Refinement of CrIS Channel Selection for Global Data Assimilation and Its Impact on the Global Weather Forecast

To maximize the contribution of the Cross-track Infrared Sounder (CrIS) measurements to the global weather forecasting, we attempt to choose the CrIS channels to be assimilated in the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). From preselected 431 CrIS channels, 207 channels are newly selected using a one-dimensional variational (1D-Var) approach where the channel score index (CSI) is used as a figure of merit. Newly selected 207 channels comprise 85 temperature, 49 water vapor, and 73 surface channels, respectively. In addition, to examine how the channels are selected if the forecast error covariance is differently defined depending on the latitudinal regions (i.e., Northern and Southern Hemispheres, and tropics), the same selection process is carried out repeatedly using three regional forecast error covariances. From three regional channel sets, two-channel sets are made for the global data assimilation. One channel set is made with 134 channels overlapped between three regional channel sets. Another channel set consists of 277 channels that is the sum of 3 regional channel sets. In the global trial experiments, the global CrIS 207 channels have a significant positive forecast impact in terms of the improvement of GFS global forecasting, as compared with the forecasts with the operational 100 channels as well as the overlapped 134 and the union 277 channel sets. The improved forecast is mainly due to the additional temperature/water vapor channels of the global CrIS 207 channels that are selected optimally based on the global forecast error of operational GFS. Significance Statement The Cross-track Infrared Sounder (CrIS), with a total of 2211 channels, provides spectral measurements including unique information on the vertical structure of temperature and moisture as well as the atmospheric trace gases. However, assimilating all CrIS channels is practically difficult due to the huge computation cost and data volume. For this reason, we attempted to select the optimal CrIS channels via the physical selection method that takes into account the forecast/observation error characteristics in the numerical model where the CrIS measurements are practically assimilated. In the trial experiment, the newly selected CrIS channels have a positive forecast impact, highlighting that it is important to choose the channels physically by considering the relevant physical characteristics.