White Paper on the Future of Weather and Climate Forecasting

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Public-Private Engagement Publication No. 3

WMO Open Consultative Platform White Paper #1 Future of weather and climate forecasting

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Public-Private Engagement Publication No. 3

WMO Open Consultative Platform White Paper #1 Future of weather and climate forecasting

WMO-No. 1263

i CONTENTS

FOREWORD...... V

ACKNOWLEDGEMENTS...... 1

1. INTRODUCTION...... 3

1.1 The need for a vision for climate forecasting and weather prediction...... 3 1.2 Objective and scope of this White Paper...... 4

2. WEATHER AND CLIMATE FORECASTING: SETTING THE SCENE ...... 6

2.1 Brief history...... 6 2.2 WMO coordination role...... 7 2.3 Baseline 2020...... 11

3. CHALLENGES AND OPPORTUNITIES IN THE COMING DECADE...... 12

3.1 Infrastructure for forecasting...... 12 3.1.1 Observational ecosystem...... 13 3.1.2 High-performance computing ecosystem...... 14 3.1.3 Changing landscape: advances in infrastructure through public–private engagement...... 15

3.2 Science and technology driving advancement of numerical prediction ...... 16 3.2.1 Evolution of numerical Earth-system and weather-to-climate prediction. . . . .17 3.2.2 High-resolution global ensembles...... 19 3.2.3 Quality and diversity of models...... 19 3.2.4 Innovation through artificial intelligence and machine learning ...... 19 3.2.5 Advancing together: leveraging through public–private engagement...... 20

ii 3.3 Operational forecasting: from global to local and urban prediction ...... 21 3.3.1 Computational challenges and cloud technology ...... 22 3.3.2 Verification and quality assurance...... 23 3.3.3 Further automation of post-processing systems and the evolving role of human forecasters...... 24 3.3.4 Leveraging through public–private engagement...... 25

3.4 Acquiring value through weather and climate services...... 25 3.4.1 User perspective...... 26 3.4.2 Forecasts for decision support...... 26 3.4.3 Bridging between high-impact weather and climate services ...... 26 3.4.4 Education and training for future operational meteorologists/forecasters. . . . 27

4. CONCLUSIONS...... 28

4.1 Towards improved systems for forecasting: global, regional and local approaches. . . 28 4.2 Progressing together with developing countries...... 30

REFERENCES...... 32

BIBLIOGRAPHY...... 33

iii © iStock

Amazing supercell in Colorado

iv FOREWORD

The advancement of our ability to predict This White Paper on the Future of Weather and Climate the weather and climate has been the core Forecasting is a collective endeavour of more than aspiration of a global community of scientists 30 lead scientists and experts to analyse trends, and practitioners, in the almost 150 years challenges and opportunities in a very dynamic of international cooperation in meteorology environment. The main purpose of the paper is to and related Earth system sciences. set directions and recommendations for scheduled progress, avoiding potential disruptions and leveraging The demand for weather and climate forecast opportunities through public–private engagement over information in support of critical decision-making the coming decade. This is done through description has grown rapidly during the last decade, and of three overarching components of the innovation will grow even faster in the coming years. Great cycle: infrastructure, research and development, and advances have been made in the utilization of operation. The paper presents the converging views of predictions in many areas of human activities. the contributors, but also accounts for some variations Nevertheless, further improvements in accuracy of those views in areas where different options exist for and precision, higher spatial and temporal advancing our capacity to predict weather and climate. resolution, and better description of uncertainty Thus, it informs and provides for intelligent choices are needed for realizing the full potential of based on local circumstances and resources. forecasts as enablers of a new level of weather- and climate-informed decision-making. I am pleased to present the White Paper on the Future of Weather and Climate Forecasting to the global audience and to encourage the use of its findings and In June 2019, WMO launched the Open Consultative recommendations by decision makers, practitioners and Platform (OCP), Partnership and Innovation for the scientists from all sectors of the weather and climate Next Generation of Weather and Climate Intelligence, enterprise. I would like to acknowledge, with much in recognition that the progress in weather and climate appreciation, the work done by Dr Gilbert Brunet, Chair services to the society will require a community-wide of the WMO Scientific Advisory Panel, as the lead author approach with participation of the stakeholders from and coordinator of the group of more than 30 prominent the public and private sectors, as well as academia and scientists and experts worldwide who contributed to the civil society. The OCP is expected to serve as a vehicle for paper. I would like also to express my sincere thanks to sustainable and constructive dialogue among sectors, all the contributing authors and reviewers for devoting to help articulate a common vision for the future of the their time and sharing their knowledge and foresight for weather and climate enterprise in the coming decade the benefit of the whole enterprise. and beyond.

Undoubtedly, the 2020s will bring significant changes to the weather, climate and water community: on the one hand through rapid advancement of science and technology, and on the other hand through a swiftly Prof. Petteri Taalas changing landscape of stakeholders with evolving Secretary-General capabilities and roles. Such changes will affect the way weather and climate forecasts are produced and used. This is the reason the OCP selected the theme of “Forecasting and forecasters” as one of the “grand challenges” of the coming decade, which will require collective analytics to identify opportunities and risks and provide advice to planners and decision makers of relevant stakeholder organizations.

v ACKNOWLEDGEMENTS

This paper has been prepared by a drafting team led by Gilbert Brunet, Chief Scientist and Group Executive Science and Innovation, Bureau of Meteorology, Chair of the Science Advisory Panel, World Meteorological Organization.

The team of contributing authors includes (in alphabetical order):

Peter Bauer Deputy Director, Research Department, European Centre for Medium-Range Weather Forecasts

Natacha Bernier Director, Meteorological Research Division, Environment and Climate Change Canada

Veronique Bouchet Acting Director General, Canadian Centre for Meteorological and Environmental Prediction, Meteorological Service of Canada, Environment and Climate Change Canada

Andy Brown Director of Research, European Centre for Medium-Range Weather Forecasts

Antonio Busalacchi President, University Corporation for Atmospheric Research, USA

Georgina Campbell Executive Director, ClimaCell.org; CSO and Co-Founder, ClimaCell & Rei Goffer

Paul Davies Principal Fellow of Meteorology and Chief Meteorologist, Met Office, UK

Beth Ebert Senior Professional Research Scientist, Weather and Environmental Prediction, Bureau of Meteorology, Australia

Karl Gutbrod CEO, Meteoblue, Switzerland

Songyou Hong Fellow, Korean Academy of Science and Technology, Republic of Korea

PK Kenabatho Associate Professor, Department of Environmental Science, University of Botswana, Botswana

Hans-Joachim Koppert Director, Business Area “Weather Forecasting Services”, Deutscher Wetterdienst, Germany

David Lesolle Lecturer (Climatologist), Department of Environmental Science, University of Botswana, Botswana

Amanda Lynch Lindemann Professor, Institute for Environment and Society, Department of Earth, Environmental and Planetary Sciences, Brown University, USA

Jean-François Mahfouf Ingénieur Général des Ponts, Eaux et des Forêts, Météo-France, Toulouse, France

Laban Ogallo* Professor, University of Nairobi, Kenya

* The contributors to this White Paper express their great sadness of the demise of Prof. Laban A. Ogallo who passed away in November 2020. Prof. Ogallo was one of the pioneers of climate science in Africa and he provided a significant input to the White Paper.

1 Tim Palmer Royal Society Research Professor of Climate Physics, Professorial Fellow, Jesus College Oxford, UK

David Parsons President’s Associates Presidential Professor, Director Emeritus, School of Meteorology, University of Oklahoma, USA

Kevin Petty Director, Science and Forecast Operations and Public-Private Partnerships, The Weather Company, an IBM Business

Dennis Schulze Managing Director, MeteoIQ, Chairman of PRIMET, Chairman of Verband Deutscher Wetterdienstleister e.V. (VDW)

Ted Shepherd Grantham Professor of Climate Science, University of Reading, UK

Thomas Stocker Professor, Head of Division Climate and Environmental Physics, Physics Institute, University of Bern, Switzerland; President of the Oeschger Centre for Climate Change Research, Switzerland

Alan Thorpe Visiting Professor, University of Reading, UK

Rucong Yu Deputy Administrator, China Meteorological Administration

The group of reviewers who provided valuable comments and proposals for improving the narrative of the paper included:

V Balaji Head, Modeling Systems Group, Princeton University, USA

Brian Day Vice-President, Campbell Scientific, Canada

Andrew Eccleston General Secretary, PRIMET

Roger Pulwarty Physical Scientist at National Oceanic and Atmospheric Administration, USA

Julia Slingo Retired, former Chief Scientist of the UK Met Office (2009-2016)

The work of the drafting team was supported by Dimitar Ivanov and Boram Lee from the Secretariat of the World Meteorological Organization.

2 1. INTRODUCTION

forecasts provide major support for life-saving decisions 1.1 The need for a vision for weather through mitigation of the risk of weather and climate and climate forecasting hazards. In addition, improved forecasts create tangible socioeconomic benefits in many economic sectors (for Weather and climate forecasting is a leading example, energy, transport and agriculture), through environmental and socioeconomic challenge avoided losses, better management of resources and – whether on an urban or planetary scale, or enhanced opportunities for revenue. covering a few hours or a few seasons. Significant progress has been achieved in numerical Earth- Policy debates around the future of the planet and system1 and weather-to-climate prediction society are intense in a world with significant global (NEWP) over the past six decades, through technological transformations and environmental risks. collaborative efforts by many institutions from Such debates shape high demands for better weather the public, private and academic sectors at and climate information and for services addressing national and international levels. As the new the risks and socioeconomic impacts of the weather, decade 2021–2030 begins, vigorous NEWP and climate and water hazards. The importance of climate high-performance computing (HPC) programmes risk-based decision-making is increasing substantially of multidisciplinary research and development with population growth. This is particularly so in major (R&D) worldwide are making innovative cities, often on coasts, where more people and assets are contributions to this ongoing challenge. exposed and vulnerable to weather, climate, water, ocean and even space hazards. Essential services (for example, power, water, transport, telecommunications, the Earth-system models are developing in complexity, Internet and finance) are also exposed to these hazards. incorporating additional processes and needing more Meeting the demands for highly localized and accurate observations of diverse elements of the environment. information with frequent updates, as well as tailored Thus, observational and HPC infrastructures are central services for informed decision-making over multiple to future advancement of NEWP systems. Numerical timescales, will require a new level of collaboration modelling and prediction were among the main within the weather and climate enterprise2. Working with motivations behind the first computer applications 70 user communities in the co-design of fit-for-purpose years ago, and they are still a major use case for HPC information and services will also be important. today. Likewise, advances in satellite-based observations and telecommunications utilized in NEWP are at the Traditional risk assessment and management strategies are forefront of technological innovations. Computational increasingly challenged by systemic risks that connect local power and high-quality observations drive improvements conditions to broader global systems. These systemic risks in weather and climate models such as refined space– are unconstrained and include the potential for thresholds time resolution, better representation of the physical and surprises, along with the need to account for the processes and enhanced data-assimilation techniques. evolution of weather and climate high-impact events, They also help to quantify forecasting and modelling variability, and change across time and space. Addressing uncertainties, although trade-offs are often required such complex risks requires analytical, technical and among these. The achievements and improvements are deliberative capacity, as well as consideration of equity and remarkable; for instance, the mid-latitude 5-day weather broader participation to consider implications beyond a forecast today is as accurate as the 1-day forecast 40 single project or decision context. Thus, when considering years ago. More accurate and reliable forecasts are the future of weather and climate forecasting, the need produced by advances in science and technology. These for an international multidisciplinary research agenda,

1 The Earth system encompasses the atmosphere and its chemical composition, the oceans, land/sea ice and other cryosphere components as well as the land surface, including surface hydrology and wetlands, lakes and human activities. On short timescales, it includes phenomena that result from the interaction between one or more components, such as ocean waves and storm surges. On longer timescales for climate applications, it includes terrestrial and ocean ecosystems, encompassing the carbon and nitrogen cycles and slowly varying cryosphere components such as large continental ice sheets and permafrost. 2 The term “weather and climate enterprise” is used to describe the multitude of systems and entities participating in the production and provision of meteorological, climatological, hydrological, marine and related environmental information and services. The enterprise includes public-sector entities (NMHSs and other governmental agencies), private-sector entities (equipment manufacturers, service-provider companies, private media companies, and so forth), academic institutions, and civil society entities (community-based entities, NGOs, national meteorological societies, scientific associations, etc.). The weather and climate enterprise has global, regional, national and local dimensions.

