A Science and Technology Vision for WMO

By Gilbert Brunet et al (The full list of contributors is provided at the end of the article)

Recent decades have recorded increasing negative impacts from extreme weather and climate events due to human-induced climate change and population shifts. These regional and local impacts have global consequences. Weather, climate, water and environmental-related services (herein referred together as environmental services) provided by WMO Members help governments enhance environmental security, mitigate impacts and support economic growth. But as the climate emergency intensifies, policy makers are requiring environmental service providers to adapt their science and services to better address regional, national and local adaptation and mitigation needs. Thus, WMO already anticipates increased demand for accurate and actionable environmental services, for greater focus on impact attribution, and for better integration between disciplines. Now is the time for the Organization to focus further on translating global scientific advances into locally impactful environmental services, especially for middle- to low-income countries, while engaging users and stakeholders, providing training and support, and fostering international cooperation.

Environmental services are reliant on global infrastructure, numerical models, Earth system observations and skilled personnel. Weather-climate prediction systems have significantly improved over the past 50 years due to enhanced observations, data assimilation, understanding of physical processes and model sophistication. However, climate and weather models still struggle with finer-scale processes like cloud formation and cumulus convection, and the microphysics affecting precipitation representations. A leap in computing power would help to better resolve complex systems and provide robust information on high-impact events, tipping points, and potential irreversible changes.

WMO is ideally placed to coordinate the research and development (R&D) and knowledge sharing revolution that is needed to deliver these urgently needed environmental services. Towards that goal, the Organization needs to assess future demands and disruptors of environmental services and existing or emerging capabilities to tackle the challenges ahead. The 2023 World Meteorological Congress approved the WMO Scientific Advisory Panel ‘s Recommendations (Resolution 35 (Cg-19), see box entitled Eight Recommendations of the WMO Scientific Advisory Panel) to initiate the required R&D and knowledge sharing revolution that will deliver and support climate mitigation and adaptation. The R&D components of the recommendations are already being delivered in many aspects by the WMO sponsored and co-sponsored research programmes:

• Global Atmosphere Watch (GAW)
• World Weather Research Programme (WWRP)
• World Climate Research Programme (WCRP).

The WMO Research Board is providing guidance to the effort through multiple working groups and initiatives and contributions from the WMO Commission for Observation, Infrastructure and Information Systems (INFCOM) and WMO  Commission for Weather, Climate, Hydrological, Marine and Related Environmental Services and Applications (SERCOM).

A priority for R&D is the improvement of Numerical Earth system Weather-to-Climate Prediction (NEWP) systems and kilometre-scale (km-scale) global information and insights. The digital revolution, a surge in data availability, the expansion of the private sector and the transition to a net-zero carbon economy are set to propel the development of environmental services. The adoption of supercomputing, machine learning and artificial intelligence (AI) will play a crucial role in strengthening environmental services value chains, leading to more accurate predictions, enhanced data analysis, and improved decision-making. These advancements will enable a more integrated and effective approach to environmental services, unlocking new opportunities for innovation and sustainability. Higher spatial and temporal resolution observations, improved understanding, and modelling of the Earth system across weather and climate timescales, and high capability supercomputing are needed for improved NEWP systems. R&D of affordable, non-conventional observations and data assimilation systems should be pursued.

National Meteorological and Hydrological Services (NMHSs) involved in this innovation cycle will need to make strategic decisions in the coming years. As technologies continue to evolve and merge, and new business practices emerge rapidly, some NMHSs may find it challenging to keep pace (see Figure 1).

The innovation cycle involves a collaborative effort between the public, private and academic sectors, as highlighted by Brunet et al. (2021). The operations component focuses on creating forecasts and other products, while the service component is dedicated to delivering these products to stakeholders and customers. It is important to note that each sector can contribute to various aspects of this cycle, including R&D, operations and service. 

Within the above context, this paper shall explore the scientific and technical challenges of the future by considering the following two questions in the subsequent sections:

• What do we see as the future demands and disruptors for environmental services?
• What are the existing and emerging capabilities that can help meet these future demands equitably?

Figure 1. Innovation Cycle

Future demands and disruptors

1. Demand for greater exactitude and local detail on the impacts of weather, climate, water, and environmental-related events in the context of a changing climate

Users demand improved accuracy and relevance from environmental services. They no longer wish to simply listen to a weather forecast or climate scenario, they are interested in the possible impacts of that event on their physical, social and economic well-being and the environment. Their requirement for increased accuracy, in terms of increased spatial resolution and reduced uncertainty, and for better understanding of impacts has several important drivers on scientific endeavours in the area of meteorology and climatology.

