Understanding Weather and Climate Dynamics Using High-Resolution Global Cloud-Resolving Models
Accurate weather and climate forecasts are essential for managing the growing threats posed by extreme weather events and for planning the transition to renewable energy, yet current global weather models remain too coarse to capture the fine-scale physics of clouds and atmospheric turbulence that drive these phenomena. This project, financed by NERC (National Environmental Research Council, UK Research and Innovation) and complementing project CBF-2025-I-1154 financed by Mexico’s SECIHTI, brings together three complementary international research groups — EPCC at the University of Edinburgh (UK), the Technical University of Denmark (DTU), and CICESE in Mexico — to push the resolution boundaries of global cloud-resolving models (GCRMs), specifically the MPAS-A framework developed by the US National Centre for Atmospheric Research, into scales of relevance in the gray zone (100–1000 m). By harnessing the latest high-performance computing infrastructure, including ARCHER2 — the UK’s national supercomputer — and GPU-accelerated codes, the team is developing improved mesh-generation methods, advanced three-dimensional planetary boundary-layer (3D-PBL) schemes, and data-assimilation techniques that allow the model to better represent clouds and atmospheric turbulence at scales previously out of reach for global simulations. The approach includes systematic benchmarking of MPAS-A across grid spacings ranging from 60 km down to 0.5 km, alongside realistic case studies covering extreme weather events such as winter storms over California and Baja California, wind energy applications in the North Sea, and high-resolution simulations over the Gulf of Mexico. The expected outcomes — including peer-reviewed publications, openly licensed code in public repositories, and an online workshop for the wider research community — will advance scientific understanding of atmospheric dynamics in the gray zone and deliver improved tools for forecasting extreme weather hazards and assessing wind energy resources, with direct consequences for human safety and the planning of clean energy systems. By building a lasting three-way international collaboration and openly sharing knowledge and code following the FAIR principles, the project lays the groundwork for a new generation of exascale climate modelling capabilities that will benefit the global scientific community.
UN Sustainable Development Goals addressed: SDG 7 (Affordable and Clean Energy) · SDG 13 (Climate Action) · SDG 9 (Industry, Innovation and Infrastructure) · SDG 17 (Partnerships for the Goals)
Project team:
- Neil Chue Hong (Project Lead) — EPCC, University of Edinburgh, UK
- Evgenij Belikov (Researcher Co-lead) — EPCC, University of Edinburgh, UK
- Alfredo Peña — DTU Wind and Energy Systems, Technical University of Denmark
- Marc Imberger — DTU Wind and Energy Systems, Technical University of Denmark
- Vanesa Magar — Physical Oceanography Department, CICESE, Mexico
- Diego Canul — Universidad Autónoma de Campeche, México
Related project: CBF-2025-I-1154 — SECIHTI, Mexico (complementary project led by CICESE, financed by Mexico’s SECIHTI)