3 covering both applications and services, and providing 1.2 Objective and scope of this for a systematic link between NEWP science and policy/ decision-making, should be recognized. White Paper

Over the coming decade, these developments will drive The main objective of this paper is to provide a basis many innovations to satisfy diverse socioeconomic for informed decision-making by weather and climate needs: enterprise stakeholders in planning their activities and investments in NEWP and operational forecasting during • Higher-resolution and more localized and relevant the coming decade. This decade, often referred to as the NEWP forecasts, updated frequently (hourly or even “decade of digital transformation”, will bring profound sub-hourly) for cities and other areas of interest. These impacts on organizations of all types. The weather and will be combined with nowcasting tools optimized to climate enterprise will also undergo significant changes provide users with enhanced decision support based since it is highly driven by data and information technology on more timely forecast updates (on a minutes scale) (IT). The High-level Round Table on the launch of the Open before and during high-impact weather. Consultative Platform (OCP) Partnership and Innovation for the Next Generation of Weather and Climate Intelligence • Enhanced quality of observational data for analyses (5–6 June 2019, Geneva) highlighted this expectation, and and for assimilation into NEWP systems, as well as included “Forecasting and … forecasters” among the five increased number of Earth-system observations of themes on key challenges for the next decade (WMO, 2019a). all types done in an economic and sustainable way. This reflects the recognition that the innovation cycle (see Figure 1) for weather and climate forecasts includes • Transition to a full Earth-system numerical prediction various stakeholders from public, private and academic capability with coupled subcomponents, to deliver sectors. The important drivers of the innovation cycle are a wider breadth of information-rich data that are computational and observational infrastructures (in the consistent across the atmosphere, land and ocean, middle of the figure), and increasing stakeholder and including waves, sea ice and hydrological elements. customer demand (on the circumference of the figure) for Aligned with the Earth-system framework and approach, tailored and seamless weather and climate forecasting these NEWP systems will enable prediction of multi- (localized, timely, precise and accurate). Figure 1 shows hazard events in a fully consistent manner, providing that stakeholders and customers can push clockwise new more precise, accurate and reliable information. initiatives at different positions in the innovation cycle: R&D, operation and services. The structure of this paper is • Seamless weather and climate risk-based services will aligned along three components of the innovation cycle: be further developed, providing insights from minutes infrastructure, R&D and operation. Stakeholders engaged to seasons, to enable improved decision-making in all three components will have to make strategic choices and risk reduction. This will include the integration in the coming years, and some will struggle to keep up as of historical observations and forecasts with a full technologies continue to combine and advance, and new characterization of uncertainty. ways of doing business appear quickly. © iStock

Macedonia

4 INFRASTRUCTURE

Figure 1. The innovation cycle: the public–private engagement challenge

This paper aims to help decision makers, researchers Thus, the paper also partly treats elements at the input and even users in the rapidly changing landscape of the side (observational data), as well as at the output side weather and climate enterprise, by compiling views, (generation of products for services) of this chain. knowledge and expertise of a group of prominent scientists Science and research that form the basis for forecasting and practitioners from the public, private and academic and determine its foreseen advances are also discussed. sectors. It does not attempt to provide unique solutions Technology is another key factor in the discussion of on the many open questions of the future of weather the future with many exciting developments in IT and and climate forecasting. Instead, it serves to improve computing that bring enormous opportunities for the understanding of ongoing R&D, and to identify improved quality and efficiency. technological trends and sometimes possible impediments to progress such as the lack of data sharing. In this way, The many contributors to this paper were all people risks and opportunities for each player can be better dealing with Earth-system weather and climate numerical assessed, and decisions made on future organizational prediction. However, for the purposes of this paper, they plans and investment can be better informed. were asked to try to forecast the future of their enterprise. Engaging 27 such contributors may be seen as applying The scope of this paper is purposefully restricted to the the ensemble prediction method, which highlights process of NEWP innovation and production of weather uncertainties and potential different trajectories of and climate forecasts, and also to climate insight when development. Therefore, the individual views and inputs there is a close relationship with NEWP and climate of each contributor are available at the following weblink: change science issues. The production value chain in https://library.wmo.int/doc_num.php?explnum_id=10552. the operation (see Figure 1) is increasingly developing The bibliography at the end of this white paper also towards seamless interfaces among its elements. provides an extensive list of further reading.

5 2. WEATHER AND CLIMATE FORECASTING: SETTING THE SCENE

2.1 Brief history

Operational weather forecasting and climate predictions Without going into the details of the pre-NEWP decades started long before numerical modelling using of weather forecast development, it is worth mentioning computers became possible. There have always been that the knowledge and methods improved slowly. attempts to understand weather and climate patterns However, the number of incorrect forecasts (visible and eventually foresee their future states, due to the to the public, due to the popularity of the subject) led impact on humans and their activities. In the absence to a prevailing scepticism about the ability of science of theories and knowledge of the forces driving weather to deal with the challenge and to make operational behaviour, such attempts have been part of astrology forecasting possible with reliable day-to-day outcomes. or folklore for centuries. There were several important This may have been the reason for Margules to state, theoretical advances in the early nineteenth century, in the early twentieth century, that weather forecasting including a growing understanding of the nature of was “immoral and damaging to the character of a storms. The efforts for organized systematic collection meteorologist” (Lynch, P., 2006). of observational data and using these data for predicting weather events started later in that century. A common However, developments at the beginning of the twentieth reference point for the start of “weather forecasting” is century quickly changed the pessimism of Margules the work of Admiral FitzRoy during the 1850s and 1860s. into a much more optimistic scenario for the future of FitzRoy started issuing storm warnings for sailors in 1860, weather forecasting. Since the ground-breaking work of and, one year later, general weather forecasts (the first Abbe (1901), Bjerknes (1904) and Richardson (1922), the such forecast appeared in The Times on 1 August 1861). challenge of NEWP has been related to an initial value FitzRoy’s work was enabled by the rapidly expanding conditions problem of mathematical physics (based use of electrical telegraphs, which allowed collection of on the non-linear equations governing fluid flow), and observations from several stations, and some primitive has been approached using numerical techniques and situational analysis. It seems he also introduced the use algorithms. of the terms “forecast” and “forecasting” in place of “prognostication”, which had been used previously (BBC The success of the first numerical prediction by Charney News, 2015). et al. (1950) launched a spectacular trend of innovations in NEWP over the following seven decades. Routine, These first attempts at weather forecasting were, real-time forecasting with NEWP started in the mid- understandably from today’s perspective, rather 1950s and was introduced in operations in the 1960s. unsuccessful. Nevertheless, interest in developing Improved observational coverage, the advent of satellite knowledge and methods for meteorological analysis observations, the steady growth of computer power and and prediction grew rapidly during the last decades breakthroughs in the theory of Earth-system coupled of the nineteenth century and the early decades of the processes all underpinned a successful story of weather twentieth century. Collecting and exchanging (through forecasting in the NEWP era. telegraphs) data across national borders established one of the early cases of globalized infrastructure and The high cost of NEWP, including the capital investment an unprecedented international cooperation between for computers and their running and maintenance costs, scientists and practitioners. The “weather knows no as well as resources needed in R&D, meant that the most borders” slogan called for a partnership that needed developed nations had the highest concentration of major governance – to initiate a global standardization of developments. Nonetheless, exemplary cooperation and methods and procedures for research and operations in knowledge-sharing with scientists from many countries each individual country.The formal start of such organized and institutes has nurtured advancement of NEWP. international cooperation was the first International European countries undertook a strong collaborative Meteorological Congress in Vienna in August 1873. This move with the establishment of the European Centre for event established a format of collaboration that WMO Medium-Range Weather Forecasts (ECMWF) in 1975 as continues today. an intergovernmental organization.

6 Progress in NEWP is often illustrated by the improvement The same study also provided an outlook for Era 5, in the horizontal and vertical resolution of operational encompassing the next 30 years until 2050, which could models. There has been an almost 40 times increase in well be named the era of “next generation of weather the horizontal resolution of global models (from about and climate Earth-system intelligence”. 400 km in the early 1960s, to less than 10 km in 2020); in addition, regional fine-mesh models have reached a 1-km resolution. In the vertical direction, from the 2.2 WMO coordination role early one- and three-layered quasi-geostrophic models, today’s models utilize more than 130 levels, reaching an It is important to highlight the role of WMO in the altitude of about 80 km (pressure of 0.01 hPa). progress of and insight into weather and climate forecasting. The WMO technical commissions (for There are several excellent papers on the history of the example, the Commission for Atmospheric Sciences, highlights of NEWP developments (Pudykiewicz and Brunet, the Commission for Climatology, the Commission for 2008; Benjamin et al., 2019; see also Box 2). For example, Basic Systems, and the Joint Technical Commission Benjamin et al. (2019) reviewed the progress in forecasting for Oceanography and Marine Meteorology) were and NEWP applications over the 100-year period from 1919 instrumental in facilitating international collaboration to 2019, and divided the period into four ”eras” as follows: and knowledge-sharing. The World Weather Research Programme and the World Climate Research Programme • Era 1 (1919–39: maps only; observations and were at the forefront of scientific efforts underpinning extrapolation/advection techniques) progress in NEWP development and in research-to- operation transition. • Era 2 (1939–56: increasing science understanding; application especially to aviation; birth of computers) Establishment of the WWW programme was one of the main WMO contributions. This was initiated on • Era 3 (1956–85: advent of NEWP and dawn of 20 December 1961 with the adoption of Resolution remote-sensing) 1721 (XVI) by the United Nations General Assembly (United Nations, 1961), which called upon WMO to • Era 4 (1985–2018: weather forecasting, and especially undertake a comprehensive study of measures: NEWP, matured and penetrated virtually all areas of human activity)

Box 1. Major milestones in weather and climate forecasting

• 1861: Met Office weather forecast services using • 1960 onward: Satellite-based meteorological telegraphs established by FitzRoy observations and telecommunications at the forefront of technological innovations since the launch of the • 1873: Working towards global meteorological first weather satellite TIROS-1 observatories and international data sharing with the foundation of the International Meteorological • 1960s onward: Emergence of general circulation Organization in Vienna models for climate research and forecasting

• 1900–1922: Birth of numerical weather prediction • 1962: Establishment of the World Weather Watch (NWP) with the work of Abbe (1901), Bjerknes (1904) (WWW) programme with its three main components and Richardson (1922) (Global Observing System, Global Telecommunication System and Global Data-Processing System) • Early 1920s: Onset of statistical climate prediction and global atmospheric teleconnection insights pioneered • 1963: Lorenz’s seminal work on chaos initiated by Walker atmospheric predictability theory and paved the way to numerical ensemble prediction in the 1980 and 1990s • 1950: First computer NWP forecast on ENIAC (Electronic Numerical Integrator and Computer) by • 1969: Launch of the Global Atmospheric Research Charney et al. (1950) Program (GARP) led by Charney

7 “(a) To advance the state of atmospheric composed of three main components: the Global science and technology so as to provide greater Observing System, the Global Telecommunication knowledge of basic physical forces affecting System and the Global Data-Processing and Forecasting climate and the possibility of large-scale weather System (GDPFS), coupled with the Meteorological modification; Applications Programme. Thus, the output of the WWW system was a global set of observational and forecast (b) To develop existing weather forecasting data that were shared among WMO Member States capabilities and to help Member States make and Territories, and served as input for development effective use of such capabilities through regional of the whole spectrum of user-oriented applications meteorological centres” and services.

It is interesting to note the emphasis of “large-scale Today, GDPFS is an elaborate system of global and weather modification”, which was hoped would mitigate regional centres, including nine World Meteorological the unfavourable weather impacts on human activities. Centres (WMCs) and 11 Regional Specialized This hope proved over-optimistic, as became clear in the Meteorological Centres (RSMCs), with geographical following decades, and weather modification research specialization (see Figures 2 and 3). Various centres and operational activities have not developed much. are tasked with production of: global deterministic However, those early intentions for human control on and ensemble NWP; limited-area deterministic and weather and climate may be revived to a certain extent ensemble NWP; nowcasting; various specialized due to recent geoengineering ideas to mitigate climate forecasting activities, like tropical cyclone forecasting; change. However, the gains of geoengineering relative atmospheric transport and dispersion modelling to reduced greenhouse gas emissions and against the (nuclear and non-nuclear); atmospheric sandstorm hazards it could bring to the environment must be and duststorm forecasting; numerical ocean wave balanced rigorously. prediction; aviation forecasting; and so forth. In addition, 13 centres are designated as Global Producing Centres Paragraph (b) above of Resolution 1721 is significant for Long-range Prediction (monthly to seasonal), and for the scope of this White Paper. In cooperation with four centres as Global Producing Centres for Annual to partners, WMO established the WWW programme Decadal Climate Prediction.

