Combining Satellite Microwave Radiometer and Radar Observations to Estimate Atmospheric Latent Heating Profiles

In this study, satellite passive microwave sensor observations from the TRMM Microwave Imager (TMI) are utilized to make estimates of latent + eddy sensible heating rates (Q1-QR) in regions of precipitation. The TMI heating algorithm (TRAIN) is calibrated, or "trained" using relatively accurate estimates of heating based upon spaceborne Precipitation Radar (PR) observations collocated with the TMI observations over a one-month period. The heating estimation technique is based upon a previously described Bayesian methodology, but with improvements in supporting cloud-resolving model simulations, an adjustment of precipitation echo tops to compensate for model biases, and a separate scaling of convective and stratiform heating components that leads to an approximate balance between estimated vertically-integrated condensation and surface precipitation. Estimates of Q1-QR from TMI compare favorably with the PR training estimates and show only modest sensitivity to the cloud-resolving model simulations of heating used to construct the training data. Moreover, the net condensation in the corresponding annual mean satellite latent heating profile is within a few percent of the annual mean surface precipitation rate over the tropical and subtropical oceans where the algorithm is applied. Comparisons of Q1 produced by combining TMI Q1-QR with independently derived estimates of QR show reasonable agreement with rawinsonde-based analyses of Q1 from two field campaigns, although the satellite estimates exhibit heating profile structure with sharper and more intense heating peaks than the rawinsonde estimates. 2