Assimilating High-Resolution All-Sky Infrared Radiances from GOES-R and Himawari-8 for Severe Weather and Tropical Cyclone Prediction

The impacts of assimilating all-sky infrared satellite radiances from new-generation geostationary satellites Himawari-8 and GOES-R (GOES-16), for convection-permitting initialization and prediction of severe weather and tropical cyclones are explored. We combine the Community Radiative Transfer Model (CRTM) to the ensemble Kalman filter (EnKF) data assimilation system developed at Penn State University (PSU) and built around the Weather Research and Forecasting model (WRF). The first case chosen in this study is the genesis and rapid intensification stage of super-typhoon Soudelor (2015), one of the most intense tropical cyclones ever observed by Himawari-8. It is found that the hourly cycling assimilation of the infrared radiance can improve the estimate of the initial tropical cyclone intensity, as well as the spatial distribution of essential convective activities, in particular, within the inner-core region of tropical cyclones. Deterministic forecasts initialized from the EnKF analyses are capable of simulating the rapid intensification of Soudelor, which shows encouraging prospects of future improvement in tropical cyclone prediction through assimilating all-sky infrared radiance from geostationary satellites such as Himawari-8 and GOES-R. The assimilation of the infrared radiance contributes not only to developing moist convections through moisture initialization, but also to better constraining the initial vortex, both of which are essential to accurately capture the genesis and rapid intensification process of tropical cyclones. The second case we will be presenting is the assimilation of GOES-R all-sky radiances for the analysis and prediction of the June 17 2017 severe weather event that occurred over the Great Plains. Our special focus will be the use of GOES-R radiance for better analyzing the convective initiation before the event have any significant reflectivity appeared on standard WSR-88D observing network.