Improving subseasonal precipitation forecasts through a statistical-dynamical approach : application to the southwest tropical Pacific

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 Meteo-France and ECMWF reforecasts in the Subseasonal-to-seasonal (S2S) database. The large-scale predictors used for bridging are climate indices related to El Nino 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 Meteo-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.