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WEATHER CLIMATE WATER
Public-Private Engagement Publication No. 3
WMO Open Consultative Platform White Paper #1
Future of weather and
climate forecasting
WMO-No. 1263
Cover photo credits: © iStock © World Meteorological Organization, 2021 The right of publication in print, electronic and any other form and in any language is reserved by WMO. Short extracts from WMO publications may be reproduced without authorization, provided that the complete source is clearly indicated. Editorial correspondence and requests to publish, reproduce or translate this publication in part or in whole should be addressed to: Chairperson, Publications Board World Meteorological Organization (WMO) 7 bis, avenue de la Paix P.O. Box 2300 CH-1211 Geneva 2, Switzerland Tel.: +41 (0) 22 730 84 03 Fax: +41 (0) 22 730 81 17 Email: publications@wmo.int ISBN 978-92-63-11263-7 NOTE The designations employed in WMO publications and the presentation of material in this publication do not imply the expression of any opinion whatsoever on the part of WMO concerning the legal status of any country, territory, city or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries. The mention of specific companies or products does not imply that they are endorsed or recommended by WMO in preference to others of a similar nature which are not mentioned or advertised. The findings, interpretations and conclusions expressed in WMO publications with named authors are those of the authors alone and do not necessarily reflect those of WMO or its Members.
WEATHER CLIMATE WATER
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
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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.
3covering 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
4INFRASTRUCTURE
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.
52. 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.
6Progress 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)
8Montreal Tromso Offenbach
Ottawa ECMWF St Petersburg
Anchorage
Moscow Novosibirsk
Exeter Obninsk Khabarovsk
Edmonton Toulouse Vienna Vladivostok
Montreal Rome Offenbach
Tromso Tashkent Tokyio
Washington Casablanca
WinnipegAnchorage Ottawa ECMWF Tunis AthensSt Petersburg
Moscow Novosibirsk Honolulu
Barcelona Exeter Cairo
Obninsk Khabarovsk
Miami
Edmonton Toulouse Jeddah
Vienna Karachi Vladivostok
Dakar Rome TashkentNew Delhi Beijing
Tokyio
Washington Casablanca
Winnipeg Tunis Athens
Barcelona Cairo Honolulu
Hong Kong
Miami Algier Nairobi
Jeddah Karachi
Dar es Salaam
Dakar Beijing
Callao New Delhi Darwin
Brasilia Nadi
Vacoas Hong Kong
Niteroi Algier Nairobi
La Reunion
Valparaiso Dar es Salaam
Callao Darwin
Buenos Aires
Brasilia
Pretoria Nadi
Vacoas
Niteroi Melbourne
La Reunion
Valparaiso Wellingtone
Buenos Aires Pretoria
Legend
Melbourne
World Meteorological Centres (WMCs)* (9) Wellingtone
RSMCs Nuclear Emergency Response** (10)
Legend
RSMCs Geographic Specialization (12) RSMCs Non-Nuclear Emergency Response** (3)
World Meteorological
RSMCs (NRT***) Lead Centre Centres (WMCs)* (9)of Wave Forecast (1)
for Coordination RSMCs
RSMCsNuclear Emergency
Sand and Response**
Duststorm (10)(2)
Forecasts
RSMCs Geographic
RSMCs (NRT***) Specialization
Lead Centre (12)
for Coordination of EPS Verification (1) RSMCs Non-Nuclear Emergency Response** (3)
RSMCs Nowcasting (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 Severe
RSMCsWeather
Tropical Forecasting (5)
Cyclone Forecasting (6) RSMCsLimited
RSMCs GlobalArea
Deterministic NWP
Deterministic NWP(8)
(6)
RSMCs Severe
RSMCsWeather Forecasting
Severe Weather (24) (5)
Forecasting ICAO designated
RSMCs VolcanicNWP
Global Deterministic Ash 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 c) Long-Range
World Global CentresForecasts.
are also Global Producing Centres for a) Deterministic Numerical Weather Prediction, b) Ensemble Numerical Weather
** RSMC for nuclear and
Prediction, andc)non-nuclear emergency response have Atmospheric Transport and Dispersion Modelling (ATDM) capabilities.
Long-Range Forecasts.
*** NRT stands for Non-Real-Time
** RSMC for nuclear and non-nuclear emergency response have Atmospheric Transport and Dispersion Modelling (ATDM) capabilities.
*** NRT stands for Non-Real-Time
DESIGNATIONS USED
DESIGNATIONS USED
The depiction and use of boundaries, 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 website are not warranted to be error free nor do they necessarily impy official endorsement or acceptance by the WMO.
on this website are not warranted to be error free nor do they necessarily impy official endorsement or acceptance by the WMO.
Figure 2. WMO-designated GDPFS centres (nowcasting and weather forecasting, up to 30 days)
Source: WMO (2019)
9De Bilt
Offenbach
Montreal
ECMWF
Moscow
Exeter
Seoul
Algier
Toulouse
De Bilt
Tunis
Offenbach Tokyio
Montreal Casablanca
Washington ECMWF
Moscow
Barcelona
Exeter Tripoli Cairo
Seoul
Algier
Toulouse
Bridgetown Tunis
Pune Tokyio Beijing
Casablanca
Washington
Barcelona Tripoli Cairo
Nairobi
Niamey
Guayaquil
Bridgetown Pune Beijing
Brasilia
Nairobi
CPTEC Niamey
Guayaquil
Brasilia
Buenos Aires Pretoria
CPTEC Melbourne
Buenos Aires Pretoria
Legend
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)
World
RSMCs Meteorological
(NRT***) Centres
Lead Centre (WMCs)*
for (9)
Coordination of LRFMME**** (2) RCC -GPC
Networks Regional Climate
for ADCP*** (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 stands
* World for Non-Real-Time.
Global Centres are also Global Producing Centres for a) Deterministic Numerical Weather Prediction, b) Ensemble Numerical Weather
*** ADCP stands
Prediction, forc)Annual
and to Decadal
Long-Range Climate Prediction
Forecasts.
** NRT stands
**** LRFMME for Non-Real-Time.
stands 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 depiction and use
DESIGNATIONS of boundaries, geographic names and related data shown on maps and included in lists, tables, documents, and database
USED
on this website
The areand
depiction notuse
warranted to begeographic
of boundaries, error free names
nor doand
they necessarily
related impy
data shown onofficial
maps andendorsement or acceptance
included in lists, by the and
tables, documents, WMO.database
on this website are not warranted to be error free nor do they necessarily impy official endorsement or acceptance by the WMO.
Figure 3. WMO-designated GDPFS centres (long range and climate forecasting, over 30 days)
Source: WMO (2019)
102.3 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).
113. 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
12remote-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.
13International 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
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