First, it is essential to improve NEWP system accuracy and quantifying uncertainty together with the knowledge of local vulnerabilities to understand weather and climate impacts. Current climate predictions struggle with convective scale processes that cause storms, floods and, indirectly, droughts and heatwaves because of a deficient water cycle representation. Significant numerical and physics modelling limitations persist when representing precipitation patterns, climate variability modes, and extreme event statistics. Uncertainty in knowledge of Earth system interactions is partly due to insufficient comprehensive observations. This observational and modelling uncertainty limits our understanding of high-impact, low-likelihood events, and climate tipping points. Persistent numerical modelling biases in tropical convection, extra-tropical storm-tracks and rainfall have global consequences, affecting atmospheric circulations and midlatitude weather patterns.

NEWP systems are increasingly using ensembles, but further research is needed for optimization and effective utilization. Challenges include providing skillful probabilistic forecasts and addressing uncertainties. Effective communication and visualization are essential for supporting decision-making. Major international efforts are required to develop high-resolution NEWP systems and their associated services, with focus on Recommendations 1, 2 and 6, ensuring value for all communities.

Secondly, understanding the impacts of weather, climate, water-related and other environmental events requires integrating geophysical and social sciences. Future users and policymakers may prefer holistic environmental services that capture event impacts instead of separate services from weather, climate, water and environment agencies. This integration necessitates collaboration between natural and social scientific disciplines, driving the principle behind Recommendation 6.

Finally, attribution of extreme events to anthropogenic climate change is crucial for the future climate services demanded by policy and decision-makers. As the science advances, WMO needs to provide leadership and guidance as requested by Recommendation 4. Detection and attribution require fine spatial scale. WMO should foster research and promote observational and modelling capabilities globally, particularly in vulnerable regions. Continued development in this area will increase the availability of knowledge and techniques in all countries.

2.  Digital revolution and big data

The dramatic increase in personal device usage has transformed how people receive information, with users expecting customized weather data. For example, in the United Kingdom (UK), the use of digital devices for weather information increased from 37% in 2012 to 76% in 2020. This opens opportunities for service providers and government agencies to deliver official warnings directly to the public. The demand emphasizes the need for quality NEWP output, especially in digital services without human intervention. NMHSs must develop agile strategies, possibly through regional collaboration, to maintain authority amidst a fast-changing market with multiple information providers, including the private sector.

Our science, technology and services communities must manage significantly larger quantities of data due to advances in modelling and observations, driven by demand for localized, comprehensive, accurate and timely information. Unprecedented environmental insight, improved predictions, and bespoke services can be achieved by integrating various data sources like remote sensing, ground-based observations, sensor networks, citizen science and online sources. However, this necessitates major investment in big data systems, data analytics, machine learning, and AI, which could be challenging for middle- to low-income countries. Regional scale collaboration and cloud computing could help facilitate pooling of resources and become crucial enablers.

The rapidly expanding data volumes, in part driven by the implementation of new WMO policies and Global Basic Observing Network (GBON) standards, will significantly challenge NMHS’s abilities to manage data. Geographic Information System (GIS) platforms and Application Programming Interfaces (APIs) can enable users to access data and create their own services, integrating demographic, economic and vulnerability data from other agencies. Novel approaches, skill development and infrastructure investments are required to remove the barriers that hinder the use of global scientific outputs by all NMHS for local benefits. The challenges of big data and digital revolution in service delivery are addressed in Recommendation 3.

3. Global science for local impact

Maximizing the use of weather forecasts, climate predictions and environmental factors (such as pollution) requires new methods to ensure that all countries, particularly middle- to low-income countries, can downscale products for local risk reduction and resilience applications. Equitable global participation in environmental sciences, infrastructure development and service delivery is crucial. Middle- to low-income countries should be active participants, driving the future science agenda and making research and innovation a priority. This challenge is addressed in Recommendation 2.

4. Private sector as an active player and quality assurance of services

The relationship between WMO, the private sector and academia will have to reinforced to resolve weather, climate and air-quality forecasting challenges. WMO should take the lead in quality assurance for data, products and services, and in establishing globally accepted standards. By partnering with the private sector and academia, WMO can develop methodologies for validation and guidance on data use and management for all time scales. WMO should assist in educating users to identify high-quality products that may benefit national and international climate change adaptation policies. Recommendation 5 aims to address this driver.

5. Move towards a zero-carbon global world

The Paris Agreement of the United Nations Framework Convention on Climate Change (UNFCCC) aims to hold global warming below 2 °C and to pursue efforts to limit it to below 1.5 °C, although there is ever more evidence pointing to the latter becoming an unlikely target. Either target requires immediate and sustained reductions of greenhouse gas emissions to reach net-zero carbon emissions by the middle of the 21st century.

