Journal
JOURNAL OF HYDROMETEOROLOGY
Volume 19, Issue 3, Pages 517-532Publisher
AMER METEOROLOGICAL SOC
DOI: 10.1175/JHM-D-17-0174.1
Keywords
Precipitation; Rainfall; Microwave observations; Radars; Radar observations; Remote sensing; Bayesian methods
Categories
Funding
- NASA Postdoctoral Program at Goddard Space Flight Center
- Universities Space Research Association [NNH15CO48B]
- PMM Science Team funding [NNH15ZDA001N-PMM]
- GPM Mission
- Austrian Ministry for Science and Research
- University of Graz
- state of Styria
- city of Graz
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Precipitation profiles from the Global Precipitation Measurement (GPM) Core Observatory Dual-Frequency Precipitation Radar (DPR; Ku and Ka bands) form part of the a priori database used in the Goddard profiling algorithm (GPROF) for retrievals of precipitation from passive microwave sensors, which are in turn used as high-quality precipitation estimates in gridded products. As GPROF performs precipitation retrievals as a function of surface classes, error characteristics may be dependent on surface types. In this study, the authors evaluate the rainfall estimates from DPR Ku as well as GPROF estimates from passive microwave sensors in the GPM constellation. The evaluation is conducted at the level of individual satellite pixels (5-15 km) against three dense networks of rain gauges, located over contrasting land surface types and rainfall regimes, with multiple gauges per satellite pixel and precise accumulation about overpass time to ensure a representative comparison. As expected, it was found that the active retrievals from DPR Ku generally performed better than the passive retrievals from GPROF. However, both retrievals struggle under coastal and semiarid environments. In particular, virga appears to be a serious challenge for both DPR Ku and GPROF. The authors detected the existence of lag due to the time it takes for satellite-observed precipitation to reach the ground, but the precise delay is difficult to quantify. It was also shown that subpixel variability is a contributor to the errors in GPROF. These results can pinpoint deficiencies in precipitation algorithms that may propagate into widely used gridded products.
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