• 1969 onward: Global NWP innovations since the first • 1997: Ground-breaking numerical prediction advances global NWP simulation by Robert in the use of multiple sources of Earth-system observations with the introduction at ECMWF of four- • 1975: Federation of global NWP R&D effort in Europe dimensional data assimilation with the foundation of the European Centre for Medium-range Weather Forecasts (ECMWF) • 2002: Earth Simulator, Japan – a landmark supercomputer investment for climate, weather and • 1979: First GARP Global Experiment, to gather geophysical research the most detailed observations ever of the global atmosphere • 2007: A great step forward for weather and climate Earth-system forecasting with 3 000 Argo oceanic • 1980s onward: Development of coupled ocean– floats in global operation atmosphere climate models • 2015 onward: Dealing with prediction uncertainty in • 1992: Operational implementation of ensemble data assimilation with ensemble–variational data- prediction systems at the ECMWF and the National assimilation techniques Centers for Environmental Prediction (NCEP)

8 Montreal Tromso Oenbach St Petersburg Anchorage Ottawa ECMWF Moscow Novosibirsk Exeter Obninsk Khabarovsk Edmonton Toulouse Vienna Vladivostok Rome Tashkent Tokyio WashingtonMontreal Casablanca Tromso Oenbach St Petersburg WinnipegAnchorage Ottawa ECMWFTunis Athens Moscow Novosibirsk Honolulu Barcelona Exeter Cairo Khabarovsk Miami Obninsk Edmonton Toulouse ViennaJeddah Karachi Vladivostok Dakar Rome Tashkent TokyioBeijing Washington Casablanca New Delhi Winnipeg Tunis Athens Barcelona Cairo Hong KongHonolulu Miami Algier Nairobi Karachi JeddahDar es Salaam Dakar Callao New Delhi Darwin Beijing Brasilia Nadi Vacoas Hong Kong Niteroi Algier Nairobi La Reunion Valparaiso Dar es Salaam Callao Darwin Buenos AiresBrasilia Pretoria Vacoas Nadi Niteroi Melbourne La Reunion Valparaiso Wellingtone Buenos Aires Legend Pretoria Melbourne Wellingtone World Meteorological Centres (WMCs)* (9) RSMCs Nuclear Emergency Response** (10) Legend RSMCs Geographic Specialization (12) RSMCs Non-Nuclear Emergency Response** (3) RSMCs (NRT***)World Meteorological Lead Centre Centresfor Coordination (WMCs)* (9) of Wave Forecast (1) RSMCs Nuclear Sand and Emergency Duststorm Response** Forecasts (10) (2) RSMCs Geographic Specialization (12) RSMCs (NRT***) Lead Centre for Coordination of EPS Veriﬁcation (1) RSMCs Non-Nuclear Nowcasting Emergency (3) Response** (3) RSMCs (NRT***) Lead Centre for Coordination of Wave Forecast (1) RSMCs Sand and Duststorm Forecasts (2) RSMCs (NRT***) Lead Centre for Coordination of DNV (1) RSMCs Limited Area Ensemble NWP (2) RSMCs (NRT***) Lead Centre for Coordination of EPS Veriﬁcation (1) RSMCs Nowcasting (3) RSMCs Numerical Ocean Wave Prediction (4) RSMCs Global Ensemble NWP (7) RSMCs (NRT***) Lead Centre for Coordination of DNV (1) RSMCs Limited Area Ensemble NWP (2) RSMCs Tropical Cyclone Forecasting (6) RSMCs Limited Area Deterministic NWP (6) RSMCs Numerical Ocean Wave Prediction (4) RSMCs Global Ensemble NWP (7) RSMCs SevereRSMCs Weather Tropical ForecastingCyclone Forecasting (5) (6) RSMCs Limited Global Area Deterministic Deterministic NWP NWP (8) (6) RSMCs SevereRSMCs Weather Severe WeatherForecasting Forecasting (24) (5) RSMCsICAO designated Global Deterministic Volcanic NWPAsh Advisory(8) Centres (9) RSMCs Severe Weather Forecasting (24) ICAO designated Volcanic Ash Advisory Centres (9) * World Global Centres are also Global Producing Centres for a) Deterministic Numerical Weather Prediction, b) Ensemble Numerical Weather Prediction, *and World c) Long-RangeGlobal Centres Forecasts. are also Global Producing Centres for a) Deterministic Numerical Weather Prediction, b) Ensemble Numerical Weather ** RSMC forPrediction, nuclear and c) non-nuclear Long-Range Forecasts.emergency response have Atmospheric Transport and Dispersion Modelling (ATDM) capabilities. *** NRT stands** RSMC for Non-Real-Timefor nuclear and non-nuclear emergency response have Atmospheric Transport and Dispersion Modelling (ATDM) capabilities. *** NRT stands for Non-Real-Time DESIGNATIONS USED The depictionDESIGNATIONS and use of boundaries,USED geographic names and related data shown on maps and included in lists, tables, documents, and database The depiction and use of boundaries, geographic names and related data shown on maps and included in lists, tables, documents, and database on this websiteon this are website not warranted are not warranted to be error to be free error nor free do nor they do they necessarily necessarily impy impy ocial ocial endorsement endorsement or or acceptance acceptance by bythe theWMO. WMO.

Figure 2. WMO-designated GDPFS centres (nowcasting and weather forecasting, up to 30 days)

Source: WMO (2019)

9 De Bilt Oenbach Montreal ECMWF Moscow Exeter Seoul Algier Toulouse De Bilt Tunis Oenbach Tokyio Montreal Casablanca Washington ECMWF Moscow BarcelonaExeter Tripoli Cairo Seoul Algier Toulouse Tunis Bridgetown Pune Tokyio Casablanca Beijing Washington Barcelona Tripoli Cairo Nairobi Niamey Guayaquil Bridgetown Pune Beijing Brasilia Nairobi Niamey Guayaquil CPTEC

Brasilia Buenos Aires Pretoria CPTEC Melbourne

Buenos Aires Legend Pretoria Melbourne World Meteorological Centres (WMCs)* (9) RCC - Networks Regional Climate Prediction and Monitoring NODEs (11) Legend RSMCs (NRT***) Lead Centre for Coordination of ADCP*** (1) RCC Regional Climate Prediction and Monitoring (9) RSMCsWorld (NRT***) Meteorological Lead Centre Centres for (WMCs)* Coordination (9) of LRFMME**** (2) RCC -GPC Networks for ADCP*** Regional Climate (4) Prediction and Monitoring NODEs (11) RSMCs (NRT***) Lead Centre for Coordination of ADCP*** (1) RCC Regional Climate Prediction and Monitoring (9) RSMCs (NRT***) Lead Centre for Coordination of LRF veriﬁcation (2) GPC for Long-Range Forecasting (13) RSMCs (NRT***) Lead Centre for Coordination of LRFMME**** (2) GPC for ADCP*** (4) RSMCs (NRT***) Lead Centre for Coordination of LRF veriﬁcation (2) GPC for Long-Range Forecasting (13) * World Global Centres are also Global Producing Centres for a) Deterministic Numerical Weather Prediction, b) Ensemble Numerical Weather Prediction, and c) Long-Range Forecasts. ** NRT* World stands Global for Non-Real-Time. Centres are also Global Producing Centres for a) Deterministic Numerical Weather Prediction, b) Ensemble Numerical Weather *** ADCPPrediction, stands and for c) Annual Long-Range to Decadal Forecasts. Climate Prediction **** LRFMME** NRT stands stands for Non-Real-Time.for Long-Range Forecast Multi-Model Ensemble *** ADCP stands for Annual to Decadal Climate Prediction DESIGNATIONS**** LRFMME USED stands for Long-Range Forecast Multi-Model Ensemble The depictionDESIGNATIONS and use USED of boundaries, geographic names and related data shown on maps and included in lists, tables, documents, and database on thisThe website depiction are and not use warranted of boundaries, to be geographic error free namesnor do and they related necessarily data shown impy on o cial maps and endorsement included in lists, or acceptance tables, documents, by the andWMO. database on this website are not warranted to be error free nor do they necessarily impy o cial endorsement or acceptance by the WMO.

Figure 3. WMO-designated GDPFS centres (long range and climate forecasting, over 30 days)

Source: WMO (2019)

10 2.3 Baseline 2020

To provide a vision for developments in weather forecasting and climate predictions over the next 10 years (Vision 2030) and beyond, it is important to set up a baseline: the present situation in year 2020. The main elements of the ”current state” – baseline 2020 – are as follows:

• High-resolution global deterministic models for the medium range operate at horizontal resolutions of ~10 km, with 50–140 vertical layers and ~10 prognostic variables. These models are usually run for 10–15 days with an update cycle of 6 h (four times a day).

• Ensemble prediction systems for the medium range use ~50 ensemble members and the horizontal resolution is ~20 km. For an extended range of up to 45 days, the horizontal resolution is ~35–40 km.

• As these systems are extended beyond the medium range towards the seasonal range, the horizontal resolution is usually downgraded to 40–100 km, while vertical levels and ensemble size are kept constant. Major updates in these systems occur less frequently, typically every 5 years or so, with a rate of improvement closer to a week of extra lead time per decade of development for the Madden–Julian oscillation (Kim et al., 2018).

11 3. CHALLENGES AND OPPORTUNITIES IN THE COMING DECADE

Operational weather forecasting based on 3.1 Infrastructure for forecasting numerical prediction systems has continuously improved over the past few decades. The Two main infrastructural elements define the performance usefulness of NEWP forecasts has been pushed of NEWP systems: the observational ecosystem that to lead times beyond 10 days for some high- provides the input data and the IT ecosystem including impact weather phenomena such as mid-latitude communication, computers and storage, with all internal snowstorms in North America. However, the and external interfaces. steady progress has been at a slower pace for some forecasted elements, like quantitative The steady improvements in the skill of NEWP are precipitation, where more efforts are needed. based, in large part, on the performance of the global observing system of systems, which has advanced significantly in the past few decades. Recent examples By 2050, it is envisaged that NEWP will approach the of such improvements include the development of space- theoretical limit of mid-latitude predictability of the based measurements for wind and clouds/precipitation chaotic atmosphere – a century after the first numerical using lidar and radar technologies, respectively. Remote- weather forecasts were produced by Charney and sensing technologies such as infrared and microwave his team. Several factors have steered progress, sounders/imagers in all-sky conditions, combined with including: advances in NWP underpinned by increasing advanced ground-based observational networks as the HPC capacity; improved observational instrumentation bed-rock, have provided accurate initial conditions that providing more accurate data with higher temporal and are a key factor for improved synoptic-scale forecast skill. spatial resolutions; better representation of complex physical processes; better model initialization through In addition to atmospheric measurements, the evolving the utilization of expanding satellite observations and capabilities of other Earth-system observations has more effective data-assimilation methods; and use of made progression possible towards integrated Earth- ensembles to represent uncertainty in the initial state system modelling and forecasting. For example, and model processes. Furthermore, scientific insight operational oceanography has increased the availability across fields ranging from meteorology to computer of observations necessary to improve ocean state science has provided a growing suite of tools, catalysing estimation, including its mesoscale variability. This has innovations in numerical prediction system design. On brought rapid improvement in the accuracy of oceanic the policy side, prevailing free and open data sharing forecasts. Starting in the 1990s, oceanic measurements, among countries and institutions has provided access like Argo floats and Tropical Ocean Global Atmosphere to observational data for operational and research arrays, permitted operationalization of forecasts of purposes, which has facilitated progress. However, storm surges, waves and sea ice for use by operational in some areas, policies implying commercial or other centres. Progress has also been made in land-surface conditions in accessing important data sets have slowed hydrology, but much more is needed to advance the progress. terrestrial hydrology observations and integrate these observations into NEWP systems at all timescales. The increased availability and adoption of forecast- driven tools for weather- and climate-informed decision- In contrast to the advances in remote-sensing, there making, especially by the commercial sector, have has been alarming evidence that in situ, high-quality also facilitated major progress. The demand for such observation systems have decreased in number over decision-support tools by many industry sectors is the past 20 years in some regions of the world. Such growing rapidly when striving to mitigate weather and negative effects are notable in developing countries climate impacts on operations and profits. This presents due to insufficient public funding for operating and challenges and opportunities for further advancing maintaining observing networks. The in situ networks weather and climate forecasting, which is yet to reach remain foundational for monitoring climate variations its full potential. and change by serving as reference stations, even with the rapid growth of satellite and other remote observations. They are also important to climate and weather simulations as a reference for the accuracy of

