A study on longwave infrared channel selection based on estimates of background errors and observation errors in the detection area of FY-4A

Geostationary Interferometric Infrared Sounder (GIIRS) is on board FY-4A, the new generation geostationary weather satellite of China. It is the first high-spectral-resolution advanced infrared sounder on board a geostationary weather satellite in the world. It has 1650 spectral channels to cover long-wave infrared (689 channels) and middle-wave infrared (961 channels) bands. Due to the limitations of computer storage capacity and data transmission and variational assimilation timeliness, it is difficult to assimilate all the channels in the operational assimilation system. On the other hand, the multi-channel satellite information also exhibits certain degrees of spatial correlation and spectral similarity. Therefore, it is necessary to reduce the channel dimension of hyper-spectral data and remove data redundancy in practical application. The subset of channels that play a major role in a particular target is used to provide maximum observation information. For the long-wave channels of GIIRS, an experiment on its channel selection is carried out by combining the weighting function peaks and information entropy. Re-estimates of observation errors and background error covariance in the detection area of FY-4A based on operational Global/Regional Assimilation and Prediction Enhanced System(GRAPES) are also considered. Research results show that it is very important to reasonably choose the channels when retrieving atmospheric temperature from the GIIRS data. On the other hand, the method that combines the weighting function peaks and information entropy is better than the method that only considers a single factor when the number of channels is the same. And the decreases in atmospheric temperature and humidity errors are almost the same when the number of channels obtained from the combination method (40 channels) are smaller than that in the other method (50 channels). This study lays a foundation for the application of GIIRS in the variational assimilation system.