Global satellite millimeter-wave precipitation retrievals trained with a cloud-resolving numerical weather prediction model

This paper develops global retrieval algorithms for surface precipitation rate (mm/h), peak vertical wind (m/s), and water-paths (mm) for rainwater, snow, graupel, cloud water, cloud ice, and the sum of rainwater, snow, and graupel, for the Advanced Microwave Sounding Unit (AMSU) aboard the NOAA-15, -16, and -17 satellites. These retrieved products are expected to be available to researchers and operational users shortly after each orbit is completed, thus providing up to 4-6 precipitation-rate images each day for nearly every spot on earth with nominal 15-km resolution. The same algorithm could be adapted to similar instruments such as AMSU/MHS on the NOAA-18 satellite and AMSU/HSB aboard the Aqua satellite. The algorithm was trained using a numerical weather prediction model (MM5) for 106 globally distributed storms that predicted brightness temperatures consistent with those observed simultaneously by AMSU. Neural networks were trained to retrieve hydrometeor water-paths and 15-minute average surface precipitation rates for rain and snow at ~15-km resolution for land and sea at all viewing angles. Different estimators were trained for land and sea, where surfaces classed as snow or ice were generally excluded from this study. Surface- sensitive channels were incorporated by using linear combinations (principal components) of their brightness temperatures that were observed to be relatively insensitive to the surface, as determined by visual examination of global images of each brightness-temperature-spectrum principal component. Predicted rms errors for retrieved precipitation rates, hydrometeor water paths, and peak vertical wind were evaluated using independent samples of MM5 truth. AMSU and AMSR-E retrieved precipitation rates were also compared.