Contribution of expert aggregation to temperature prediction part i
Many Numerical Weather Prediction models and their associated Post-Processed 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 post-processed Integrated Forecasting System (IFS), forecasts of Application of Research to Operations at Mesoscale (AROME), Action de Recherche Petite Echelle Grande Echelle (ARPEGE) and quantiles of the post processed Pr{é}vision 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.