Improving subseasonal precipitation forecasts through a statistical–dynamical approach : application to the southwest tropical Pacific
Résumé
Subseasonal forecasts are based on coupled general circulation models that often have a good representation of large-scale climate drivers affecting rainfall. Yet, they have more difficulty in providing accurate precipitation forecasts. This study proposes a statistical-dynamical post-processing scheme based on a bayesian framework to improve the quality of subseasonal forecasts of weekly precipitation. The method takes advantage of dynamically-forecast precipitation (calibration) and large-scale climate features (bridging) to enhance forecast skill through a statistical model. It is applied to the austral summer precipitation reforecasts in the southwest tropical Pacific, using the Météo-France and ECMWF reforecasts in the Subseasonal-to-seasonal (S2S) database. The large-scale predictors used for bridging are climate indices related to El Niño Southern Oscillation and the Madden–Julian Oscillation, that are the major sources of predictability in the area. Skill is assessed with a Mean Square Skill Score for deterministic forecasts, while probabilistic forecasts of heavy rainfall spells are evaluated in terms of discrimination (ROC skill score) and reliability. This bayesian method leads to a significant improvement of all metrics used to assess probabilistic forecasts at all lead times (from week 1 to week 4). In the case of the Météo-France S2S system, it also leads to strong error reduction. Further investigation shows that the calibration part of the method, using forecast precipitation as a predictor, is necessary to achieve any improvement. The bridging part, and particularly the ENSO-related information, also provides additional discrimination skill, while the MJO-related information is not really useful beyond week 2 over the region of interest.
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