Analysis and Assimilation of Rainfall from Blended SSM/I, TRMM and Geostationary Satellite Data

A technique to statistically blend low-Earth orbiting passive microwave satellite data together with geostationary orbiting infrared satellite data in a near real-time fashion is described and presented. This blended geostationary-microwave technique is oriented towards rapid-update operational usage in quantitative precipitation forecasting and numerical weather prediction models. The technique relies upon near real-time sources of microwave-based rain rate estimates from the Special Sensor Microwave/Imagers (SSM/I) and the Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI). A dynamic adjustment of the infrared (IR) data based on intercalibration to the time and space-coincident microwave-based rain rates is initiated upon arrival of new microwave data. The combined geostationary coverage from GOES, Meteosat and GMS allows for a spatially complete global rainfall analysis.

Examples of rain accumulations are shown for a series of tropical storms including Hurricane Mitch in late October 1998. To examine the model impact of assimilating a global rainfall analysis, a physical initialization of the Navy Operational Global Atmospheric Prediction System (NOGAPS) global spectral model was performed for a period of time in late September 1998, during the presence of Hurricane Georges in tropical Atlantic Ocean. Mean 24, 36 and 48-hour position error improvements ranged from 13 to 16 percent. These results demonstrate the capability of a diurnally sampled rain rate analysis in making positive impacts on forecasting of tropical cyclone positions.