An improved benchmark for CONUS winter season precipitation forecasts

Matt Switanek

Tuesday, Feb 04, 2020, 2:00 pm
DSRC Room 1D403


Monthly tropical sea surface temperature (SST) data are used as predictors to make statistical forecasts of cold season (November-March) precipitation. Through the use of a “combined-lead sea surface temperature" (CLSST) model, predictive information is discovered not just in recent SSTs but also from SSTs up to 18 months prior. We find that CLSST cold season forecast anomaly correlation skill for precipitation is higher than that of the North American Multi-Model Ensemble (NMME) and the SEAS5 model from the European Centre for Medium- Range Weather Forecasts (ECMWF) when averaged over the US. The forecast skill obtained by CLSST in parts of the Intermountain West is of particular interest because of its implications for water resources. In those regions, CLSST dramatically improves the skill over that of the dynamical model ensembles, which can be attributed to a robust statistical response of precipitation in this region to SST anomalies from the previous year in the tropical Pacific.


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