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Sequential aggregation of probabilistic forecasts—Application to wind speed ensemble forecasts
Author(s) -
Zamo Michaël,
Bel Liliane,
Mestre Olivier
Publication year - 2021
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12455
Subject(s) - probabilistic logic , probabilistic forecasting , consensus forecast , computer science , numerical weather prediction , set (abstract data type) , forecast skill , ensemble forecasting , probability distribution , forecast verification , machine learning , econometrics , forecast error , artificial intelligence , meteorology , mathematics , statistics , physics , programming language
Abstract In numerical weather prediction (NWP), the uncertainty about the future state of the atmosphere is described by a set of forecasts (called an ensemble). All ensembles have deficiencies that can be corrected via statistical post‐processing methods. Several ensembles, based on different NWP models, exist and may be corrected using different statistical methods. These raw or post‐processed ensembles can thus be combined. The theory of prediction with expert advice allows us to build combination algorithms with theoretical guarantees on the forecast performance. We adapt this theory to the case of probabilistic forecasts issued as stepwise cumulative distribution functions, computed from raw and post‐processed ensembles. The theory is applied to combine wind speed ensemble forecasts. The second goal of this study is to explore the use of two forecast performance criteria: the continuous ranked probability score (CRPS) and the Jolliffe–Primo test. The usual way to build skilful probabilistic forecasts is to minimize the CRPS. Minimizing the CRPS may not produce reliable forecasts according to the Jolliffe–Primo test. The Jolliffe–Primo test generally selects reliable forecasts, but could lead to issuing suboptimal forecasts in terms of CRPS. We propose to use both criteria to achieve reliable and skilful probabilistic forecasts.