An improved surface precipitation rate retrieval algorithm for Advanced Microwave Sounding Unit (AMSU) data is presented. Its reduced sensitivity to surface effects improves its sensitivity to stratiform precipitation and permits it to work over most snow and sea ice. It is trained using 106 globally and seasonally distributed cloud-resolving MM5 storm simulations that predicted well AMSU brightness temperatures observed simultaneously. The improvements result mostly from the choice of frequencies and principal components feeding the final neural network estimators. The retrievals perform well in terms of simulated rms retrieval errors, plausible global precipitation maps, global precipitation frequency statistics in terms of latitude and precipitation rate, and in comparisons with other sensors and algorithms, including nearly coincident CLOUDSAT data.
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