FY-3A MWHS Data Quality Control

The data of FY-3A MWHS has high spatial resolution,and after assimilation the accuracy of the numerical forecast can be effectively improved.But the accuracy of analysis would be brought down without quality control before assimilation.In order to study and selected the outliers data of FY-3A MWHS data,the PCA(Principal Component Analysis) methods has been applied in a rainfall storm case on August 17 in 2010.The results indicated that there were 148 outliers data identified and it accounted for 19.3% of the total by the PCA method in the three-day information.It accounted for 5.5% in Channel 3,8.2% in channel 4,and 5.4% in channel 5,respectively.The distribution of MWHS data got much closer to data center and the variance contribution of EOF(Empirical Orthogonal Function) became much smoother after all the outliers data had been removed,which demonstrated the PCA was an effective quality control methods to reduce the influence on the average value by the outliers data.