Accessibility navigation


Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciarán

Charlton-Perez, A. ORCID: https://orcid.org/0000-0001-8179-6220, Dacre, H. ORCID: https://orcid.org/0000-0003-4328-9126, Driscoll, S., Gray, S. ORCID: https://orcid.org/0000-0001-8658-362X, Harvey, B. ORCID: https://orcid.org/0000-0002-6510-8181, Harvey, N. ORCID: https://orcid.org/0000-0003-0973-5794, Hunt, K. ORCID: https://orcid.org/0000-0003-1480-3755, Lee, R. ORCID: https://orcid.org/0000-0002-1946-5559, Swaminathan, R. ORCID: https://orcid.org/0000-0001-5853-2673, Vandaele, R. and Volonté, A. ORCID: https://orcid.org/0000-0003-0278-952X (2024) Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciarán. npj Climate and Atmospheric Science. ISSN 2397-3722 (In Press)

[img] Text - Accepted Version
· Restricted to Repository staff only
· The Copyright of this document has not been checked yet. This may affect its availability.

2MB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Abstract/Summary

There has been huge recent interest in the potential of making operational weather forecasts using machine learning techniques. As they become a part of the weather forecasting toolbox, there is a pressing need to understand how well current machine learning models can simulate high-impact weather events. We compare forecasts of Storm Ciarán, a European windstorm that caused sixteen deaths and extensive damage in Northern Europe, made by machine learning and numerical weather prediction models. The four machine learning models considered (FourCastNet, Pangu-Weather, GraphCast and FourCastNet-v2) produce forecasts that accurately capture the synoptic-scale structure of the cyclone including the position of the cloud head, shape of the warm sector and location of warm conveyor belt jet, and the large-scale dynamical drivers important for the rapid storm development such as the position of the storm relative to the upper-level jet exit. However, their ability to resolve the more detailed structures important for issuing weather warnings is more mixed. All of the machine learning models underestimate the peak amplitude of winds associated with the storm, only some machine learning models resolve the warm core seclusion and none of the machine learning models capture the sharp bent-back warm frontal gradient. Our study shows there is a great deal about the performance and properties of machine learning weather forecasts that can be derived from case studies of high-impact weather events such as Storm Ciarán.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO)
Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:115971
Uncontrolled Keywords:weather forecasting; AI; machine learning; storm Ciarán
Publisher:Nature Publishing Group

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation