Ozone ensemble forecast with machine learning algorithms

• Published in: Journal of Geophysical Research Vol. 114; no. D5; pp. D05307 - n/a
• Main Authors: Mallet, Vivien, Stoltz, Gilles, Mauricette, Boris
• Format: Journal Article
• Language: English
• Published: Washington, DC American Geophysical Union 16-03-2009; Blackwell Publishing Ltd
• Subjects: Atmospheric Composition and Structure; Atmospheric Processes; Computer Science; Data assimilation; Earth sciences; Earth, ocean, space; Exact sciences and technology; machine learning; Mathematical Geophysics; Modeling and Simulation; multimodel ensemble; Pollution; Polyphemus; Prediction; sequential aggregation; urban and regional; Europe; Western Europe; machine learning; multimodel ensemble; sequential aggregation; Multimodel; Ensemble simulation; algorithms; parametrization; Linear combination; prediction; Summer; aggregation; concentration; Modeling; performances; ozone; Multimodel ensemble; Sequential aggregation; Machine learning
• ISSN: 0148-0227; 2156-2202
• DOI: 10.1029/2008JD009978

We apply machine learning algorithms to perform sequential aggregation of ozone forecasts. The latter rely on a multimodel ensemble built for ozone forecasting with the modeling system Polyphemus. The ensemble simulations are obtained by changes in the physical parameterizations, the numerical schemes, and the input data to the models. The simulations are carried out for summer 2001 over western Europe in order to forecast ozone daily peaks and ozone hourly concentrations. On the basis of past observations and past model forecasts, the learning algorithms produce a weight for each model. A convex or linear combination of the model forecasts is then formed with these weights. This process is repeated for each round of forecasting and is therefore called sequential aggregation. The aggregated forecasts demonstrate good results; for instance, they always show better performance than the best model in the ensemble and they even compete against the best constant linear combination. In addition, the machine learning algorithms come with theoretical guarantees with respect to their performance, that hold for all possible sequences of observations, even nonstochastic ones. Our study also demonstrates the robustness of the methods. We therefore conclude that these aggregation methods are very relevant for operational forecasts.