Sequential aggregation of probabilistic forecasts-Application to wind speed ensemble forecasts

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.

Automated summary

This study focuses on combining forecasts from different numerical weather prediction models using expert advice theory to improve forecast performance. Additionally, it explores the use of two forecast performance criteria, CRPS and the Jolliffe-Primo test, to achieve reliable and skillful probabilistic forecasts.