Analogue Ensemble Averaging Method for Bias Correction of 2-m Temperature of the Medium-Range Forecasts in China

Abstract

The 2-m temperature is one of the important meteorological elements, and improving the accuracy of medium- and long-term forecasts of the 2-m temperature is important. The similarity forecasting method is widely used as a calibration technique in the statistical postprocessing of numerical weather prediction (NWP). In this study, the analogue ensemble averaging method is used to correct the deterministic forecast of the 2-m temperature with a forecast lead time from 180 h to 348 h using the CMA-GEPS model. The bias, mean absolute error (MAE), and root mean square error (RMSE) are used as the evaluation metrics. In comparison with NWP, the systematic error of the model for 2-m temperature is effectively reduced during each forecast period when using the analogue ensemble averaging method. In addition, the differences in forecast errors between regions are reduced, and the accuracy of 2-m temperature forecasts over complex terrain, especially in Southwest China, Northwest China, and North China, is improved using this method. In the future, there is certainly potential to apply the analogue ensemble averaging method to the bias correction of medium- and long-term forecasts of more meteorological elements.