Impact of Assimilating the CrIS Thermodynamic Information in Cloudy Skies on Hurricane Forecasts

Hyperspectral infrared (IR) sounders have high vertical resolution atmospheric profile information, which improves the forecast skills in numerical weather prediction (NWP). However, IR sounder observations are mostly affected by clouds, usually clear radiances (not affected by clouds) from IR sounders are assimilated. The cloud-clearing (CC) technique, which removes the cloud effects from an IR cloudy field-of-view (FOV) and derives the cloud-cleared radiances (CCRs) or clear sky equivalent radiances (CSERs), is an alternative yet effective way to use the thermodynamic information in cloudy skies for assimilation. This study developed a Visible Infrared Imaging Radiometer Suite (VIIRS) based CC method for deriving Cross-track Infrared Sounder (CrIS) CCRs under partially cloudy regions. CrIS radiances and the collocated high resolution VIIRS cloud mask product and its radiances from IR bands are used together to derive the CrIS CCRs. Due to the lack of absorption bands in VIIRS, two important quality control (QC) steps are implemented in the CC process. The validation with VIIRS clear radiances indicates that the CC method can effectively obtain the CrIS CCRs for FOVs with partial cloud covers. To compare the impacts of original CrIS radiances and CCRs, experiments are carried out on Hurricane Joaquin (2015) and Hurricane Matthew (2016) using Gridpoint Statistical Interpolation (GSI) assimilation system, along with Weather Research and Forecasting (WRF) and Hurricane WRF (HWRF) models. At the analysis time, more CrIS observations can be assimilated in the system with CrIS CCRs than with original CrIS radiances, which leads to improved atmospheric fields than original radiances. Similar improved impacts are also observed in the forecast fields. The comparison of temperature and specific humidity with radiosondes indicates the data impacts are growing larger with longer time forecasts. The results of the hurricane track and intensity forecasts show that the assimilation of CrIS CCRs can improve the track forecast through the impacts on the weather system forecasts.