12 remote-sensing observations, and for identifying forecast investing in low-cost technology, often built upon errors. Local observations such as weather radars are research advances, to build short-lifetime missions an important part of early warning systems, which (for example, constellations of nanosatellites). The need accurate short-term forecasts of convection and availability, quality, interest and methods to pay other hazards. Various capacity-development projects for these observations have yet to be evaluated. have attempted to fill these observational gaps in the Public–private arrangements will be needed for developing countries, but the success of these efforts improved coordination of the short- and long-term has been undermined by the lack of sustainability and delivery schedules of these different space-based continuity of the operations after the expiration of the observations and for identifying possible synergies, project period. especially where the private sector could fill some observational gaps. Efforts should be made to exploit On the IT side, mid-range HPC systems, which nowadays new satellite observations, and to better utilize the are more affordable and accessible, permit effective data already available. Since many of the advances operations and research. This could allow for a wider in operational prediction are built upon refining and range of forecasting centres to operate regional NEWP improving research breakthroughs, access of the systems in partnership with global forecasting providers, research community to these private sector satellite- by enabling demanding computational processes with based observations is also critical. higher space–time resolution in complex settings. A significant computational challenge continues to be • Significant challenges remain in the access to and assimilating the ever-increasing volume and variety of exploitation of data from observing systems owned observational data, particularly from satellites. and operated by various non-State stakeholders. For example, many underutilized in situ weather stations exist, often used for academic purposes, but with 3.1.1 Observational ecosystem potential to contribute to operational forecasting. Many municipalities, farms, road agencies and other Availability of observational data is key to reaching industries maintain regular observations with their the desired model performance, even with the best own networks of instruments. Such observations NEWP model. Thus, discussion about the refinement/ may be of substandard quality compared with those development of future NEWP models should go together operated by National Meteorological and Hydrological with that of future observing capabilities. Several factors Services (NMHSs), but through sharing arrangements of the observational ecosystem need to be considered: and innovative quality control, they could add significantly to the overall observing ecosystem, • Overcoming the lack of observational data and data especially in remote areas, where operation and quality issues is critical for continuous improvement. maintenance of ground stations poses challenges. For example, poor instrumentation, particularly in developing countries, limits the ground-truthing • The growing availability of “non-conventional” and application of NEWP systems especially at observations will offer major new opportunities catchment/basin/watershed levels, where most water for augmenting the classical approaches and filling management decisions are usually made. existing observational data gaps. There is a plethora of such new data, many available as by-products of • Monitoring the Earth’s surface at high temporal systems or devices not intended for meteorological frequency and high spatial resolution will improve or similar purposes. These include: estimating rainfall the description of kilometre and sub-kilometre scales from attenuation of signals between cell phone associated with convective systems, boundary layer towers, commercial surface sensors purchased and processes and new surface types (for example, towns, deployed by citizens, virtual sensors, “Internet of lakes and rivers). Meeting this observational challenge Things” devices, smartphone sensors and military- will be demanding as numerical models move grade weather stations. The data provided by these towards convective-permitting scales. Boundary layer new systems or devices offer unprecedented sources observations and also observations in data-sparse of information, but can also present challenges in regions would advance forecasting considerably. terms of observational quality, data access and volumes, and privacy and ethical concerns when data • The evolution of satellite programmes for operational are owned by individuals or commercial companies. prediction undertaken by governmental space With these concerns addressed appropriately, and agencies is stable but takes place over timescales with proper quality control, such non-conventional of decades. The development of satellite remote- data could deliver observations in sparsely covered sensing for the research community has a more rapid domains like urban areas, tropical land surfaces, response. In parallel, the private sector has started oceans, the upper atmosphere and polar regions.

13 International collection and sharing of such weather • Projects conducted by leading global weather prediction observations is already happening with websites centres, and the climate projection community (for like the Met Office Weather Observation Website. example, the Coupled Model Intercomparison Project However, their systematic use in NEWP should be (CMIP)), already struggle to afford the sustainable cautious since the long-term availability and reliability supercomputing infrastructures required for hosting of such data provision cannot be guaranteed. R&D activities and upcoming prediction system upgrades, in terms of capital investment and running • Supplementary information based on indigenous and operational costs (for example, the cost of electrical traditional knowledge and citizen science is yet to be power). To overcome these challenges, research explored as a potential source for improved forecasts organizations are under increasing pressure to find and insights. However, these forms of information ways to join forces in operating the HPC infrastructure remain challenging across several dimensions, such and gain efficiency through resource and cost sharing. as frequency and distribution of collection, mapping between epistemological domains and quality control. • The main technological breakthroughs linked to HPC are These challenges can be addressed only through expected from the combined effects of several sources. more systematic and grounded research partnerships. In the past, an exponential computing power growth rate was provided by increasing transistor density while • Future weather and climate observational data should maintaining overall power consumption on general- be interoperable with socioeconomic, biophysical and purpose chips. Today, new power-efficient processor other data, especially at the local and urban levels, technologies (for example, graphics processing units, to expand knowledge generation and to provide tensor processing units, field programmable gate arrays informative forecasting results to end users. and custom application-specific integrated circuits) are increasingly available and necessary to sustain that • Finally, when planning observational ecosystem exponential growth. Their use requires code adaptation improvements and optimization, it should be kept in to different ways of mapping operations onto processor mind that achievements and improvements in NEWP memory, parallelization and vectorization. It might be systems have permitted the same global forecast that some of the new processors targeting artificial skills to be accomplished utilizing fewer observations, intelligence (AI) will never be effective at solving as demonstrated by reforecast experiments based on partial differential equations, and it is necessary to seek reanalyses. This allows the opportunity to consider radically new approaches, such as emulation by machine optimal and cost-effective design of future operational learning (ML). The implementation of such adaptation observing systems better tailored to the capabilities will require enough lead time to be effective and serve of the forecasting systems. Furthermore, the skill of the entire community. Furthermore, there is a need to NEWP systems often depends more on the ability to enhance the scope of expertise towards computational properly assimilate existing observations, rather than sciences in all programmes, which offers potential for on adding additional observations. Hence, rigorous attracting new talent and career development. forecast sensitivity studies are needed to understand the impact of observational data to inform and • As future architectures will be composed of a wider prioritize investments in observational and NEWP range of different technologies, mathematical methods systems at all space–time scales. As an example, and algorithms need to adapt so computations can even with the phenomenal impact of the increase in be delegated to those parts of the architecture that satellite observations for NEWP, in situ observations deliver optimal performance for each task. Such will always be needed to provide a reference, such specialization is not embodied in present-day codes as for surface pressure. However, what the optimal and not delivered by the available compilers and investments in such in situ observations are to satisfy programming standards. A breakthrough can be all user requirements is still an open question. achieved only by a radical redesign of codes, likely to be carried out by the weather and climate community in partnership with computer scientists and hardware 3.1.2 High-performance computing ecosystem providers. This redesign will ensure the theoretically achievable performance gains are scalable from small The evolution towards running higher-resolution and to large machines and are transferable to even more more complex NEWP systems on tight operational advanced and novel technologies in the future without schedules poses significant challenges for HPC and “big yet another redesign effort. data” handling. Computing and data must always be considered together since more sophisticated prediction • The resulting combination of code adaptivity and systems create more diverse and more voluminous algorithmic flexibility will require a community-wide output data. Challenges include the following: effort; again, there are concerns for computing and

14 data handling. Future HPC workflows are likely to the private sector) instead of satellite hardware. To be distributed across specialized units for the heavy ensure mutual benefits and avoid “data inequalities”, computing and data handling tasks, for operating the WMO data policy should evolve to reflect the software layers interfacing with observational input changing observational ecosystem with its economic and prediction output data, and for providing a foundation, while at the same time preserving the flexible and open interface to a large variety of users. global basic infrastructure delivery as a public good This will be a move away from single centres towards for the benefit of all nations. federated infrastructures, components of which will be operated on the cloud. • Innovative remote observing platforms and systems (such as autonomous drones, crowdsourced • Weather and climate data volumes are massive and observations or other emerging systems that can are growing daily. The old model of storing data in probe and measure atmospheric parameters in three centralized archives – ostensibly part of HPC systems, dimensions) will have a significant impact on global which likely produce the data – is not capable of scaling forecast quality at higher resolutions as well as for up to support the diverse user groups expected to extreme events. Understanding the precision and want these data over the coming decade, for two accuracy of these new information sources will be a reasons: (a) remote users likely cannot access them critical research target to help increase their usefulness. and (b) security concerns make it unwieldy to support large user bases on these systems. A solution is • The expansion of HPC ecosystems offers opportunities needed to make data open and accessible for diverse for much more comprehensive and prevalent public– stakeholders from across the globe. A competitive private partnerships. Indeed, such partnerships market of cloud-powered data archives and support are crucial if the supercomputers of the future are infrastructure is likely to be a key enabler. to be designed to be best suited to the numerical simulation software used across the weather and climate enterprise. 3.1.3 Changing landscape: advances in infrastructure through public–private • Transition to cloud solutions for archiving and engagement computing will be a major trend in the coming decade. This opens another area of collaboration and The new technological developments in the infrastructure partnership among sectors, as big data companies domain have significantly increased the range of will continue to provide cloud services. Many NMHSs solutions available to the NEWP challenges, many of and other public sector agencies will gain long-term which are increasingly being offered by private sector efficiency by using these services. However, the stakeholders: transfer of responsibility for data handling to those service providers should be based on strong and • Innovative technological solutions in observations reliable relationships with guarantees for protection and monitoring with possible application in weather of data and continuity of service over long periods. and climate forecasting will continue to be delivered in part by the private sector. As in telecommunications, • A challenge for rapid advancement of weather more private sector providers are likely to offer and climate forecasting is to find the right balance observational data services (“observations as a of investment in remote, in situ and space-based service”) rather than simply selling hardware as in observations. Today’s backbone observing system the past. Further engagement of the private sector needs to be maintained with sufficient redundancy with affordable initial investments (including more to fill potential gaps in case of individual mission “start-up” businesses) will be possible due to failure. But the global observation network already the availability of low-cost weather stations. The shows significant resilience and coverage; therefore, miniaturization of satellites and their instruments questions about cost-effectiveness and affordability also promises cost-effective, flexible and resilient arise. The still existing data deficits in some regions, options for augmenting critical components of the mostly due to insufficient public funding (or, in some global observing system. cases, data sharing policy issues), may be offset through leveraging private sector data sources, which • The growing amount of data through private sector have been growing rapidly over the last decade. The investments poses a question on the conditions for the diversity of the new data produced by innovative utilization and sharing of such data by other sectors technologies, including “by-product” data derived at national and international levels. For some data from the Internet of Things, are expected to find their sets, it is likely that national and international space place in future data-assimilation systems, with special agencies will start to procure data services (from attention to their accuracy and reliability.

15 • Another investment in data infrastructure will also • Major modelling centres have moved from running be required to communicate weather and climate separate models for (global) weather and climate to information to users as efficiently as possible. The a unified modelling approach that enables seamless sheer volumes of data from new observational representation of the range of physical processes and modelling systems will grow substantially taking place across multiple time and space scales. across space and time scales, although this is not Such developments have necessitated application typically thought of as an infrastructure need. New of new scale-aware approaches for model physics post-processing algorithms to derive user-relevant that accommodate high-resolution short-range information from big data may be one way to forecasts through to longer-range predictions and overcome the data deluge and barriers to access. projections. Diagnostic approaches from NEWP, using Investments will be necessary in new information data-assimilation increments to characterize model systems (including communication components) biases, have led to climate model improvements, but that integrate observation and forecasting data with there is much more to do in this area. There are strong risk assessments into early warning and/or planning arguments for systematic assessment of climate statistics (for example, infrastructure design criteria), model performance in prediction mode. as part of infrastructure that forms a bridge to services. • Global modelling systems have continually been augmented by regional systems, which add value 3.2 Science and technology driving due to their higher spatial and temporal resolutions. advancement of numerical prediction Some of these systems have evolved into kilometre- scale (convective-permitting) ensembles with rapid updates (for example, hourly). Demand for high- Future progress in NEWP will utilize the new generation resolution forecasts has grown rapidly to address of computational technology, which will allow integration many weather-, water- and climate-related societal of better Earth-system knowledge into numerical issues with a high impact on sustainability. Such issues models. Furthermore, the ever-increasing capability of include: environmental conditions in urban areas and computers and new solutions for storage will enable megacities; water management (including the need transition to a seamless multiscale modelling approach. for long-term infrastructure investment to address The following developments have driven advancement expanding potable water shortages); and more frequent in the last decade: and more severe flooding that affects millions living in high-risk coastal plains and river valleys, and in densely • Scientific understanding and the availability of populated areas. Substantial progress has been made observational data has greatly improved the predictive recently in developing micro- to neighbourhood-scale skill for high-impact weather events such as windstorms models for comprehensive empirical and physics- (including tropical storms), storm surges, heavy based downscaling. Yet, there is still a demand for even precipitation and floods, droughts and heatwaves, higher-resolution (sub-kilometre) NEWP models with wildfires, pollution and volcanic ash. Many of these integration of local geography that facilitate sector- high-impact events have already been included in specific forecast products, for instance, in support of environmental forecasting applications, based on the energy management or public health care in highly attained skill factor (which is well documented in the populated urban regions. literature and by the records of the WMO Lead Centres for Deterministic Forecast Verification). • Research and community driven NEWP models have been used mainly for regional modelling but • There has been a growing complexity in numerical increasingly also at a global scale, in addition to models in terms of resolution (horizontal and vertical) the comprehensive forecast systems run by global and physical processes, as well as a nascent move and regional centres. Such systems are available as towards seamless Earth-system modelling. The open source and have flexibility for more customized arrival of more comprehensive ocean modelling application of NEWP with use-case-specific domain systems, better representation of the cryosphere and and model settings, as used widely by private weather of vegetation and the carbon cycle, and increased companies. Their use is broadening with the possibility understanding of the role of the upper atmosphere to run models on the cloud, together with community- and the deep ocean have been among those based R&D, including contributions from users in the developments. Some models have also begun to improvement of these models. incorporate biochemical feedback. In all of this, the water cycle remains the most critical part of the Earth • Climate predictions and projections have also system, and efforts have continued to improve all advanced significantly, including through the aspects of the water cycle – from the sky to the sea. application of ensemble approaches that closely