The Paris Agreement and net-zero strategies have implications for NMHSs as they are at the forefront of weather and climate science and services. Increasing energy demand for supercomputing may compete with the need for improved NEWP systems, and a more sustainable approach to consumable observational infrastructure, like radiosondes, may be necessary. The WMO Strategic Plan 2024–2027 includes an objective around environmental sustainability, committing to a sustainable, net-zero, resilient world. Recommendation 8 expands on this by addressing computing infrastructure. New collaborations could positively impact diversity and inclusion in WMO operations, and both WMO and NMHSs can become strong voices within the United Nations family and national governments in the transition towards net-zero.

The push for net-zero will create new markets for environmental services. As energy production shifts towards renewables, information on weather, climate, water and the environment will become more valuable for locating and operating wind, solar and hydro power generation sites.

Emerging and existing capabilities to help meet future needs

Exascale computing – The next generation computational technology is crucial for NEWP’s advancement. Current investments in supercomputing for weather, climate and air-quality predictions have not fully unlocked NEWP’s potential. NEWP R&D must balance investments in resolution, complexity, ensemble size and timeliness for optimal results. A coordinated global effort is needed for Earth system modelling and data analysis at the exascale, motivating Recommendation 1.

Machine learning (ML), artificial intelligence (AI) and cloud computing – ML and AI have numerous potential applications in the environmental services value chain, most notably in improving weather forecasts by removing model biases. The European Centre for Medium-Range Weather Forecasts  (ECMWF) has already made revolutionary advances in this area. They are running real-time ML and AI weather predictions and sharing the results. These techniques, which require reforecasting and observational datasets for algorithm training, are already used in operational post-processing systems.

Cloud computing can democratize access to high-powered computing, potentially allowing all countries, including middle- to low-income countries, to utilize top-quality weather and climate information. By managing large datasets in the cloud, data handling issues can be avoided. NEWP model providers and supercomputing vendors could collaborate to integrate multimodels in clusters, making them available as a cloud service. Countries could then affordably access NEWP systems or postprocess global model output for their specific regions without the burden of managing heterogeneous supercomputing and data. This approach is captured in Recommendation 3 and could be the technical implementation for Recommendation 2.

Observation technologies and techniques

High-quality, well-managed ground and space-based observations are crucial for weather, climate, water and environment enterprises. However, the number of such systems has decreased worldwide over the past 20 years due to financial constraints. Vulnerable areas, particularly in middle- to low-income countries, need well-equipped stations for early warning systems and as reference points for complementary systems, private partnerships, climate simulations and weather forecasts. Precipitation is challenging to measure, and reliable observations, with the required spatial density and high spatial-temporal resolution essential for validating models against observations, are lacking.

As km-scale modelling progresses, observation density must increase, particularly with respect to data on the water cycle. While GBON provides the basic surface-based observing network for global NEWP modelling and climate analysis, it lacks the coverage needed for future applications. Complementary systems, including technological advances in low-cost communications and sensors, can help fill these gaps, utilizing the “Internet of Things” and mass-market sensors for environmental monitoring. In addition, under-utilized weather stations can contribute to the overall system if sharing arrangements are agreed upon. In addition, the Systematic Observations Financing Facility (SOFF) supports countries to close the basic observational data gap for environmental services. SOFF assistance prioritizes Least Developed Countries and Small-Island Developing States to accelerate the sustained collection and international exchange of the most essential surface-based weather and climate observations in compliance with GBON.

Collating, storing and quality controlling data from various sources, especially “citizen scientists”, requires developing and sharing tools and expertise. WMO must consider its approach to data quality and metadata standards as complementary data sources become more common. “Nonconventional” observations, like estimating rainfall from attenuation of signals between cell phone towers, and continued research in new technologies may be essential for achieving comprehensive and affordable data coverage. Facilitating the availability and sharing of these observations should occur under the WMO Unified Data Policy.

Complementary observations can enhance spatial and temporal resolution, but the optimal balance of satellite, surface-based and other observations for specific use cases remains a challenge. Forecast sensitivity studies help target investment for maximum benefits. NEWP’s data assimilation systems must accommodate novel observation sources, integrating comprehensive observations with multiscale models. Coupled prediction systems need co-located observations across system interfaces for model development, evaluation and forecast initialization. Global, comprehensive in situ observations, like GAW, with open data access, metadata, quality monitoring and high-resolution are essential for environmental services. These observations are crucial for understanding the Earth system and addressing research, societal and policy-relevant questions. Observations’ importance is explicitly captured within Recommendation 1.

Evolving international frameworks 

International cooperation is a strength of WMO. Global science collaboration is needed to exploit exascale supercomputing and observation opportunities, but such efforts must be accompanied by mechanisms for countries to utilize these advances in their environmental services as stated by Recommendation 2. WMO Regional Associations, NMHSs and local sector-based user institutions should work together to co-design and deliver multidisciplinary research products and to develop local and regional capacity to seamlessly integrated modelling data and downscale information to regional and local needs.

