Improving Prediction and Monitoring of High Impact Landfall Tropical Cyclones over the US and Asia through Satellite Data Assimilation

Satellite microwave and infrared observations from various passive remote-sensing instruments on board Polar-orbiting Operational Environmental Satellites (POES) and Geostationary Operational Environmental Satellites (GOES) provide rich information of the four-dimensional thermal structures of tropical cyclones and their environments. POES microwave instruments provide global all weather observations and GOES imager instruments provide temporally continuous, horizontally dense observations at visible and infrared channels. Together, the POES and GOES data are unique for capturing fast evolving weather systems such as tropical cyclones and their environmental atmospheric states. New advances in satellite observing technologies and calibration algorithms, signified by the Advanced Microwave Sounding Technology (ATMS), the Cross-track Infrared Sounder (CrIS), and the Advanced Himawari Imager (AHI), bring new opportunities and challenges to satellite data assimilation for the prediction and monitoring of high impact landfalling tropical cyclones over the US and Asia. ATMS combines previously separate temperature and humidity sounders into a single instrument, providing automatically collocated microwave temperature and humidity sounding data of all channels. A consistent resolution among longwave infrared (LWIR), middlewave infrared (MWIR) and shortwave infrared (SWIR) bands of the CrIS full spectral resolution data allows for an improved quality control for infrared radiance assimilation, such as detection of optically thin clouds at different altitudes based on the differential absorption and scattering properties of a series of paired LWIR and SWIR channels (same weighting function peak) located in the LWIR and SWIR CO2 bands. Compared with prior GOES imagers, Advanced Himawari Imager (AHI) provides six more infrared sounding channels for the first time. This study presents results of assimilating ATMS, CrIS and AHI radiance data using the Hurricane Weather Research and Forecasting (HWRF) system in which the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) system and the Community Radiative Transfer Model (CRTM) are incorporated. Impacts of new updates to bias correction (BC), quality control (QC), cloud detection and observation error estimate on satellite data assimilation for improved hurricane forecasts confirm an extreme importance of a quality assurance of satellite data. Future developments required for maximizing further the benefits of satellite data to NWP through assimilation are discussed.