Global weather models are about to get a lot sharper—researchers are testing whether shrinking the grid spacing from several kilometres down to one kilometre or less makes forecasts more accurate. Current global cloud-resolving models (GCRMs) cannot directly simulate the small-scale physics of convection, cloud microphysics, and turbulence that drive weather patterns. These processes fall into a "grey zone" of scales between 100 and 1,000 metres, where existing models rely on approximations. This project asks whether next-generation supercomputers—including exascale machines and GPU accelerators—can run GCRMs at fine enough resolution to resolve those processes explicitly, rather than approximating them. If successful, the work could improve forecasts of extreme weather events such as storms and heatwaves, and help planners site wind farms more accurately by modelling wind patterns over complex terrain and open sea. The project also seeds a collaboration between Danish, Mexican, and UK partners, combining expertise in atmospheric observations, regional forecasting, and high-performance computing. While the research is fundamentally about understanding how weather and climate systems behave at very small scales, that understanding directly feeds into practical tools for renewable energy planning and hazard prediction.
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Global cloud-resolving models (GCRMs) underpin the study of many important application areas, such as wind energy and extreme weather hazards, related to the physics and dynamics of weather regimes. Modern supercomputers have the computational power to run GCRMs at one-kilometre scales. In this project, we investigate whether reducing grid spacings to one kilometre or less increases the model accuracy and whether the dynamics and the physics at those spacings, for example, concerning convection, cloud microphysics, or turbulence schemes, should be reviewed. This will advance knowledge about the physics and dynamics of weather regimes at scales of 100 to 1000 m and use novel model implementations to analyse wind patterns over complex terrains and at sea. A key challenge is understanding whether it is possible to reduce the grid spacings of GCRMs by leveraging the next generation of computing infrastructure, including exascale and GPU accelerators. The project seeds an ongoing collaboration between three international partners with complementary expertise. The Department of Wind and Energy Systems at the Technical University of Denmark (DTU) is a leader in atmospheric flow observations and meteorological models that can be used to study the impact of the atmosphere on wind energy systems. The Physical Oceanography Department at the Center for Scientific Research and Higher Education at Ensenada (CICESE) operates regional meteorological forecasts and weather and climatological databases for the Mexican Northwest and has led the Consortium for Research in the Gulf of Mexico since 2015. EPCC at the University of Edinburgh are leaders in the application of High-Performance Computing to research, co-developed the Met Office NERC Cloud Model (MONC), and host the UK's national supercomputer, ARCHER2. Improving the resolution of GCRMs will play a critical role in advancing our understanding of clouds and their interactions with the broader climate system, by permitting the resolution of convection and turbulence in the "gray zone" of atmospheric scales between 100 and 1000 m at Global and Regional scales for periods longer than one season. This will improve forecasting and modelling, studying extreme weather events, and aid in the planning of renewable energy systems.
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