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Experimental Precipitation Subseasonal Forecast Historical Skill

Subseasonal skill score based on the historical performance of each model and their multi-model ensemble.

The different skill scores are mapped by calendar month. The forecasts lead times are combined over the weeks 2-3 and the weeks 3-4 from the forecast start time (i.e., 14-day long periods respectively 8 to 21 days and 15 to 28 days after the forecast is issued). Forecasts skill scores combine start times by calendar month and across years 1999 to 2010.

The probabilistic forecasts shown here are obtained from the statistical calibration of three models from the Subseasonal to Seasonal (S2S) Prediction Project database (Vitart et al, 2017) which are combined with equal weight to form multi-model ensemble precipitation tercile probabilities forecasts. Individual model forecasts are calibrated separately for each point, start and lead using Extended Logistic Regressions (ELR; Vigaud et al, 2017) based on the historical performance of each model, and thus provide reliable intra-seasonal climate information in regards to a wide range of climate risk of concerns to the decision making communities and for which subseasonal forecasts are particularly well suited.

These skill scores diagnostics maps give a sense of where and when (issued which months of the year and for which weekly lead times) subseasonal forecasts may have the potential to provide useful information.

The actual forecasts, of which these skill scores are measuring the historical performance, are to be found in the Experimental Precipitation Subseasonal Forecast Maproom.

Skill scores definitions:


Dataset Documentation

Forecasts Skill Scores: Global 1˚ Multi-Model Ensemble forecasts skill scores per month of the year over the period 1999-2010 available here.



Contact with any technical questions or problems with this Map Room.