Microwave Sounder Cloud Detection Using a Collocated High-Resolution Imager and Its Impact on Radiance Assimilation in Tropical Cyclone Forecasts

Abstract

ccurate cloud detection is one of the most important factors in satellite data assimilation due to the uncertainties associated with cloud properties and their impacts on satellite-simulated radiances. To enhance the accuracy of cloud detection and improve radiance assimilation for tropical cyclone (TC) forecasts, measurements from the Advanced Microwave Sounding Unit-A (AMSU-A) on board the Aqua satellite and the Advanced Technology Microwave Sounder (ATMS) are collocated with high spatial resolution cloud products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Aqua and the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) satellite. The cloud-screened microwave radiance measurements are assimilated for Hurricane Sandy (2012) and Typhoon Haiyan (2013) forecasts using the Weather Research and Forecasting (WRF) Model and the three-dimensional variational (3DVAR)-based Gridpoint Statistical Interpolation (GSI) data assimilation system. Experiments are carried out to determine the optimal thresholds of cloud fraction (CF) for minimizing track and intensity forecast errors. The results indicate that the use of high spatial resolution cloud products can improve the accuracy of TC forecasts by better eliminating cloud-contaminated microwave sounder field-of-views (FOVs). In conclusion, the combination of advanced microwave sounders and collocated high spatial resolution imagers is able to improve the radiance assimilation and TC forecasts. The methodology used in this study can be applied to process data from other pairs of microwave sounders and imagers on board the same platform.