16 resemble those used in NEWP systems. Seasonal Other methods, such as ML, applied to sequences of climate prediction is now operational and used to radar and satellite observations promise progress in inform resilience and preparedness actions, such as predicting convective phenomena in the next minutes in the Famine Early Warning Systems. to an hour or two (the typical nowcasting range). The improvements in the very short forecast ranges will Coupled atmosphere–ocean models of growing enable more added value of specialized high-impact complexity and degrees of freedom have replaced weather forecasting products (for example, those atmospheric general circulation models, since the first for early warning systems, aviation and shipping). Intergovernmental Panel on Climate Change (IPCC) Additional value for users can be created through assessment report in 1990. Such numerical models co-design between users and application developers, that share many similarities with NEWP systems combining the deep knowledge of the evolution of have been used to produce climate projections weather systems with a better understanding of used to study mechanisms of climatic response impacts and automated integration of the forecasts and to evaluate emissions scenarios in support of into user-specific decision-making chains. Moreover, international policy negotiations. They have helped the trend is that many of these decision-support to warn of upcoming changes, such as the thawing models will be fully automated and based on machine- of permafrost and receding ice cover. However, the to-machine interactions. coupled systems have had to be run at lower spatial and temporal resolutions because of the resource- • R&D efforts will continue to advance the coupling of intensive and time-demanding requirement to perform atmosphere and ocean models at the global scale, such simulations on decadal to centennial timescales. in data assimilation and in forecasts throughout the At the same time, models of less complexity have entire forecast range from hours to seasons. A major often served as valuable tools to shed light on task is the design of hybrid ensemble–variational fundamental dynamical and physical processes that assimilation schemes for the coupled systems. In the are too difficult to entangle in more complex models. coming decade, significant advances are expected in However, it will be essential to understand how to the predictive aspects of the marine and terrestrial connect the information from the high-resolution and ecosystems coupled to the physical climate system; lower-resolution models. there are also likely to be nascent efforts in predicting some aspects of anthropogenic impacts.

3.2.1 Evolution of numerical Earth-system • High expectations are placed on progress in and weather-to-climate prediction addressing the challenges (see Box 3) of accurate representation of physical and dynamical processes The advancement of NEWP has meant the interactions in the models, since improvement of the physics by non-atmospheric components of the Earth system parameterizations has been slow so far. This includes can no longer be neglected or crudely represented. processes of deep convection, orographic drag, cloud Atmospheric weather-centric operational centres are microphysics and aerosol interaction, thermodynamic about to embark on a decade of advances, many of and dynamic coupling with land surfaces, snow and which will stem from the broader utilization of coupled sea ice, and their impact on the global circulation. environmental systems that include the ocean, waves, This has resulted in, inter alia, modest progress in sea ice, hydrology and land surface. Progress will depend quantitative precipitation forecasting (QPF). The on success in addressing outstanding challenges: potential for more substantial progress in QPF is likely to come from kilometre and sub-kilometre models • For the coming decade, it is expected that significant where the physical–dynamical coupling in convective improvements in weather forecasting products and systems is explicitly represented. Regional forecast services, including their communication to user models already do this, and global models are likely communities, may be achieved in the two extremes to follow within the coming decade. of the forecast range (very short and long time ranges), while the short- to medium-range forecasts • Atmospheric composition modelling has developed will continue their steady progress. Such expectations significantly in the last decade for air quality are based on developments in several areas described applications like smog and pollen warnings, forecasting below. of hazardous plumes from volcanic eruptions, forest fires, oil and gas fires, and dust storms. Mainly • Improved nowcasting and very short-range forecasting developed to account for the effects of meteorology (<6 h) will require high-resolution NEWP models to on air quality, the added atmospheric composition address some outstanding issues, including those variables will be used increasingly to understand associated with initial imbalances (model spin-up). better the possible feedbacks of the atmospheric

17 composition on NEWP predictive skill, especially • NEWP innovations will also come from further by changing the radiation budget or ultimately by transitioning from deterministic to probabilistic affecting cloud formation and precipitation. There is forecasts informed by large ensembles of high- still a long way to go for near-real-time weather and resolution numerical prediction simulations with chemical data assimilation to improve both weather a greater scope of applications like in hydrology. and chemical weather forecasts, but the retrieval of Bayesian statistical approaches promise ways to satellite data and direct assimilation of radiances is leverage these data to create fine-tuned (“sharp” and likely to achieve this. Some encouraging results are “calibrated”) probabilistic forecasts, which will have a the positive impact of assimilation of ozone on the great deal of value for many types of decision-making. wind fields. • Sub-seasonal to seasonal forecasting has Accurate representation of diabatic heating/cooling, demonstrated exciting progress, with operational especially in the tropics, needs to be addressed as implementations using, for example, the Madden– a persistent challenge in weather/climate forecasting Julian oscillation skill out to week 4, and seasonal for longer forecast lead times. This also applies to systems showing skill in the Northern Hemisphere other processes that are likely to improve longer- (NH) extratropical winter and the Southern Hemisphere range forecast skill such as capturing large-scale extratropical spring/summer, in addition to the dynamical teleconnections. The slow advances in the historical skill in the tropics. But the low signal‐to‐noise subgrid parameterization of some of the dynamical ratio for seasonal forecasts, especially in the Northern and physical processes indicate these complex non- Hemisphere mid-latitudes, especially in the Atlantic linear phenomena cannot be simulated satisfactorily sector, is challenging; it needs large ensemble sizes unless they are numerically resolved explicitly. and requires significant post‐processing. The daily- The expected increase of the space–time resolution to-seasonal-scale water cycle, sea-ice cover and of NEWP systems in the next 5–10 years carries a hope thickness predictions are also ongoing challenges. that this long-standing barrier to progress could be mitigated with a certain degree of success. • Regional climate simulations have been undertaken to provide more detailed information and have proved • Reanalyses and reforecasts from the latest modelling instrumental in progressing understanding of future systems are now used on NEWP timescales to calibrate climate impacts. The focus of these simulations the forecasts, to remove systematic biases, to assess is increasingly on extreme weather, and the latest the predictive skill and to place high-impact event generation of kilometre-scale convective-permitting forecasts into their climatological context. An ongoing models is delivering a step change in assessing challenge for presenting forecasts seamlessly to users future climate impacts. However, these models are is simultaneously conveying the future state of the still dependent on global driving models, which also variable (normal in the short range) and its anomaly need to improve through higher resolution and better (normal for longer ranges). parametrizations. © Mr. Šime Barešić (Croatia) Šime Barešić © Mr.

18 3.2.2 High-resolution global ensembles forecast systems. These centres, and their surrounding knowledge industries (including universities and national The targeted most important development in NEWP in laboratories), remain important loci for innovation. the coming decade or more should be the development Nevertheless, it is a challenge to maintain financially and deployment of global ensemble prediction systems independent and competitive high-quality models; with horizontal resolutions of 1–3 km, where some of the hence, a future trend of increasing R&D partnerships key atmosphere and ocean processes are represented might be expected. Such partnerships do not need to explicitly. There are three reasons for suggesting this: imply convergence of modelling systems (that is, the tendency of multiple centres or research groups to adopt • Extreme events are often associated with coherent, the same core systems), but it can be beneficial where non-linear, organized or self-aggregated structures, that has occurred. such as mesoscale convection (leading to extreme precipitation) or persistent quasi-stationary However, the pressures of the forecast cycle and the anticyclones (leading to extreme drought). There IPCC report cycle work against risk taking in this arena. is ample evidence that high-resolution models For example, in the last CMIP exercise, the list of capture better the scale interactions that are crucial participants included several models that were offshoots for simulating these phenomena. In addition, other of a few models, but this does not make these offshoots physical processes like land-surface feedbacks and less independent (Brunner et al., 2020). The cost of interactions with detailed topography are crucial for developing NEWP systems satisfying high-quality user the onset and evolution of many types of high-impact requirements argues against diversity, which would extreme events, such as windstorms, land-falling suggest NMHSs, or consortia of NMHSs, are still the hurricanes and coastal surges. most likely to have the financial capacity to be involved. A watchpoint for the weather and climate community • Current-generation models are unable to assimilate in the coming decade will be to strike a proper financial large amounts of crucial available observational investment balance between diversity and quality of information due to inadequate model resolution, which modelling systems. is an important, often underestimated, shortcoming of such systems. This leads to a prevailing assumption that the best way to improve the initial conditions of 3.2.4 Innovation through artificial intelligence a weather forecast is to increase the number of high- and machine learning quality observations. However, increasing strands of evidence demonstrate that improving the quality of Big data, which are massively present in all areas of the assimilating model, including its resolution and today’s life, create expectations for new methods of ability to use available data, is often a more effective collecting and processing observational data, including and efficient way of improving the initial conditions. a huge amount of non-conventional data. This carries high potential for vigorous R&D into new forecasting • Improved resolution is also important in reducing methods and applications. Forecasts based on AI and the systematic errors in a model, which are typically ML have already emerged in the last couple of years, of the same magnitude as the signals such models with notable interest of private weather companies with attempt to predict, especially in longer-range good IT resources in such developments. The research forecasts. and operationalization of those new approaches will accelerate in the coming decade, providing new It is reasonable to expect that achieving the target of opportunities for services at an unprecedented use 1–3 km global ensembles for NEWP with at least 50 scale. An interesting part of these developments is members should become possible by 2030, with the the engagement of citizen science and communities utilization of future exascale supercomputing. through crowdsourcing and informal collaboration. Such developments include the following (see also Box 3): 3.2.3 Quality and diversity of models • AI methods offer great potential for tasks that have There has been a proliferation in the number of global been limited by insurmountable data-processing climate and NEWP models in recent years. Considering challenges in the past. The combination of new the rapid improvement of HPC capabilities, and processor technologies and AI methods, allowing where centres can afford the computational cost of heavy-duty, parallel data processing for commercial such forecast systems, they should be expected to applications, enables weather and climate prediction remain in the business of the entire global to local applications.

