The Impact of Assimilation of GPM Microwave Imager Clear-Sky Radiance on Numerical Simulations of Hurricanes Joaquin (2015) and Matthew (2016) with the HWRF model

[摘要] The impact of assimilating Global Precipitation Mission (GPM) Microwave Imager (GMI) clear-sky radiance on the track and intensity forecasts of two Atlantic hurricanes during the 2015 and 2016 hurricane seasons is assessed using the NOAA operational Hurricane Weather Research and Forecasting (HWRF) model. The GMI cloud-cleared brightness temperature is assimilated using a Gridpoint Statistical Interpolation (GSI)-based hybrid ensemble-variational data assimilation system, which utilizes the Community Radiative Transfer Model (CRTM) as a forward operator for satellite sensors. A two-step bias correction approach, which combines a linear regression procedure and variational bias correction, is used to remove most of the systematic biases prior to data assimilation. Forecast results show that assimilating GMI clear-sky radiance has positive impacts on both track and intensity forecasts, with the extent depending on the phase of hurricane evolution. Forecast verifications against dropsondings and reanalysis data show that assimilating GMI clear-sky radiance, when it does not overlap with overpasses of other microwave imagers, can lead to improved forecasts of both thermodynamic (e.g., temperature and specific humidity) and dynamic variables (geopotential height and wind field), which in turn lead to better track forecasts and a more realistic hurricane inner-core structure. Even when other microwave imagers are present (e.g., AMSU-A, ATMS, MHS, etc.), the assimilation of GMI still reduces temperature forecast errors in the near-hurricane environment, which has a significant impact on the intensity forecast.

[发布日期] 2018-10-24

[学科分类] 大气科学

[关键词] ASSIMILATION;GPM SATELLITE CONSTELLATION;MICROWAVE IMAGERY;RADIANCE;FORECASTING;BIAS;CORRECTION;HURRICANES;MATHEMATICAL MODELS;CLIMATE MODELS