Power transformation of variables for post-processing precipitation forecasts: Regionally versus locally optimized parameter values

Abstract

Short-term precipitation forecasts are mainly derived from numerical weather prediction (NWP) models. Raw NWP forecasts typically require post-processing to improve their accuracy and reliability through statistical calibration. For post-processing precipitation forecasts, several well-known calibration models employ power transformation to remove the positive skewness of precipitation. The most common practice is to use a pre-fixed transformation parameter value for both observation and forecast variables. Another approach is to allow the parameter values to differ for the two variables and locally optimize them at a spatial point to ensure the best performance of a calibration model. Howev..