Impacts of a New Bias Estimate and a New Cloud Detection Algorithm on CrIS Data Assimilation

The Cross-track Infrared Sounder (CrIS) on board Suomi National Polar-Orbiting Partnership (SNPP) satellite provide data for profiling atmospheric temperature and water vapor and supporting continuing advances in data assimilation and NWP modeling. The CrIS radiances were well calibrated and their SDR data had reached a validated level since December 18, 2013 for user applications. This study will present the assimilation of CrIS data in the Gridpoint Statistical Interpolation (GSI) system and the impacts from uses of a new bias estimate and a new cloud detection algorithm on the analysis and model forecasts. The bias estimate employed two months CrIS data in clear-sky conditions, which are determined by collocated VIIRS cloud mask. Differences of the CrIS biases between the new estimate and those incorporated in the current GSI system will be discussed. The new cloud detection algorithm is developed by properly pairing longwave CO2 channels with shortwave CO2 channels for detecting the clouds at different altitudes. Results of the cloud detection using the new algorithm are compared with those obtained from the cloud detection currently in the GSI system in different cloud regimes. Impacts of these modifications on quantitative precipitation forecasts and hurricane and typhoon track and intensity forecasts are demonstrated by a few case studies.