Evaluation of Global Precipitation Measurement Rainfall Estimates against Three Dense Gauge Networks

Evaluation of Global Precipitation Measurement Rainfall Estimates against Three Dense Gauge Networks AbstractPrecipitation 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 (GPROF) algorithm 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, we evaluate the rainfall estimates from DPR-Ku as well as GPROF estimates from passive microwave sensors in the GPM constellation. Our evaluation is conducted at the level of individual satellite pixels (5 to 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, we 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. We 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. We also showed 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Hydrometeorology American Meteorological Society

Evaluation of Global Precipitation Measurement Rainfall Estimates against Three Dense Gauge Networks

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1525-7541
D.O.I.
10.1175/JHM-D-17-0174.1
Publisher site
See Article on Publisher Site

Abstract

AbstractPrecipitation 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 (GPROF) algorithm 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, we evaluate the rainfall estimates from DPR-Ku as well as GPROF estimates from passive microwave sensors in the GPM constellation. Our evaluation is conducted at the level of individual satellite pixels (5 to 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, we 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. We 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. We also showed 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.

Journal

Journal of HydrometeorologyAmerican Meteorological Society

Published: Feb 9, 2018

References

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