Comparison of multiple ensemble forecast post-processing methods based on EMOS

Systematic biases in numerical weather prediction commonly require post-processing correction. Ensemble Model Output Statistics (EMOS) is a post-processing method for ensemble forecasts. In recent years, two other variations of EMOS (gEMOS and SAMOS) have been proposed to improve EMOS. This paper aims to evaluate their performance. A comparative study has been conducted for 2 m temperature, relative humidity, 10 m wind speed, and 3 h cumulative precipitation in North China using five numerical models, i.e., the Global Ensemble Prediction System (GEPS), the Global Forecast System (GFS), the Regional Ensemble Prediction System (REPS), and two mesoscale weather numerical forecasts (MESO-10 km, MESO-3 km) with different spatial resolutions from the China Meteorological Administration (CMA). Results show that all the three post-processing methods can reduce forecast errors of the CMA models 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 performance is comparable to that of EMOS. For precipitation, SAMOS's performance is constrained by climatological precipitation distribution simulation; the forecast error of SAMOS is larger than that of EMOS, yet it is still smaller than that of gEMOS.