27 ± 0.06 0.27 ± 0.06 erbs_n9 0.26 ± 0.06 0.26 ± 0.06 erbs_n9_nl0 0.26 ± 0.06 0.27 ± 0.06 sl2 0.27 ± 0.05 0.27 ± 0.06 -101 - between the scenarios ranges from 294.19 W/m2 to 273.26 W/m2 , which is a reasonable range based on previous ERB mission estimates. The scenarios involving the ERBS precessing satellite are 10 W/m2 smaller in the LW and comparable in the SW. As has been seen in the other scenarios, the ERBS satellite disturbs the LW diurnal sampling. The SW does not show this problem. The average albedo for Truth is 0.27 and is closer to the global annual averages around 0.29 for previous missions. Again, this could be due to missing polar data (ice) and seasonal variation. The shortwave flux for Truth is 79.82 W/m2 . The range of values is 87.52 to 76.85. The differences between the monthly averages with only different instruments, as described above, are on the order of 1-2 W/m2 . - 102 - Chapter 5 Recommendations for Future ERB Missions 5.1 Summary of Results The parametric tool is able to provide monthly averages of LW and SW flux. By comparison with an ideal observing mission (12 sun synchronous satellites with equatorial crossing times of one hour interval) we have found that three satellite combinations which have equatorial crossing times at mid-morning, noon and mid-afternoon times provide the best SW monitoring. Crossing times near sunrise and sunset should be avoided. For the LW it is sufficient to perform measurements from a morning and afternoon satellite over clear ocean areas. However, a LW diurnal model is a necessity over land surfaces, if one wants to have a two satellite mission. Variable cloudiness throughout the day would not be measured by a two satellite system', however. Comparison of missions consisting of two satellites but a different combination of equatorial crossing times produced slightly different results. A better time resolved database would be necessary to prove these results significant. Table 5.1 shows the average and variability (a) of SW flux differences over the entire range of observation for different anisotropic models used for SW inversion. These values are obtained by finding the difference in SW flux for a region observed with an ideal instrument (Case A, as described in Chapter 4) and one whose flux is corrected using the anisotropic corrections listed in the first column (Case B is Lambertian and Case C is the ERBE ADM). The instantaneous values are made by finding the difference in fluxes for each pixel. The daily and monthly values are made from the difference between 103 - 104 - Table 5.1. Average SW Flux Differences and Variability at different time and space scales Units W/m2 . Time is local equatorial crossing time ADM Time Instantaneous ±cr Daily ±a Monthly dbcr Lambertian 0900 14.42 ± 60.55 -1.39 ± 19.34 -1.11 ± 10.93 ERBE-ADM 0900 -1.71 ± 19.41 -0.64 ± 3.86 -1.07 ± 1.84 Lambertian 1200 21.08 ± 32.38 5.79 ± 8.10 5.99 ± 5.43 ERBE-ADM 1200 0.73 ± 14.34 -0.03 ± 2.75 -0.37 ± 1.16 Lambertian 1500 2.81 ± 67.95 -3.40 ± 22.46 -3.46 ± 12.15 ERBE-ADM 1500 -2.97 ± 18.90 -1.02 ± 4.26 -0.55 ± 1.81 regional fluxes. The average and variability of the differences decreases with increase in the time scale and the use of improved anisotropicfactors. Note that there is a difference depending on the equatorial crossing times. This can be attributed to the dependence of anisotropic factors on 9q and clouds. Regional flux differences between the truth scenario and the other mission scenarios are shown as a function of satellite number in Figures 5.1 and 5.2 . These figures show essentially that the average differences approach zero and the variability of the differences is also reduced, as the number of satellites is increased. For single satellite scenarios, the variability in the differences is on the order of 10 W/m2 . In the SW inversion comparison, variability due to anisotropic correction is about 1-2 W/m,2 (Table 5.1, last column). This means that 15 % of the final monthly regional variability can be attributed to using the binned ADMs. For multiple satellite scenar ios, the variability in the monthly regional differences decreases to 2.5 W/m2 (5 and 6 satellite scenarios) but the variability due to the ADM inversion remains near 0.9 W/m2 . Therefore, SW inversion errors make up a larger percentage of the variability as sam pling is improved. These estimates in SW inversion errors are minimum because ERBE - 105 - 20.0 ------,------r • ERBS ◄ScaRaB ▼CERES • ERBS-N9 10.0 • ERBS N9 N1C c 2 M a^3 a o.o u