Overland precipitation estimation from TRMM passive microwave observations

Procedures for passive microwave precipitation estimation over land are inv estigated based on a large database of Tropical Rainfall Measuring Mission (TRMM) observations. The procedures include components for rain area deline ation, convective/stratiform (C/S) rain classification, and estimation of v ertically integrated water content or surface rainfall rate. The investigat ed algorithms include neural network schemes for both the rain area and C/S classification and statistical algorithms for precipitation estimation. Th e coincident active and passive microwave observations from TRMM, with the active (TRMM precipitation radar) observations providing the reference valu es for the various precipitation parameters, are used for algorithm calibra tion and validation. The calibration and validation are based on 1 yr of da ta over the continental United States and a repetitive sampling strategy th at make the results statistically significant. Good agreement is demonstrat ed with TRMM precipitation radar observations in rain delineation, and it i s shown that C/S classification can considerably improve precipitation esti mation. It is also shown that better performance may be achieved in estimat ing vertically integrated hydrometeor contents as compared with rainfall ra tes.