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Monitoring trends in ensemble forecast performance focusing on surface variables and high‐impact events
Author(s) -
Ben Bouallègue Zied,
Magnusson Linus,
Haiden Thomas,
Richardson David S.
Publication year - 2019
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3523
Subject(s) - brier score , quantitative precipitation forecast , context (archaeology) , headline , representativeness heuristic , event (particle physics) , computer science , forecast verification , meteorology , forecast skill , environmental science , precipitation , econometrics , climatology , statistics , mathematics , machine learning , geography , geology , linguistics , philosophy , physics , archaeology , quantum mechanics
This paper discusses methodological aspects of a monitoring process which focuses simultaneously on ensemble forecasts, surface variables, and high‐impact events. Which score(s) is (are) suitable for this task is a central question but not the only one to be answered. Here, we investigate the properties of the Brier score, logarithmic score, and diagonal elementary score in the context of forecast performance monitoring as well as the impact of methodological choices such as the event threshold definition, the reference forecast, and the role assigned to representativeness errors. A consistent picture of the verification process is eventually drawn where the design of the event climatology plays a key role. This study is illustrated by verification results for three surface variables (24 hr precipitation, 10 m wind speed, and 2 m temperature) over 15 years of operational ECMWF ensemble forecasting activities. Results are also compared with a current ECMWF headline score: the relative operating characteristic skill score for the Extreme Forecast Index.