19 One of the largest potentials of AI methods (here system cannot be underestimated, and work should mostly ML) is in the observational data pre-processing continue in reducing the systematic errors therein, and forecast model output post-processing. These thus developing physics-based process representation are areas where large volumes of heterogeneous for weather and climate change studies. data need to be processed to characterize data quality, complex multivariate observational error structures, identify patterns of causal connection 3.2.5 Advancing together: leveraging through for diagnosing sources of model error and extract public–private engagement information useful for a wide range of applications. Some AI-based systems have already demonstrated The increasing complexity of NEWP will create competitive results with very short-range limited- an even stronger need for extended partnerships area model forecasts, as well as some competency across the weather and climate enterprise. There on seasonal timescales. The availability of long will also be requirements for developing numerical historical observational data records, reanalyses modelling systems and for observation processing, and operational forecasts offers a great resource for data assimilation, results representation/visualization, learning from existing data and for deriving intelligent advanced verification systems and translation of analytics methods for operational use. ML methods forecasts into user-specific services. Several reasons also present opportunities for accelerating forecasting for enhancing public–private engagement (PPE) in the systems, especially in areas where new data sources coming decade are listed below: can be exploited and combined with traditional data or where there is no clear understanding of the physics. • The present level of government funding for NMHSs is insufficient to keep up the necessary pace of R&D. • Another great potential of ML lies in merging disparate Scientific contributions from the academia and the data sets, such as weather and socioeconomic data like private sector have increased in recent decades, air traffic management/operating data from airports. facilitated by an increase in scientific staffing, along The use of ML techniques could provide advances in with investments in capital resources and operations. enhancing the accuracy and automation of services Such investments have brought advances in many including in operational forecasting, decision support, areas such as observing capabilities and platforms radar activities, satellite applications, hyperlocal (space based and terrestrial), novel nowcasting forecasting, impact-based forecasting and situational techniques, regional- and global-scale NWP awareness for forecasters. ML offers opportunities capabilities, and climate risk analytics. Sustained to automate routine functions, thus enabling staff to growth by the private sector in scientific competencies undertake other, greater value added tasks. Advances have created, and will continue to create, opportunities in the underpinning science of ML will continuously for PPE, especially in the areas of research and service need to be translated into new products and services delivery. by NMHSs and the private sector.3 • There will likely be an ongoing requirement for the • While appreciating the potential of ML methods, academic community to play a leading role in aspects replacing entire physics-based forecast models with of basic, long-term and high-risk research, and in neural networks is unrealistic due to the immense scientific consulting. Complementary to that, enlarged number of degrees of freedom, the system’s non- partnerships among public, private and academic linearity and the difficulties of applying constraints organizations can facilitate faster transfer of research such as conservation. Proper, conservative EOF results into operations, thus bringing user benefits. decompositions may provide avenues to address For example, the private sector’s mounting interest some of the shortcomings. However, it is impossible and proficiencies in applied research and science for ML/AI to obviate the need for good physics- can provide some unique, cross-cutting partnership based and dynamical models. The size of existing opportunities. data archives is believed to be too small to train ML forecast models as skilful as today’s operational • Collaboration between public and private sectors systems, especially for rare weather events. Nor can will increase, since the whole potential of science such models explore future climates, which may be and technology can be unlocked only when data significantly different from today’s. The benefits of are openly shared, and standards are mutually a good seamless NEWP/climate change prediction agreed. Appropriate legal and ethical codes would

3 These issues are of paramount importance for planning of future service delivery, in particular for NMHSs. Thus, they will be addressed in more detail in a separate White Paper on the future of weather and climate services.

20 Box 2. Some predictability, dynamical and physical modelling challenges

Significant outstanding challenges remain in terms Apparent model and data-assimilation shortcomings of weather and climate predictive skill: include:

• Mid-latitude weather regime transitions at the week • Major uncertainties associated with parametrizations 3 to week 4 time range of key processes such as deep convection, orographic drag, cloud microphysics (and aerosol interactions), • Teleconnections between tropical phenomena thermodynamic and dynamic coupling with land surfaces, (for example, the Madden–Julian oscillation) and snow and sea ice, and their impact on global circulation mid-latitude synoptic-scale weather patterns at the monthly range • Limited understanding of the possible feedbacks of atmospheric composition on NEWP predictive skill • Seasonal-scale sea-ice cover and thickness predictions, and climate simulations and the different trends observed in the Arctic and Antarctic • Mismatching fluxes at the land–atmosphere, ocean– atmosphere and ocean–sea ice–atmosphere interfaces • Large-scale patterns of precipitation and mid-latitude circulation changes in climate change projections • Insufficient and incomplete treatment of systematic and random errors in data assimilation and ensembles • Deep-ocean circulation and ice sheet sensitivity to change in climate models • Insufficient use of diagnostic methods for tracing key sources of model error back to their roots in weather • Convective precipitation beyond the short range and climate models

Source: WMO (2015)

assure accountability to taxpayers and integrity of 3.3 Operational forecasting: from global all stakeholders in contributing to a two-way flow of benefits. Transparent processes, open communication to local and urban prediction and strong trust relationships will be desired PPE characteristics in the coming decade. Operational forecasting – a major task of all NMHSs since their establishment, and a rapidly growing • AI/ML is an area where the private sector is actively business in the private sector – has continuously innovating and has developed mature tools. This offers evolved over past decades. The production of forecast new opportunities for mutually beneficial partnerships information on the various time and space scales has across sectors. Large companies such as Amazon, steadily grown and improved in quality (accuracy, IBM, Google and Microsoft have been and will stay reliability and timeliness) thanks to the technological, at the forefront of AI and data analytics. This provides scientific and operational advances in NEWP. Among opportunities for public entities such as NMHSs the marked achievements in operational forecasting to partner and benefit from those developments. is the significant improvement in the ability to predict The incredible outreach capabilities of these “data high-impact events such as tropical cyclones, winter giants” can also be used in the dissemination part storms, flooding, heatwaves and other hazards. of the value chain where the delivery of information through smart mobile devices is the future form of Great progress has been achieved in adding value to access to forecasts, warnings and advice. In addition, model output by converting it into actionable weather to explore the potential of these new technologies, and climate information that users from various academic stakeholders are an integral part of those sectors can reliably integrate into their decision-making partnerships, with their supporting research and routines. The application of such post-processing has validation of concepts. been on the rise in operational centres (in public and

21 private sectors) that create high-accuracy tailored 3.3.1 Computational challenges and cloud products based on raw NEWP data. A major paradigm technology change has occurred in the last decade through the introduction of the concept of impact- and risk-based Progress in numerical modelling has always been linked forecast and warning services. These require coupling to the ever-increasing capacity of computers to handle of NEWP output data with information from other complex tasks with vast amounts of data and with the systems and models, combining meteorological with speed needed to deliver model output on time for its non-meteorological information to support decision intended use. In the coming decade, a major challenge tools used at regional to local and urban scales. for research and operations in NEWP development will be the uptake of new opportunities offered by exascale Impact-based forecast products have become a key computing and cloud technology: element of (multi-hazard) early warning systems in support of disaster risk reduction (DRR). Social science • It is expected that introduction and advancement of has become important in providing the context of exascale computing systems will begin in the 2020s. vulnerability and exposure needed to better understand However, only a few “top-of-the-pyramid” centres will the risks of hazardous impacts since DRR actions are be able to afford such computing systems soon, owing multisector and multidisciplinary by nature. Knowledge to their high cost. Promising signals come from some of user behaviour is also important to inform and centres like the Met Office in the United Kingdom of provide more effective communication. Great Britain and Northern Ireland, which has already published a plan for modernization of its HPC system A vast amount of high-resolution global and regional at a cost of £ 1.2 billion over the next 10 years. More NEWP data is made freely available to all potential generally, an optimistic expectation is that HPC users, including NMHSs, through the open data policies systems will continue to expand, and their utilization adopted by most major operational centres. The need for may become increasingly accessible and affordable transformation of the operational practices of NMHSs for more NMHSs, which could allow them to operate beyond the major producing centres has become evident regional NEWP systems in partnership with leading with the ever-increasing amount of such open and free global forecasting providers (private or public). NEWP information. Intelligent use of this resource frees up NMHSs to focus on enhancement of the service delivery • The use of big data analytics will require investment side of the value chain, where the value to users is generated in infrastructure and people. Various options for by the translation of NEWP output into impact-based infrastructure uplift are available, and there is an actionable forecasts, warnings and advisories. The same opportunity to leverage scalable cloud computing development in open data has unlocked opportunities for platforms to deliver big data analytics and storage the private sector, where it has strength in IT and applied infrastructure. An advantage of this approach is that science, to develop innovative applications and services data centre and computational resources development, in niche industry areas and for the public. maintenance and support are shifted to the cloud services provider. Furthermore, cloud computing Investment in computational capability is needed to platforms will provide the capability to scale computing improve simulations and prediction systems, and and storage infrastructure on demand. also to store and provide real-time and subsequent research access to data sets. These investments will • The amount of data produced daily by large centres globally range from tens to even hundreds of billions will be of the order of a few petabytes by the end of this of dollars over the coming decade. However, they will decade. Even today, only parts of the data are available enable improved services to significantly mitigate to users (for example, Deutscher Wetterdienst makes the impacts of weather and climate hazards and available about one third of the daily production), and create appreciable socioeconomic benefits. Though an even smaller fraction is downloaded and used. The substantial, the investments represent a small fraction main reasons are the limited bandwidth of the Internet of the annual impact of day-to-day weather variations and the complexity of using the data provided. Cloud on economic performance, which is estimated at US$ technologies have matured in recent years; they are 1.5 trillion annually. In addition, natural disasters force now available everywhere and offer a wide variety a US$ 520 billion drop in consumption and drive ~26 of functions. They support the so-called “bringing the million people into poverty every year. Moreover, data to the user” paradigm. The bandwidth issue can these numbers are likely to grow due to the impacts of be solved by providing the output of NEWP models climate change and trends in demographics as people and big observational data in cloud data storage. This are increasingly living in cities and along coastlines. will enable users from a weather enterprise to run their Hence, the return on such investments is anticipated own NEWP models and sophisticated applications on to be huge. the cloud, which may open new business opportunities.

22 Box 3. Potential major breakthroughs from ML

The use of ML will lead to significant progress over the parameterizations on data provided by higher- whole range of “classical” modelling, data assimilation resolution models like large-eddy simulation models and post-processing algorithms in the coming decade. It can be applied at each step of the weather forecasting • ML can be used in post-processing of numerical process. Advantages include the following: variables, allowing incorporation of non-linear correlations • ML can improve the utilization of observing systems, allowing forward planning of maintenance activities, • ML can help to estimate impacts by combining improved quality control and intelligent filling of data model output and further observations or statistically gaps relevant data

• ML can improve the ability to input information A wide range of open-source ML frameworks (for from complex observing systems into models’ data example, TensorFlow and PyTorch), together with well- assimilation by providing better compression rates, known programming languages like Python or C++, which will significantly increase the precision and support the development of ML applications. It therefore speed of forward operators becomes possible to tackle large-scale problems with the advent of dedicated accelerators. A cloud infrastructure • ML can help model and estimate observation errors provides easy access to accelerator hardware. If in data assimilation meteorological data are readily available, small- and medium-sized enterprises can develop successful • ML can allow incorporation of further complex applications, especially if they bring together expertise subgrid physical parametrizations, or training subgrid from different disciplines.

• A prerequisite for broader use of forecast information which in a broad sense should refer to a complex and products is the availability of tools that allow set of characteristics such as accuracy, reliability, easy integration of the data into user processes. consistency, timeliness and fitness for purpose. Implementation of advanced data interactions, such as Moreover, verification results performed by various subsetting or transformations, is relatively easy with providers should be comparable to allow user choice the available cloud resources. Cloud infrastructures and help providers apply continuous improvement of provide a development and operating environment their processes and products. that improves effectiveness and efficiency on any scale, accelerating the development process and daily • Forecast providers usually apply a strong verification operations. regime internally. This involves the understanding of how much accuracy is gained in each step of the forecasting system, particularly if numerical 3.3.2 Verification and quality assurance models, post-processing and human intervention are combined. Development of NEWP has always been accompanied by verification of forecasts against reality. Scientific • In addition to internal verification regimes, the use verification methods have evolved over the years, and of independent third-party evaluators can become a some have been standardized through GDPFS regulations: strong necessity to provide objective assessment of the quality of a product from the standpoint of end-user • NMHSs have been the traditional providers of forecast operations. Such assessments, applied systematically, and warning information, but the current landscape can offer credible, independent, operationally relevant of service providers is extremely diverse and varies information on the capability of a certain provider, significantly from country to country. Users today have based on identified key attributes. The number of to choose which provider to select for the information truly independent third-party organizations in the they need. Thus, in the operational context, verification weather enterprise that possess the aforementioned becomes a key source of information about quality, traits in evaluation is extremely limited. This opens a