lr t ^ b > 4 § o -10.0 -20.0 0 1 2 3 4 5 6 7 Number of Satellites Figure 5.1 Systematic Differences and Variances between LW Truth fluxes and fluxes for different number of satellites. For the single satellite, the crossing times are, 0900 (circle), 1200 (diamond), 1500 (square). The 2 satellite scenario is a right pointing blank triangle. For the three satellite, the crossing times are 0600, 0900, 1500 (circle), symmetric 0900, 1200, 1500 (diamond)and afternoon weighted 0900, 1500, 1800 (square). Filled symbols are model simulations using elements from several existing ERB missions. AD Ms were used to create the SW B2 fluxes, and an interpolated ERBE ADM was used to calculate realistic radiances. It has been shown, however, that the ERBE ADMS are not sufficiently resolved for 6q less than 25° for clear sky oceans [Dlhopolsky and Cess, 1992]. Since this is the first range of the ERBE ADMs, no interpolation can be done. The simplified cloud classification scheme which we applied is also a problem. 5.2 Results and Definition of Optimized Mission Scenarios Results of this study indicate that the limitations of ERB missions are not solely due to temporal sampling but also due to the uncertainties related to ADMs. Improvements, therefore, cannot be made by designing a satellite mission based only on sampling re quirements. Inversion algorithms must be supplied with more accurate ADMs which have - 106 20.0 • ERBS -4ScaRaB ▼ CERES • ERBS-N9 10.0 • ERBS-N9-N1C %o c -10.0 -20.0 2 3 4 5 Number of Satellites Figure 5.2 Systematic Differences and Variances between SW Truth fluxes and fluxes for different number of satellites. (Symbol definitions are same as Figure 5.1) better definitions of scene classification. The instruments on board the satellites must have the capability of detecting the scene classifications for which there exist ADMs. 5.3 Discussion of Possible Application in other climatological Programs. The differences between mission scenarios studied here are very small when looked at on a global average. This is true also for Global Climate models. However, the affect of changing climate at the regional level is both poorly modelled and in the case of ERB measurements, poorly observed. Many current climatological research programs are focusing on the regional scale, i.e. GEWEX (WCRP). A global monitoring mission would need higher resolutiongrid size in order to provide TOA ERB informationfor these regional studies. This would require higher resolution sensors and smaller scan steps on a scanner. As mentioned above, scene classification and ADMs would need to be improved. - 107 - The parametric tooldeveloped in this study can be used in the future to address some of problems inherent in a ERB monitoring mission. Grid size and sensor resolution can be easily varied. Since the tool is modular, any number of tests can be added to it. For example, a LW inversion procedure or a LW diurnal model. The operation of the model can be improved with better time resolution of the observed data set. It is feasible to have a simulated earth-atmosphere system which is created by radiative transfer models and ECMWF meteorology. - 108 - Acknowledgements The authors wish to thank Carsten Standfuss for supplying the programs to convert the Meteosat B2 counts to fluxes, Kenitchi Maruyama for producing the database and Luis Kornblueh for supplying the C version of the Brouwer-Lyddane orbit generator routine used in this study. We thank Mike Wintzer and Paul Ingmann for their many discussions. 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(American Meteorological Society, Boston, Masachusetts). Wielicki, B.A. and Green, R. N. 1989: Cloud Identification for ERBE Radiative Flux Retrieval Journal of Applied Meteorology, 28, 1133-1146. Wielicki, B. A., Parker, L. 1992: On the Determination of Cloud Cover from Ssatellite Sensors: The Effect of Sensor Spatial Resolution. J. Geophysical Res.,97, 12799 - 12823. Wu, Jin 1995: Pairing of Radar Instruments on Satellites Could Provide Optimal Map ping of Sea Surface Winds EOS 3 January, 1995. NASA Langley Distributed Active Archive Center http://eosweb.larc.nasa.gov . - 114 Zhang, Y. -C., Rossoxv, W. B., Lacis, A. A. 1995: Calculation of Surface and Top of Atmosphere Radiative Fluxes for Physical Quantities based on the ISCCP data sets 1. Method and Sensitivity to Input Data Uncertainties. J. Geophysical jRes.,100, 1149 - 1166. Appendix A - 116 - - 117 - A.l User’s Manual Description of the Parametric Tool 1. Set Flags for output files. 