Improving Assimilation of Microwave Radiances in Cloudy Situations with Collocated High Resolution Imager Cloud Mask

Monday, 15 December 2014
Jinlong Li1, Hyojin Han2, Jun Li2, Mitch Goldberg3, Pei Wang2 and Zhenglong Li4, (1)Univ Wisconsin Madison, Madison, WI, United States, (2)University of Wisconsin-Madison, Madison, WI, United States, (3)National Environmental Satellite, Data, and Information Service, JOINT POLAR SATELLITE SYSTEMS OFFICE, Silver Spring, MD, United States, (4)CIMSS/SSEC, Madison, WI, United States
Tropical cyclones (TCs) accompanied with heavy rainfall and strong wind are high impact weather systems, often causing extensive property damage and even fatalities when landed. Better prediction of TCs can lead to substantial reduction of social and economic damage; there are growing interests in the enhanced satellite data assimilation for improving TC forecasts. Accurate cloud detection is one of the most important factors in satellite data assimilation due to the uncertainties of cloud properties and their impacts on satellite observed radiances. To enhance the accuracy of cloud detection and improve the TC forecasting, microwave measurements are collocated with high spatial resolution imager cloud mask. The collocated advanced microwave sounder measurements are assimilated for the hurricane Sandy (2012) and typhoon Haiyan (2013) forecasting using the Weather Research and Forecasting (WRF) model and the 3DVAR-based Gridpoint Statistical Interpolation (GSI) data assimilation system. Experiments will be carried out to determine a cloud cover threshold to distinguish between cloud affected and cloud unaffected footprints. The results indicate that the use of the high spatial resolution imager cloud mask can improve the accuracy of TC forecasts by eliminating cloud contaminated pixels. The methodology used in this study is applicable to advanced microwave sounders and high spatial resolution imagers, such as ATMS/VIIRS onboard NPP and JPSS, and IASI/AVHRR from Metop, for the improved TC track and intensity forecasts.