Calibrating daily 2 m maximum and minimum air temperature forecasts in the ensemble prediction system

BP neural network-Self Memory method (BP-SM) is used to calibrate daily 2 m maximum (minimum) air temperature forecasts at 512 stations in China with the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) in March 2016. Seven statistical characteristics used as predictors are calculated based on 2 m air temperature model output of EPS. Daily maximum (minimum) air temperature forecasts by BP-SM, BP and direct model output (DMO) are compared. The post-processing with BP-SM is shown to improve the forecast accuracy. Compared with BP method, more advantages of BP-SM method are attained in longer predictable time. The accurate rate of daily maximum (minimum) air temperature forecasts with absolute errors less than 2℃ reaches above 60% and even over 90% at some stations. Compared with DMO, the forecasting skill score of BP-SM is 30% on average, and above 60% over the eastern Tibetan Plateau. This program is obviously superior with forecast errors within 2℃(1℃). The calibrated daily 2 m maximum air temperature is slightly better than the daily 2 m minimum air temperature. By BP-SM method, the systematic deficiencies of daily 2 m maximum (minimum) air temperature forecasts are significantly reduced.