2. Read run_parameters.list. See Table A.l. 3. Read geographical scene and ADM external data files and store in arrays. 4. Calculate and store time slot arrays. 5. Calculate and store coordinates of B2 data. 6 . Start Universal Time loop over day: 7. For each satellite: (a) Read elements file. See Table A.2 (b) Calculate nadir coordinates. (c) Operate scanner for each nadir point. (d) Search database for time and space colocation. (e) Average colocated B2 pixels into model pixels. (f) Write scanner data to external file: Table A.3 Level 1. 8. After all satellites fly for three days: (a) Read scanner data for first local day. (b) Invert radiances according to run_parameters.list. (c) Fill Time/Space Matrix for each region: Table A.3 Level 2. (d) Calculate Daily Average for each region: Table A.3 Level 3. (e) Proceed to next day (Step 6 above). - 118 - Operation of Parametric Tool • Setup: 1. Header Files 2. Run Parameters 3. Satellite Parameters • Model Run 1. Compile: make -f Makemonthly 2. Run: monthly 3. Run: monthly_avg The final output of monthly is 31 data files containing daily averages of SW and LW fluxes. Tables A.4, A.4, A.5, A.6 , A.7 list the name and a short description of the C programs, header files (in the order in which they must appear within the C programs), external data sources and output datafiles. -119 - Table A.l. Example of input parameters for each run. Parameter Value Comments Version number 8 Version of Model Initial date (yyddd) 94121 Data Available 1 Save s8 files 1 = yes,0= no Space limitations on Disk Number of satellites 2 For entire run Scanner status 0 = off, 1= on 1 Testing Instrument status 0 = off, 1= on 1 Instrument Errors Adm.correction 1 ADM errors Include random adm errors 1 = yes,0= no 0 Testing Output run information to screen l=yes, 0=no 0 Information on Screen First ut day (inclusive) 1 First Day Last ut day (inclusive) 30 Last Day Left Ion (-180) -50.0 West Longitude Boundary Right Ion (+180) 50.0 East Longitude Boundary Lower lat (-90) -50.0 South Latitude Boundary Upper lat (+90) 50.0 North Latitude Boundary - 120 - Table A.2. Example of parameters specific to each satellite. Parameter Value Start time 0000 End time 2400 Inclination [° ] 98. Local equator crosssing 0900 Height of satellite in kilometers. 800. Sensor a (along track) 4.0 Sensor b (cross) 3.5 Scanner step number . 63 Scan step resolution (degrees) 2. Scan step time between nadir points (seconds) 6. - 121 Table A.3. Data Collected at Different Program levels. Level 1: Scanner Level 2: Hourbox Level 3: Daily Level 4: Monthly LW B2 pixels LW Model pixels LW hours LW Days SW B2 pixels SW Model pixels SW hours SW Days Hourbox Index Hourbox Index Day Index Day Index Day Index Day Index Row Index Row Index Row Index Row Index Column Index Column Index Column Index Column Index Orbit number Longitude Longitude Longitude " Longitude Latitude Latitude Latitude Latitude Anisotropic Factor “ do Avg e0 e Avg 0 4> Avg <}> LW Radiance W/m2/ster LW Flux W/m2 LW Flux 6 W/m2 LW Flux W/m2 SW Radiance W/m2/ster SW Flux W/m2 SW Flux “ W/m2 SW Flux W/m2 Scene Albedo Albedo Albedo Cloud Cover Scene Fraction Julian date “Average of Anisotropic factors used to convert B2 SW fluxes to radiances. 6 Daily Average over day with no Diurnal model. “Daily Average over day with fitting performed using scene dependent ERBE direc tional models. - 122 - Table A.4. C programs Program Description absolute_time.c Converts input date to Julian time. astrogation.c Calculates astronomical solar variables used by all satel lite model routines. b 2sceneid.c Reads external data file for scene classification at ISCCP B2 resolution. binnecLadm.c Reads ERBE ADM data file. boundary _test.c Tests consistency of Longitude and Latitude input. center .angle.c Calculates central angle of pixel given sensor size and scan angle. class-index.c Separates cloud fraction. close_output-files. c Closes output files. colocation.c Performs spatial colocation of ISCCP B2 pixels with the modelled pixel. See section on Spatial Colocation. cross_track_scanner.c Calculates the longitude and latitude coordinates of the off-nadir pixels. daily_average.c Performs LW and SW fitting of data in time/space ma trix and calculates daily average. data.coordinates.c Calculates longitude and latitude of center point of B2 pixels. data_init_slot_utime.c Find fraction of day for data slot at GMT and store. date 2julian.c Calculates Julian day from gregorian calendar day, month, year. eq_cross_time.c