Characterizing GPROF Regional Bias Using Radar-Derived Hydrometeor Information

TL;DR

This study identifies key physical factors causing regional biases in satellite rainfall estimates and demonstrates how incorporating hydrometeor information reduces these biases, enhancing the accuracy of precipitation data records.

Contribution

It introduces a method to quantify and reduce regional biases in GPROF satellite precipitation retrievals using radar-derived hydrometeor information.

Findings

Rain intensity and ice-rain ratio are primary bias sources.

Incorporating hydrometeor info halves interregional bias.

Adding polarization-corrected brightness temperature further reduces bias.

Abstract

Current satellite precipitation retrievals like GPROF assume that brightness temperature 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, complicating the use of traditional ground validation techniques which seek to understand these uncertainties. This study aims to characterize the physical contributors to these biases for use in uncertainty quantification. To do this, coincident GPROF Version 7, 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. Rain intensity and ice-rain ratio were identified as the primary sources of GPROF regional biases. By incorporating the information from these sources, the self-similarity between these three regions was brought from within 13 percent to within 7 percent, reducing the interregional bias by half. Including a third constraint based on the polarization-corrected 37-GHz brightness temperature further improved this self-similarity to within 4 percent by accounting for second-order hydrometeor profile differences which were underutilized by GPROF. Comparing the effects of these three constraints between GPROF Version 7 with the 1D version of GPROF-NN showed similar improvements, indicating the utility of this uncertainty quantification and adjustment method across precipitation products. With these constraints, regional GPROF biases can be made more consistent, improving the fidelity of the precipitation climate data records and operational precipitation products which utilize this information.