基于EMOS的多种集合预报后处理方法对比研究OA

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.