AbstractSatellite multisensor precipitation products (SMPPs) have a variety of potential uses, but suffer from relatively poor accuracy due to systematic biases and random errors in precipitation occurrence and magnitude. We use the Censored Shifted Gamma Distribution (CSGD) to characterize the Tropical Rainfall Measurement Mission Multi-Satellite Precipitation Analysis (TMPA), a commonly-used SMPP, and to compare it against the rain gage-based North American Land Data Assimilation System Phase 2 (NLDAS-2) reference precipitation dataset across the conterminous United States. The CSGD describes both the occurrence and the magnitude of precipitation. Climatological CSGD characterization reveals significant regional differences between TMPA and NLDAS-2 in terms of magnitude and probability of occurrence. We also use a flexible CSGD-based error modeling framework to quantify errors in TMPA relative to NLDAS-2. The framework can model conditional bias as either a linear or nonlinear function of satellite precipitation rate and can produce a “conditional CSGD” of describing the distribution of “true” precipitation based on a satellite observation. The framework is also used to “merge” TMPA with atmospheric variables from Modern-Era Retrospective analysis for Research and Applications (MERRA-2) to reduce SMPP errors. Despite the coarse resolution of MERRA-2, this merging offers robust reductions in random error due to the better performance of numerical models in resolving stratiform precipitation. Improvements in the near-realtime version of TMPA are relatively greater than for the higher-latency research version.
Journal of Hydrometeorology – American Meteorological Society
Published: Sep 1, 2017
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