Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part I: Improved Method and Uncertainties

William S. Olson, Yansen Wang, and Christine Chiu

Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, Maryland

Christian D. Kummerow

Department of Atmospheric Sciences, Colorado State University, Fort Collins, Colorado

Song Yang

School of Computational Science, George Mason University, Fairfax, Virginia

Grant W. Petty

Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, Madison, Wisconsin

Wei-Kuo Tao, Thomas L. Bell, and Scott A. Braun

Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, Maryland

Stephen E. Lang

Science Systems and Applications, Inc., Lanham, Maryland

Daniel E. Johnson

Goddard Earth Sciences and Technology Center, Greenbelt, Maryland


A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5°-resolution range from approximately 50% at 1 mm h−1 to 20% at 14 mm h−1. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%–80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5° resolution is relatively small (less than 6% at 5 mm day−1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%–35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%–15% at 5 mm day−1, with proportionate reductions in latent heating sampling errors.