Understanding Regional Passive Microwave Precipitation Bias Using Radar-Derived Information

Current passive microwave precipitation retrievals assume that brightness temperature (TB) is sufficient to constrain rainfall. This information, however, often represents multiple rain states, resulting in rainfall estimate uncertainties. These uncertainties, while dominated by random variability, can also exhibit substantial regional biases which complicate the use of traditional ground validation techniques. This study aims to characterize the physical contributors to these biases for use in uncertainty quantification. Coincident GPROF Version 7, Global Precipitation Measurement (GPM) Microwave Imager (GMI), and GPM combined observations were examined over three tropical land regions, the Amazon, Congo, and Southeast Asia, which are known to exhibit distinct biases relative to one another when comparing GPROF with GPM Combined precipitation. The interrelation between rain intensity and ice-rain ratio (IRR) was identified as the primary descriptor of regional bias. Using this interrelation, the regional bias range is decreased from ±13% to ±7%. Including a third constraint based on the polarization-corrected 37-GHz TB further reduced this range to ±4% by accounting for second-order effects. Comparing the effects of these three parameters between GPROF Version 7 and the 1-D version of GPROF-NN showed similar improvements, indicating the utility of this uncertainty quantification and adjustment method across precipitation products. With these constraints, regional precipitation biases can be understood from a physical perspective, presenting an opportunity for uncertainty quantification which uses knowledge of the physical state of the atmosphere.