The Quality Control And Cloud Detection Of FY-3A Microwave Humidity Sounder Data

The assimilation of meteorological satellite data have been proved to be the main root to improve the numerical weather prediction. The assimilation procedure requires satellite data to be as accurate as possible. So we need to control the quality of data and get rid of those data which would not be simulated well enough. This study focus on the quality control of FY-3A MWHS data, and the ability of quality control procedure to identify those data which are influenced by cloud and precipitation.This study uses Bi-Weighting Quality Control procedure to study the O-B biases of FY-3A MWHS where O represents Observed brightness temperature and B represents RTTOV simulated Brightness temperature with NCEP GFS1°×1°grid background field as its input. The result turns out to be good and the Bi-Weighting Quality Control procedure may be used to control the data before it is used in the assimilation.As it is common to drop the data influenced by cloud and precipitation and only the data derived in clear sky is used in the assimilation in most of the assimilation system around the world, and that the FY-3A can not identify those influenced data from those are not by itself due to its channel settings, this study also focus on the connection between the outliers identified by Bi-Weighting Quality Control procedure and the cloud or precipitation points, and evaluates the possibility of Bi-Weighting Quality Control procedure to identify cloud and precipitation data points. The result show that most of the negative O-B outliers are influenced by cloud and precipitation and Bi-Weighting Quality Control procedure may be a good method to identify cloud and precipitation data points.