Quality control of observations directly affects the analysis quality of numerical prediction data assimilation.Based on the “Gaussian plus flat” distribution model of observation error and Bayes’ probability theorem,this paper reports the development of a variational quality control scheme for the 3D-Var(three-dimensional variational) assimilation and forecast system in GRAPES(Global/Regional Assimilation and Prediction System).It also discusses the initial startup and key parameters of this scheme,and furthermore analyzes and verifies its applicability and effectiveness.A heavy rainfall event in southern China is selected as a case for assimilating and forecasting the analysis using Global Forecast System(GFS) data as the background field and conventional observation data including TEMP,SYNOP,SHIPS,AIREP,SATOB and COSMIC satellite retrieval data.Also,we calculate the rain score(ETS and Bias) of batch tests with 31 days in August 2013.The results show that the “Gaussian plus flat” distribution model is a better match for the characteristics of real observation error than the Gaussian distribution.At the same time,the variational quality control method is able to correct the observation weight in accordance with the size of the observation departure.This also proves the rationality of the non-Gaussian distribution assumption for real observation error and the correctness of variational quality control theory.The variational quality control method reasonably adjusts every observation weight according to different qualities of observation,and virtually classifies the observations.This is beneficial to identifying the quality of observations so that we can assimilate every observation with different weights,as available data,effective data and damaging data.The variational quality control method significantly adjusts the analysis increment field,which includes height,wind and specific humidity,especially in some areas where the damaging data are recognized.Due to the change of the analysis increment field,a larger improvement for the analysis field is made.In view of ameliorating the analysis field,the quality of the forecast field also improves;in particular,a more positive effect on heavy rainfall areas.The forecast quality has been further improved in the intensity and center position of the precipitation,in particular,the prediction of the heavy rainfall,large rainstorms and other large-scale precipitation.The ETS and Bias scores of the batch tests also demonstrate the applicability and effectiveness of the variational quality control procedure in the operational assimilation system.Therefore,the variational quality control method plays an important role in data assimilating and forecasting of mesoscale and microscale severe weather processes.On the other hand,it can not only help improve the quality of forecast and analysis with the variational quality control method,but also can avoid the damages to forecast and analysis of some outlier data which cannot been resolved by traditional quality control procedures in extreme weather processes,such as rainstorms and typhoon.This means that variational quality control does not have a negative impact for analysis when the observations are good,and has a positive impact when abnormal observations occur.