Global precipitation retrieval algorithm trained for SSMIS using a numerical weather prediction model: Design and evaluation

Abstract

This paper presents and evaluates a global precipitation retrieval algorithm for the Special Sensor Microwave Imager/Sounder (SSMIS). It is based on those developed earlier for the Advanced Microwave Sounding Unit (AMSU) and employs neural networks trained with 122 global storms that spanned a year and were simulated using the fifth-generation National Center for Atmospheric Research/Penn State Mesoscale Model (MM5) and a radiative transfer program validated using AMSU observations. Only non-icy surfaces at latitudes less than 50° have been analyzed because their surface effects are more predictable. Sensitivity to surface emissivity variations was reduced by using only more surface-insensitive principal components of brightness temperature. Based on MM5 simulations, retrievals for land are slightly less accurate than those for sea and all are useful for rates above 1 mm/h. F-16 SSMIS, NOAA-15 AMSU, and Global Precipitation Climatology Project (GPCP) annual estimates generally agree. SSMIS retrieves less precipitation for some areas partly due to its higher resolution that resolves precipitation better. SSMIS overestimates precipitation over under-vegetated land requiring the near-surface evaporation correction illustrated earlier for AMSU.