| Summary: | This paper compares two methods for correcting passive or active microwave surface precipitation estimates based on hydrometeors sensed aloft that may evaporate before landing. These corrections were derived using two years of data from 516 globally distributed rain gauges and passive millimeter-wave Advanced Microwave Sounding Units (AMSU) aboard three NOAA satellites (N15, N16, and N18). The first correction reduces rms differences between rain gauges and AMSU annual precipitation accumulations (mm) by a separate factor for each infrared-based surface classification, while the second correction factor uses a 3-2-1 neural network (NN) trained using both surface classification and annual average relative humidity profiles. Different data were used for training and accuracy evaluation. The NN results agreed with rain gauges better than did surface classification corrections alone. The rms annual accumulation errors relative to the 516 uncorrected rain gauges using AMSU with surface classification and NN corrections were 223 and 209 mm/yr, respectively, compared to 152 mm/yr for GPCP, which incorporates rain gauge data and data from more satellite sensors.
|
|---|