Intercomparison of multiple statistical methods in post-processing ensemble precipitation and temperature forecasts

Ensemble weather forecasting generally suffers from bias and under-dispersion, which limit its predictive power. Several post-processing methods have been developed to overcome these limitations, and an intercomparison is needed to understand their performance. Four state-of-the-art methods are compared in post-processing precipitation and air temperature of the Global Ensemble Forecasting System (GEFS) reforecasts using a simple bias correction (BC) method as a reference. These methods include extended logistic regression (ExLR), generator-based post-processing (GPP), Bayesian model averaging (BMA) and affine kernel dressing (AKD). All these methods are tested over 659 national standard meteorological stations in China. The research concerns are the influence of region and forecast date and the role of BC on ensemble weather forecasting. It was found that: (1) the deterministic methods (GPP and ExLR) are more skilful than the probabilistic methods (BMA and AKD) in obtaining the well-calibrated and skilful ensemble forecasts; (2) the forecast skill of the post-processed ensemble weather forecasts is comparably high in the northern arid areas for precipitation, while the forecast skill for air temperature is only low in the Qinghai-Tibetan Plateau area; (3) the skill difference of the post-processed forecasts on different forecast date is only evident for air temperature, while not apparent for precipitation; and (4) only correcting bias for the ensemble weather forecasts can achieve about 0-70% (for precipitation) and 30-100% (for air temperature) forecast skill improvement for deterministic methods.