Comparison of multiple statistical calibration methods for ensemble forecasting based on the EMOS

?Systematic biases in numerical weather prediction commonly require post-processing correction. EMOS is a post-processing method for ensemble forecasts. In recent years, there have another two other variations of EMOS (gEMOS and SAMOS) proposed to improve EMOS. The purpose of this paper is to evaluate their performance. A comparison has been made for 2m temperature, relative humidity, 10m wind speed, and 3-hour cumulative precipitation in North China by using five numerical models, namely the Global Ensemble Prediction System (GEPS), Global Forecast System (GFS), Regional Ensemble Prediction System (REPS), and two Mesoscale weather numerical forecasts (MESO) with different spatial resolutions from the China Meteorological Administration (CMA). Results show that all three post-processing methods reduce CMA's forecast errors across these variables. Specifically: 1. The EMOS method, which independently calculates parameters for each station, retains the unique characteristics of individual stations, resulting in optimal performance; 2. gEMOS underperforms EMOS due to its neglect of inter-station independence; 3. For variables such as temperature, humidity, and wind speed, which accurately simulate climatological distribution, SAMOS achieves performance comparable to EMOS. For precipitation, SAMOS's performance is constrained by climatological distribution simulations; the forecast error of SAMOS is larger than that of EMOS, but still smaller than that of gEMOS.