WMO regional centres can collaborate with the Global Framework for Climate Services (GFCS)to ensure that global science translates into local impacts. Investment in infrastructure and skilled personnel is crucial for regional and national institutions, and working across international organizations and structures is essential for delivering all recommendations.

Including all people 

Advancing skills in a gender equal, diverse and inclusive way is a must within the NMHS community and academia, especially in middle- to low-income countries. Impact-based services demand a new approach to education, involving various disciplines like finance, risk management and communication. Training in whole systems thinking and co-designed science is crucial to connect with affected sectors without neglecting traditional disciplines. 

Educating practitioners, researchers and teachers is critical, as is enhancing the understanding of policymakers and decision-makers regarding the value of national services and their benefits and limitations. As NEWP advances lead to more sophisticated data sources, expert interpretation will be necessary for decision and policymakers. Recommendation 7 addresses people development issues.

Conclusion

We have identified drivers, disruptors and enablers impacting environmental services over the next two decades, highlighting scientific, technical and social imperatives. The first three SAP Recommendations are potential game-changers for environmental services in the coming decades. The remaining five complement and enhance the benefits derived from the first three. WMO technical commissions and the Research Board, along the sponsored and co-sponsored research programmes, are actively engaged in investigating related topics identified in the Recommendations. This includes liaising with the Panel on Socioeconomic Benefits to advance the societal impact of WMO science, updating concept notes on exascale computing and data to include an explicit digital strategy, continuing to advance weather and climate simulation and attribution research, and forming a rapid response task team on AI for weather.

These recommendations are critical steps for WMO, its Members, and the international community to address future demands for environmental services in a climate-impacted world. The goal is to provide timely, reliable and relevant information on future climate risks, especially for vulnerable nations. Achieving this requires international collaboration, including the development of km-scale global climate models and dedicated high capability supercomputing and data facilities powered by renewable energy.

It is essential to integrate advances in observing networks, modelling systems and data sharing and to develop seamless data platforms for weather, climate, hydrological and environmental observations. Transforming operational climate data services through cloud-based platforms, data management techniques and close collaboration with the IT private sector is crucial.

To translate climate data into actionable information, interdisciplinary science for impacts and solutions must be developed, integrating geophysical and social sciences. Increased international collaboration should engage and support all nations, building capability and expertise. WMO should develop training strategies to broaden expertise beyond traditional disciplines and foster a new generation of cross-disciplinary researchers and practitioners. The success of these recommendations depends on immediate action to enhance international collaboration and cooperation among entities such as WMO, under WWRP, WCRP and GAW, and initiatives led by various NMHSs, including Destination Earth, ECMWF and the MOMENTUM Partnership.

Authors and Affiliations

• Dr Gilbert Brunet, Chair, WMO SAP, Chief Scientist, Group Executive, Science & Innovation Group, Bureau of Meteorology – Australia
• Prof. Jürg Luterbacher, Centre for International Development and Environmental Research and Department of Geography,
• Justus Liebig University of Giessen, Germany
• Mike Gray, UK Met Office
• Ian Lisk, President of WMO Services Commission, UK Met Office
• Richard Anyah, Department of Natural Resources and the Environment, University of Connecticut
• Prof Detlef Stammer, World Climate Research Programme
• Michel Jean, President of WMO Infrastructure Commission, Emeritus Associate Environment and Climate Change Canada
• Prof Greg Carmichael, University of Iowa
• Prof Markku Kulmala, University of Helsinki, Faculty of Science, Institute for Atmospheric and Earth System Research (INAR) / Physics
• Vladimir Kattsov, Voeikov Main Geophysical Observatory, St. Petersburg, Russian Federation
• Madeleine Renom, Atmospheric Science and Physical Oceanography Department, Physics Institute, University of the Republic Uruguay
• Thomas Stocker, Prof. em. University of Bern, Oeschger Centre for Climate Change Research
• Dr Amanda H. Lynch, Lindemann Distinguished Professor, Institute at Brown for Environment and Society, Department of Earth, Environmental and Planetary Sciences, Brown University, Chair, WMO Research Board
• Prof Stephen Belcher, Chief of Science and Technology, UK Met Office
• Opha Pauline Dube, Department of Environmental Science, University of Botswana
• Christopher A. Davis, Senior Scientist and Deputy Director of Education, Engagement and Early-Career Development, Chair, WWRP Science Steering Committee, NSF National Center for Atmospheric Research, Boulder, Colorado, USA
• Toshio Koike, International Centre for Water Hazard and Risk Management (ICHARM), Public Works Research Institute