23 niche and business opportunity for developing such statistical post-processing of high-resolution/ capabilities by possible new branches of stakeholders multimodel NEWP outputs, which removes local specializing in verification and eventually offering biases and adds accuracy (for example, by about “verification as a service”. WMO verification standards 1 day of lead time compared to raw NEWP). Further and best practices, including exchanges of forecasts R&D in rapid-update NEWP analysis/nowcast cycles in near real time, are a solid basis to build on. The and observation-based nowcasts will bring new NEWP systems verification process can be done capabilities for providing timely situational awareness independently by each producing centre and through information and automated alert systems for imminent regular discussions among centres. Such practices hazards (for example, for decision-making horizons could be expanded to private forecast providers. of minutes to tens of minutes, typical for hazards like tornadoes, or operations like aircraft in flight). • In a typical “business-to-business” relationship Advanced AI systems have already shown an ability between a provider and a user, an effective method to carry out such functions “on the run”, which may to inform the end user of the accuracy and reliability alleviate current challenges linked to the exponential of a forecast product is through direct partnership. increase in outputs. The formation of partnerships, when done correctly, can ultimately provide a level of flexibility and • In this new information environment, a forecaster will understanding that will ensure the forecast evolve into a trusted communicator and interpreter capability meets the requirements for intended use. of weather and climate information, able to explain Furthermore, the co-design of products between the associated impacts and assist users in their decision- provider and user builds a common understanding making. However, despite all the progress, local of needs, capabilities and limitations. knowledge may still add value to NEWP output in extreme situations when rapid decisions are taken • With further business process integration and under strong pressure. evolution of impact-based forecasts and warnings, it is necessary to develop the verification methodology • The ability to establish a causal narrative around the beyond evaluation of weather parameters into weather, attuned to a customer’s needs and level of measuring success in predicting impacts. This is most understanding, is a precious skill that needs to be likely to be a stepwise process based on availability developed for the new generation of “forecasters” of information from users on the impacts and related through adequate changes in the education and performance of their operation. Such developments training curricula. The ability to assess meteorological happening at the end of the value chain, often called data from multiple sources with a critical eye based on “business integration”, will most likely be actively understanding and experience is equally valuable. The worked on by private sector stakeholders with importance of these skills should not be underestimated; naturally closer connections to affected businesses forecasters will continue to be distinguished as an expert (for example, in aviation and other transport sectors, partner and not just a data provider. Hence, the warning or in the energy sector). A significant challenge process (although a largely automated workflow) must in this regard will be to achieve certain levels of remain the domain of the forecaster for the foreseeable standardization of the impact information and the future, to correct the otherwise automated first-guess ways it can be reported back to forecast providers. forecast, especially for rare events.

• Automation has been facilitated at the international 3.3.3 Further automation of post-processing level through the online discoverable mechanisms systems and the evolving role of human for data and products established by WMO, as well forecasters as different levels of GDPFS centres, providing monitoring and forecast products. Further The increasing complexity of NEWP systems in the development of this system will require the centres coming decade, and the huge amount of data and to be more collaborative with local agencies/offices information produced from each run of each model, will when extreme weather events affect specific regions. dictate a rapidly growing automation of the remaining The goal should be to establish disaster event-driven non-automated system elements. The historic “human– data discovery and service mechanisms focused on machine mix” will evolve in a new way in which the role rapid response to disaster impacts. This is particularly of the human will be greatly changed: important for developing parts of the world that are most vulnerable to those impacts. Such challenging • Many of the tasks performed currently by a human tasks should be assisted by PPE and could incentivize forecaster are likely to be replaced by automated developing countries to contribute more to the global processes. Such automation will be enabled by observing system and data sharing.

24 3.3.4 Leveraging through public–private 3.4 Acquiring value through weather engagement and climate services

Realization of the vision of an enhanced NEWP system While NMHSs in all 193 WMO Members are still the supporting operational forecasting in the coming decade public entities designated by governments to provide will require a new community approach with close meteorological and related services, many other engagement of the public and private sectors, as well providers have entered the weather forecasting business as academia. The need for this has been fully understood in recent decades. The spectrum of these new players at all levels, including by WMO with the adoption of is diverse, from intergovernmental organizations like the Geneva Declaration – 2019: Building Community ECMWF, through to private sector companies of various for Weather, Climate and Water Actions. The rationale sizes and profiles, to academic institutions and various of this new approach is related to the following factors: non-profit community formations.

• The ever-existing and growing pressure on the public This profound change into multi-stakeholder delivery of budget will most likely continue in the coming decade, weather and climate services is driven by several factors creating a gap in the necessary resources to address such as: rapidly growing demand for such services all challenges and ensure sustainable operations from public and private sectors (also generating new utilizing state-of-the-art technology. Therefore, NMHSs opportunities for fundraising through venture capital); and other public sector stakeholders should catalyse the open data policy of many public agencies and the creation of a synergistic ecosystem that includes the technological advancement that made available academia, technology companies, start-ups, citizen affordable solutions for entering into the service delivery science and others in the wider weather and climate business; and the improved skill of the forecasts, which enterprise. raises demand and user confidence. As a result, there is now a new era of weather and climate services with • NMHSs could continue to play a central role in many new challenges and opportunities. future weather and climate forecasting as national agencies funded through the public budget to operate As stated by Murphy (1993): “… forecasts possess no and maintain essential meteorological, hydrological intrinsic value. They acquire value through their ability to and climate activities determined by national influence the decisions made by users of the forecasts.” and international mandates. However, in the new This White Paper is concerned with the part of the value competitive and highly dynamic environment, the chain that produces the weather forecasts and climate public sector will need to adapt and prove its lead predictions. An incredible amount of information is role through sustained performance. In this way, it made available at the end of this part of the chain to would support other stakeholders to build upon the those whose task it is to translate this information into infrastructure and information provided as a public user-oriented decision-support products – the basis for good. Applying an open public data policy will be weather and climate services. This is an area of dynamic extremely important for the private sector and publicly development and interaction among the sectors of the funded research communities. enterprise, in addition to interactions with the growing user community, as well as with other sciences, such • The private sector is particularly competent in the as social science and economics. The 2019 High-level development of user-oriented post-processing of Round Table on the launch of the OCP Partnership and numerical analyses and forecasts. However, a major Innovation for the Next Generation of Weather and obstacle to usage is the availability of consistent Climate Intelligence acknowledged this dynamic and training data sets. Therefore, the provision of defined it as a separate theme of the key challenges, sufficiently long data sets of reforecasts and reanalysis namely “Theme 3: Demand and Supply of Services … of the modelling system is essential. Weather/Climate/Water/Environment intelligence driving decision-making” (WMO, 2019a). A separate White Paper • One particularly important area of multisectoral on the future of weather and climate services is planned collaboration in the future is the development of proper for issuance in 2021, to address in detail the decadal and accepted forecast verification methodologies. This expectations in that area. will require building skills to effectively implement and assess verification techniques, agree on the baseline The following aspects of the services linked to the against which a forecast is going to be measured subject of the current White Paper are presented briefly and have a good perspective of how the forecast is as focused areas, to facilitate future work aligned with to be used, since the required quality of a forecast the overall Vision 2030. ultimately depends on its anticipated use.

25 3.4.1 User perspective categorical deterministic forecasts, are much more adapted to use measures of uncertainty and confidence A central element in the discussion of forecast quality in their decision-making. Thus, probabilistic forecasts will and related improvements is routinely overlooked: the become the main form for providing services integrated end user. In particular, from an end-user’s perspective, into user risk-assessment tools. Model developers, what does “good” mean in the context of a forecast? operational forecasters and users should work together From a purely scientific perspective, the quality of a to co-design future services and information flows, forecast can be measured using sound verification for a smooth transition to probabilities. The expected techniques. However, conventional verification metrics improvements of ensemble NEWP will provide reliable (for example, 500 hPa height patterns) do not always probabilistic products to support such service design. translate into how good a forecast is from an end-user’s perspective. The real measure of forecast quality and For the next generation of forecast products needed value is in whether end users can routinely use forecasts for critical decision-making missions like disaster risk in a manner that allows them to achieve their objective reduction, stochastic downscaling and calibration will (for example, protect life, improve operational efficiency be vital for running impact models e.g. in hydrology, or reduce costs). agronomy, health, energy and so on. For instance, to successfully apply forecast-based-finance schemes The end-user community is extremely diverse; what aimed at providing financial assistance to developing might classify as a good forecast product could countries sufficiently ahead of time before being hit by a benefit one group of end users and not another. severe weather event, the forecast probabilities must be Beyond providing general measures of meteorological properly calibrated and be fully reliable. Therefore, it will predictive skill, it is essential to provide user-oriented be vital to provide considerable past reforecast data and information about how a forecast (or related product) to ensure that high-resolution and high-quality data from performs relative to a specific operational context or global ensemble systems are relayed on time to the local decision-making process. Furthermore, users of forecast agencies responsible for issuance of early warnings. An information can provide the necessary information to important aspect here is the commitment to free and improve the salience and utility of forecast products, open data policy to allow developing countries’ NMHSs which makes the user community an essential partner to optimally benefit from global ensemble systems. in the value cycle.

3.4.3 Bridging between high-impact weather 3.4.2 Forecasts for decision support and climate services

A challenge for weather and climate services is to find Outliers, or unprecedented events, represent a special ways of bringing the delivered information into the challenge for weather and climate services. Although decision context. Trust and intelligibility are important weather never exactly repeats itself, describing climate issues, since many of the uncertainties are subjective, in terms of summary statistics and extreme events is and the predictions themselves may be unverifiable (e.g. usually possible. Even record-breaking events are not for rare events, or for climate change). Ultimately, the difficult to comprehend, if they can be regarded as part construction of actionable information involves building of a continuum and to be expected given a sufficiently a chain of evidence, and the reliability of each link in the long record. chain needs to be open to assessment. Approaches that focus on relevance therefore have a role to play. The challenge of describing rare events has been a major topic in the attribution of extreme events to Supporting decision-making through forecasts at the climate change. The standard approach to extreme event human scale (for example, local conditions) is also a attribution is probabilistic, comparing distributions of challenge. The local environment at very high spatial states with and without climate change, and relating resolution – such as autonomous vehicles and smart a given event to a percentile or return period in those infrastructure – opens the breadth of possibilities and distributions. This necessarily involves creating an event expectations for high-fidelity forecasts for planning- and class defined in terms of one or perhaps two variables, logistics-based decisions. coarse grained over somewhat arbitrary time and space scales. However, for an event (for example, Hurricane The trend towards supplying users with probabilistic Sandy, which devastated New York City in 2012), such forecast products will continue in the coming years. a description seems inadequate and certainly fails to Such an approach is crucial for forecasts of chaotic describe the multiplicity of compound impacts. Thus, systems such as weather and climate. The change now an alternative approach has been developed where is that users, who in the past were strongly demanding storylines of the event are constructed, which can be

26 perturbed to create counterfactuals. This approach takes the standpoint of academic scholars. Indeed, in many the weather event as given (as a random occurrence), quarters, such excellence is considered a “career risk”. and asks what the effect was of known aspects of climate change, such as a higher sea level, warmer sea-surface The educational system also needs to adjust to new temperatures or a moister atmosphere. Such an approach partnerships that span the weather enterprise. The is nicely aligned with the case study perspective that is significant growth of the private sector creates far more well established in weather science, and offers a bridge career options in operational forecasting than the public between weather and climate services in the case of sector (for example, NMHSs) does, where employment rare events. of graduates in academic sciences has been decreasing in some countries. While education focuses primarily on To tackle the problem of unprecedented high-impact a foundation of knowledge, a shift needs to take place weather and climate events, one approach is to mine in where examples are used in problem solving and ensembles of long-range forecasts and hindcasts to critical-thinking exercises, to prepare students for new look for rare extreme events that could have occurred employment opportunities and other changes in their but did not. For rarer, unprecedented events, rare event field. The private sector frequently lists computational statistical methods offer even more potential.4 skills, oral and written communication, critical thinking and fundamental knowledge of private sector applications as underrepresented topics in academic curricula. 3.4.4 Education and training for future operational meteorologists/forecasters

What makes an exemplary operational meteorologist/ forecaster? Such an individual is likely to excel in skill acquisition, which calls for continuous learning and self-improvement. As a provider of the most valuable meteorological information for decision makers, sectors and the public, an operational meteorologist/forecaster should: have a good understanding of atmospheric science; excel in multisource data application; know about NEWP systems; understand the requirements of various users from different sectors for meteorological services; have in-depth knowledge of historical events so they can provide perspective on upcoming events; and possess relevant interdisciplinary expertise so they can develop impact-based and risk-based meteorological disaster forecasts. These characteristics help to make an operational meteorologist/forecaster an expert. In addition, knowledge of emerging technologies in big data processing like AI and ML, and their application in the forecasting will become increasingly important for the future forecasters.

Academic institutions have attempted to develop and employ interdisciplinary knowledge for addressing the interwoven challenges of NEWP in recent years. Coming to terms with these challenges is requiring new modes of thinking from all regions, as well as a range of disciplines varying from computer science to economics to public health. It will also require cross-national and multi-institutional modes of collaboration. Scientific © iStock challenges are therefore exacerbated by geopolitical and pedagogical challenges. The systems of merit, reward and recognition do not lend themselves easily to rigorous excellence in interdisciplinary research from

4 These methods arose in statistical physics and represent a form of “importance sampling”, which allows most of the computational resources to be focused on the events of interest, rather than on the events in the middle of the distribution (as in the brute-force approach). The method has been applied to heatwaves, and there appears to be no reason why it could not be operationalized.

