Contribution of Expert Aggregation to Temperature Prediction. Part I: Comparison of Different Strategies

Année de publication

2026

Many numerical weather prediction models and their associated postprocessed models are available. Combining all of these predictions in an optimal way is, however, not straightforward. This can be achieved thanks to expert aggregation (EA) which has many advantages, such as being online, being adaptive to model changes, and having theoretical guarantees. In this paper, we propose a method for making deterministic temperature predictions with EA. We used exponentially weighted average, MLprod and MLpol, and Bernstein online aggregation. Hence, we combine and outperform the forecasts of the raw and postprocessed Integrated Forecasting System (IFS), forecasts of Applications of Research to Operations at Mesoscale (AROME), Action de Recherche Petite Echelle Grande Echelle (ARPEGE), and quantiles of the postprocessed Prévision d'Ensemble ARPEGE (PEARP). We also compare the different EA strategies in various settings and show that they outperform the National Blend of Models. Finally, we discuss certain limitations. Significance Statement There are many different weather forecasting models nowadays, so it can be difficult to choose a prediction. The aim of this study is to combine several temperature forecasts in an optimal way. To achieve this, we use a framework known as "expert aggregation" and compare various algorithms. Our results show that this framework can help to improve the temperature predictions on average. However, the challenge of efficiently utilizing experts that perform poorly on average but may be accurate at specific times remains.</div>

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