Assessing the value of clustering convection‐permitting ensemble forecasts

Gainford, A. ORCID: https://orcid.org/0000-0003-2484-8316, Frame, T. ORCID: https://orcid.org/0000-0001-6542-2173, Gray, S. ORCID: https://orcid.org/0000-0001-8658-362X, Neal, R. ORCID: https://orcid.org/0000-0003-2678-6016, Porson, A. N. and Milan, M. (2025) Assessing the value of clustering convection‐permitting ensemble forecasts. Meteorological Applications, 32 (6). e70139. ISSN 1350-4827 doi: 10.1002/met.70139

Abstract/Summary

Ensembles provide a wealth of information to aid forecasters in their day‐to‐day operations, but with increasing ensemble size and complexity, there is rarely time to fully interrogate their outputs. Clustering ensemble members into distinct scenarios based on the co‐location of hazardous weather features has previously shown promise when applied to global ensemble outputs. However, it is currently unclear whether further value can be gained when applying clustering to convection‐permitting ensemble (CPE) outputs. This study compares precipitation clusters between the operational MOGREPS‐G driving ensemble and the nested MOGREPS‐UK CPE run at the (UK) Met Office during summer 2023. When applied over the UK domain, CPE clustering does not provide clear value compared to global ensemble clustering. Instead, clusters become increasingly similar with leadtime, strongly indicating that CPE clusters are most sensitive to the synoptic forcing common between the two ensembles and that the presence of convective‐scale detail has little influence. However, when focussed on a region impacted by hazardous convection, CPE clustering identified distinct precipitation scenarios and provided improved probabilistic value compared to driving‐ensemble clustering. Finally, by comparing clusters with radar observations, it is demonstrated that the fraction of members supporting a particular scenario is a reliable quantitative prediction of the probability that the given scenario will be the most accurate. We recommend that global ensemble clustering is sufficient over larger domains, while CPE clustering is most useful when applied at regional scales.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/127503
Identification Number/DOI	10.1002/met.70139
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Wiley
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