Estimates equatorial crossing times and by interpolating between the times the satellite crosses the equator. file_check.c Checks a flag which says whether a new file needs to be opened or whether an old file will still be used. - 123 - Table A.4. (continued) Program Description find-slot.c Associate nadir pixel time with time of the data set and supply slot number so that appropriate data file is read. fraction2time.c Transformation of fractional day into hours,minutes and second. georef_b2.c Find x,y indices of B2 data for colocation loop. hourb ox -index, c Calculate hourbox index and day index for the time/space averaging matrix. interpolated_adm.c Interpolates ERBE-ADM binned anisotropic factors to continuous anisotropic factors. julian2date.c Calculates gregorian calendar day, month, year from Ju lian day. leorbits.c Driving routine for the sun-synchronous satellites. local_half_hour.c Fits albedo measured at one Oq to a new albedo mea sured at model pixel 0O using ERBE directional albedo models. monthly, c Main driving program of model. monthly_avg.c Reads daily files created by monthly, c and calculates monthly average. nadir _p ixeLgeometry. c Calculates the size in kilometers of the ground level pixel size. op en_accounting Jiles .c Open run status files. open_output_files.c Open data files for writing scanner data. pixel-bounds.c Finds indices for indexing loop during colocationproce dure. pixel_to_region.c Calculates the row, column and region number in which a scan pixel is located. read-elements.c Reads the element.satellite data files. read_r un_parameters . c Reads input file run_parameters.list -124 - Table A.4. (continued) Program Description read_scanner_data.c Reads scanner data. readmet.c Reads and stores ISCCP B2 data for particular day and time slot. satellite_position.c Brouwer-Lyddane model for satellite navigation. The returned values are geodetic latitude, longitude, central distance (i.e. from center of earth to satellite, in kilo meters), and the celestial position vector. satview_ang.c Calculates the satellite zenith and azimuth angles as seen from a given longitude and latitude. sceneid.c Reads external surface scene classification. sort_data_to_region.c Sorts scanner data according to region. sum_data.c Collects colocated B2 points and sums them for each model pixel. sunset.c Calculates the sunset hour angles for determining length of day. sunview_ang.c Calculates the sun zenith and sun azimuth angles as seen from a given longitude and latitude at day ’day ’ and at the times ’seconds’. swflux.correction.c Used to fit the B2 SW flux to the time of measurement of the model pixel. Uses effective cloud cover of B2 time. timespace_average.c Driving program for performing daily averages. write_scanner_data.c Writes scanner data. zero_avg_arrays.c Zeroes averaged data arrays for diurnal averaging rou tines. zero_stats.c Zeroes statistic counters and values for model scanner pixel. zero_sum_counters.c Zeroes statistic counters and values for diurnal averaging routines. - 125 - Table A.5. Header Files Program Description roseheader.h Included in all subroutines. Contains all astronomical constants, flag constants, structure (in C, similar to COMMON in Fortran) information, subroutine proto types, data file definitions. regional.h Included in regional scale subroutines. Contains con stants for grid size, subroutine prototypes for diurnal averaging. satellite_data_structure.h Structures, constants and subroutines related to ob served data base. scanner.h Scanner data format and related subroutines. sum_variables_structure.h Summing statistics for model pixel averages. Table A.6 . External Data files Data File Description Size adm_c.dat ERBEADMs ’ 2.7 KBytes b2geo0786.dat ISCCP B2 Scene ID: July 0 173 KBytes diralb_c.dat ERBE Directional Albedoes 0.5 KBytes geoc.dat ERBE Scene ID 10 KBytes swaniso.dat ERBE Anisotropic Factors 27 KBytes b2rsf9405_day_slot B2 Grid Shortwave Fluxes 107 MBytes b2olr9405_day_slot B2 Grid Longwave Fluxes 86 MBytes eflfcov-day-slot B2 Grid Effective Cover 27 MBytes "Differences between July and May are considered small for the scale and geographical range of this study - 126 Table A.7. Output Data files Data File Description nadir*, dat Nadir coordinates for each satellite for entire run eqcross*.dat Equatorial Crossing Times for entire run s8sat*.dat Scanner radiance files total*.dat Daily Average Files scenario*, dat Monthly Average Files zonal*.dat Monthly Average Files global*.dat Monthly Average Files