27 4. CONCLUSIONS

The decade 2021–2030 will be the decisive period part of the weather and climate enterprise, which could for realization of the 17 United Nations Sustainable help them in their mid- to long-term decisions. As Development Goals. Most of these goals have links any prediction has to account for uncertainties, some with the changing environment – climate change, water optional ways of development are also highlighted, and resources and extreme events. The desired outcomes thus additional considerations – economic or operational in all areas require enhanced resilience, which is also – may need to be applied in making choices. the main call of the WMO Vision 2030 (WMO, 2019b). The advances expected in weather forecasting and A main expectation of the coming decade is that with the climate prediction during this decade will support those ever-growing demand for weather and climate services, ambitious goals by enabling a next generation of weather the value of information will be much better understood and climate services that help people, businesses and and appreciated. The market growth for such services will governments to better mitigate risks, reduce losses, and stimulate a variety of new business models that may fit materialize opportunities from the new intelligence of diverse national and transnational frameworks, improve highly accurate and reliable forecasts and predictions. the quality and access to information, and contribute to the robustness and efficiency of the requisite This White Paper is based on a collection of views and underpinning activities like environmental monitoring insights from 27 leading experts from the research, and research. Improved coordination and collaboration operations and education fields of the weather and among the public and private sectors of the weather and climate enterprise. The aim was to outline the most urgent climate enterprise, with strong engagement of academia, issues that need to be addressed for the desired advances will accelerate the achievement of the vision for 2030. to happen by the end of this decade. “Forecasting” the In doing this, the expectation is that each sector will future of weather and climate prediction has similar continue by playing its most relevant role; at the same inherent difficulties as in any kind of prediction of the time, potential synergies will be effectively realized. future state of a complex and intertwined system. The key messages and recommendations summarizing This paper has shown the major challenges of and the discussion of the previous chapters are given below. opportunities in each of the first three components of the innovation cycle (see Figure 1): infrastructure, R&D and operations. The services component will be 4.1 Towards improved systems for discussed in another OCP White Paper. Several key forecasting: global, regional and local factors will determine the success and effectiveness of future progress in NEWP, including open data approaches access, ability for near-real-time data processing and transmission, monitoring data and forecast quality, and • It is likely that the main structures participating in the quick and focused dissemination to users. Some of these forecasting enterprise at global, regional and national factors are common to the three components of the levels will not change significantly in the coming innovation cycle (infrastructure, R&D and operations), decade. However, there will be notable shifts in but their implication and logic can be different, such roles, functions and performance requirements. Many as sharing data policies or HPC requirements for R&D exciting opportunities lie ahead, stemming from the and operation. Hence the need to structure this paper expected breakthroughs in technology and research along the components of the cycle of innovation, even developments; realizing their potential should be if this could give the impression of repetition among the key task for the whole community. The desired sections. It has been emphasized that the only way to (and possible) benefits to society can be realized only address these challenges and enable the uptake of the through a concerted effort in these times of huge risks technological and scientific achievements is through an related to the changing climate, diminishing water inclusive public–private partnership, including academia. resources, and increasing frequency and intensity of high-impact weather events. This chapter highlights some key messages and recommendations that have transpired during the • Governments need to sustain public investments in process of integration of the individual inputs of the the weather and climate enterprise if high-quality contributors. The purpose is to provide professionals and information is to be produced to save lives, protect users of forecasts and predictions with information about infrastructure and realize additional socioeconomic trends and expected development in the forecasting benefits. Investment in the global observing system

28 is fundamental, especially for sparsely populated • At the national level, NMHSs need to engage more parts of the world and over oceans. The same applies in community-based modelling and data initiatives, to the supercomputing capability needed to create and R&D consortia. The importance of working closely weather and climate information of high accuracy and with users and the opportunities for PPE should be reliability to address growing societal needs. Public recognized and promoted. Confederating activities and investments in the fundamental systems will benefit targeting intercountry collaborations will help scale all enterprise stakeholders, including those from the core NMHS technology and capability development. private sector, due to the integrated nature of the The increasing R&D coordination provided by WMO enterprise. This will incentivize innovation and better could help to nurture this approach. services for all. The private sector can help make the case to funders and development agencies for State- • For NEWP, the development/improvement of run long-term observations and reference networks climate models needs to be in line with the that underwrite downstream benefits. strategy for weather prediction. Efforts to overcome fundamental limitations in physics parameterization • Investments in observational networks will need are accelerating in the NEWP community as the to be coordinated with those for NEWP systems horizontal grid spacing falls into the grey zone, development, considering the impact of observations needing further R&D. The climate community could on forecast skill. An integrated system-of-systems take advantage of the scientific achievements made approach should be used with considerations of scope, in the NEWP community. A unified single model complementarity and cost–benefit analysis at all space system across a range of timescales (nowcasting and time scales. The global coordination role of WMO to centennial) and spatial scales (convective scale will continue to be important for better coordinated to climate system Earth modelling) is possible and investments based on the pronounced WMO Integrated desirable in this context. Global Observing System concept, with broadening of the participation of the private and academic sectors. • The evolution of global ensemble NEWP systems towards very high resolution (1–3 km) and improved • International cooperation at State level (for example, representation of the model physics will pose a through WMO) will continue to be a critical success key dilemma for service providers, including many factor in improving weather and climate forecasting. NMHSs, who are currently operating their own The exemplary cooperation among WMO Member regional modelling systems. It is very likely that the States and Territories for many decades has already quality and small-scale details of the high-resolution proved that. This tradition must continue and expand, in global products will exceed those of locally run particular ensuring free and unrestricted international models operated with limited computational data exchange and sharing, and coordinated efforts resources. Thus, the resources used for such local in further developing NEWP systems. The example of models may appear to be a waste without adding ECMWF is a showcase of how international cooperation value to freely available global data and information leads to a clear path for success. Similar international to support the operations of those services. It seems partnerships would help to accelerate advances in likely that, as now, in future high-resolution global NEWP and to cope with the complexity of the prediction ensemble modelling systems will be run operationally systems, which range from the nowcasting approaches only by those organisations having access to very built on observations for the minute-to-hour timescale, significant supercomputing capabilities. In addition, to NEWP ensembles for day-to-season timescales. it is highly valuable to enable downscaling of forecast WMO should therefore increase significantly its effort data from the global ensembles into useful products in international R&D coordination and promotion. for the various national impact sectors, especially for developing countries. The dissemination and • The WMO coordinated system of global and regional utilization of ECMWF and North American Ensemble centres and facilities (GDPFS) should continue to Forecast System forecasts by many NMHSs and serve as the backbone for product generation and private organizations are showing the viability of make NEWP forecast information available for use such an option. by various service providers. Other forms of bilateral or multilateral cooperation may be formed among • Thus, at national level, the focus of NMHSs may shift countries sharing the same regional weather/climate towards alternatives to limited-area modelling based processes and hazards. WMO centres should continue on advances in the use of AI/ML instead of running to dedicate effort and resources for capacity-building small limited-area model ensembles. Applying in regional downscaling of the global weather and sophisticated AI schemes (trained using the reforecast climate products to support enhanced national data from the global ensemble) to directly calibrate services. and downscale the output from global ensembles

29 to postcode scales may prove much more effective 4.2 Progressing together with and cost-efficient. Scientists from NMHSs should be trained in these AI schemes, applying them to the developing countries postcode scales in their own countries and using national observations to optimise the calibrations. This • The global weather and climate enterprise benefits might be one of the disruptive changes of the coming from the development and expansion at regional decade which would require advanced planning and and national levels. All nations should be providers change management. of observations to initialize, verify and improve forecasting at all spatial and temporal scales. Yet, • Implementing NEWP systems with post-processing, there is currently a marked inequality in the availability production and visualization on the cloud may offer of weather and climate forecast information in a unique advantage for developing countries as a developing countries, and a lack of capacity to produce more sustainable and cost-effective approach. It may and utilize such information to save lives and bring also allow for improved cooperation and burden socioeconomic benefits to society. Stabilizing the sharing between public and private partners. Certain developing countries’ national observing networks competencies and financial resources could be made and ensuring their long-term maintenance for available to implement this, with an emphasis on providing standard quality observational data are maximizing scalability (for example, cloud-based of paramount importance. In addition, data must be software solutions that can be customized and shared internationally – an obligation of each and redeployed to assist other countries with similar every WMO Member. needs). • One striking element of the situation is that the massive • Within the next 10 years, WMO, in partnership with investments (estimated as billions of dollars) through the private sector and academia, could come to an various development assistance projects, engaging agreed methodology for validation of quality, and numerous development funding agencies, have often recognition and attribution of various providers from provided disappointing results. The impact of these the public, private and other sectors. This would allow investments on local capacity has been low. Some such a methodology to be promoted and applied in analysts pointed out that the main reason for such a the second half of this decade, thus facilitating and failure was a model in which the development funds guiding the use of sources of proven quality in areas were spent mostly on equipment such as automatic where weather and climate forecasts are used for weather stations, communication systems, computers critical decision-making. to run NEWP and weather radars (widely not utilized © iStock

30 after delivery due to maintenance problems), with monitoring, prediction and early warning, so little attention to the self-identified goals and needs forecasters can still play an active role in achieving of NMHSs, and without planning for sustainable sustainable development of the global economy operations in the post-project period. and society. Much can be done now by sharing the best global and regional prediction data sets with • The increase in the involvement of many nations in developing countries. Scientists from developing NEWP R&D will accelerate innovation. Furthermore, countries can contribute to the development of global national weather enterprises can act as a magnet NEWP systems. When a NEWP system does not for technical capacity-building, engendering perform well for a specific high-impact event, regional improvements in education and infrastructure. It is scientists need to be able to study the factors that the therefore vital that all countries have access to and forecast had issues with and make recommendations expertise/capability to utilize the highest-quality on how to improve the global forecast system. Forums weather and climate information. need to be further developed where these insights can be fed back to the primary forecast centres and • Many players in the weather enterprise well implemented in new operational cycles. Established understand the need for a paradigm change in the frameworks such as the WMO Regional Climate provision of development assistance. With regard Centres and Regional Climate Outlook Forums should to NEWP, one approach for a country with limited exploit their full potential in this endeavour. resources could be to obtain its own modelling capability. A more efficient path might be to invest • NEWP model providers and HPC vendors could available resources to serve national purposes work together to integrate multimodels (using through building sustainable expertise and multicentres and at multiscales) in HPC clusters with computation infrastructure for accessing and utilizing pre-optimization, then make those models available available cost-efficient technology and available on the cloud as a service. Rather than buying HPC data from international sources like global model and running a NEWP model, the developing countries products. Production of shared global forecasts could buy cloud service to run NEWP systems for at high resolution is highly desirable to meet the their specific region and period in an affordable way, needs of countries without the capability to run their without worrying about the complexity of future own NEWP system (with the need for appropriate heterogeneous HPC, optimization of models and timeliness for data availability). downloading massive amounts of observational data.

• The ECMWF cooperation model may need to be • NEWP services also require investment in academic replicated to a certain extent to other regions, which institutions in developing countries to carry out will allow consolidation of human resources and targeted research, scientific consulting and training expertise, and optimization of running costs. Such of future expert forecasters and communicators. a regional approach could be successful only with Collaborations between universities of developed strong political support for national-based regional and developing countries would be advantageous in institutions and scientists to build capacity, as well as this regard. WMO, in collaboration with development for existing regional climate centres. This is a task and agencies, needs to play an enlarged role in supporting challenge for WMO in cooperation with other relevant developing countries to contribute effectively international organizations, including development and sustainably to their national observational agencies. PPE could be a guiding principle to ensure programmes as components of the global observing partnerships, availability and usability of skilled system. regional/local products. • It is critical that a cross-national, multigenerational • The weather and climate enterprise needs scientific community of scholars is built that will rethink the insights from scientists around the world. It is way experts research, teach, publish and influence essential to ensure a closer strategic alignment across the whole multidisciplinary domain of weather among developing countries and enhance regional and climate forecasting. This will facilitate a revolution meteorological cooperation. This would improve in training through equal partnerships across all regional capacity in meteorological disaster nations’ institutions.

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36 For more information, please contact: World Meteorological Organization 7 bis, avenue de la Paix – P.O. Box 2300 – CH 1211 Geneva 2 – Switzerland

Public Private Enagement Office Email: [email protected] https://public.wmo.int/ppe

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