A 3-D Cloud Detection Method for FY-4A GIIRS and Its Application in Operational Numerical Weather Prediction System
Xusheng Yan, Yaodeng Chen, Gang Ma, Luyao Qin, Peng Zhang, Xinya Gong
Abstract
As the first hyperspectral infrared sounder onboard a geostationary satellite, FengYun 4A (FY-4A) Geostationary Interferometric Infrared Sounder (GIIRS) plays an important role in high-impact weather forecasting with a high spatial and temporal resolution. Because hyperspectral infrared data are highly sensitive to clouds and these cloud-affected data are currently challenging to assimilate, cloud detection is a necessary step to assimilate hyperspectral infrared data better. FY-4A GIIRS has been operationally assimilated in the Chinese operational numerical weather prediction system CMA-GFS (China Meteorological Administration Global Forecast System), which uses an imager-assisted cloud detection method to detect the clear-sky field of view. This paper develops a new cloud detection method to detect clear channels for FY-4A GIIRS in CMA-GFS, extending the original method from two-dimensional to three-dimensional (3D) space. Compared to the original method, the 3D cloud detection method increases the number of observations to two to three times for the middle- and lower-tropospheric channels and three to four times for the upper-tropospheric channels. The additional observations are of the same quality as those retained by the original cloud detection method, and the averaged Observation minus Background (OMB) biases after bias correction for upper-tropospheric channels decreased 54% compared to the original. Experiments results indicate that this method successfully achieves the cloud detection in 3D for GIIRS data and shows the potential in improving data utilization. Also, the results show that 3D cloud detection has a slightly positive impact on temperature and wind forecasts.