Evaluation of global precipitation measurement rainfall estimates against three dense gauge networks

Authors: TAN, J. - Nasa Goddard Institute For Space Studies; PETERSEN, W.A. - National Aeronautics And Space Administration (NASA) - Johnson Space Center; KIRCHENGAST, G. - Universitat Graz; Goodrich, David - Dave; WOLFF, D.B. - National Aeronautics Space Administration (NASA) - Jet Propulsion Laboratory

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/22/2017
Publication Date: 3/1/2018
Citation: Tan, J., Petersen, W., Kirchengast, G., Goodrich, D.C., Wolff, D. 2018. Evaluation of global precipitation measurement rainfall estimates against three dense gauge networks. Journal of Hydrometeorology. 19:517-532. https://doi.org/10.1175/JHM-D-17-0174.1.
DOI: https://doi.org/10.1175/JHM-D-17-0174.1

Interpretive Summary: The National Aeronautics and Space Administration (NASA) has recently launched a group of satellites to estimate precipitation over the globe known as GPM or the Global Precipitation Measurement mission. Precipitation estimates from GPM were evaluated over three dense rain gauge networks. One in a temperate area of Austria, one in the mid-Atlantic coastal region, and the semi-arid USDA-ARS Walnut Gulch Long-Term Agroecosystem Research (LTAR) network site in southeastern Arizona. The evaluation was conducted at the level of individual satellite pixels (5–15 km) with multiple gauges per satellite pixel and precise precipitation accumulations near satellite overpass time to ensure a representative comparison. As expected, it was found that the active radar precipitation retrievals generally performed better than the passive retrievals. However, both retrievals struggle under coastal and semiarid environments. In particular, virga rainfall, that evaporates as it falls, appears to be a serious challenge for both the active and passive retrievals. Also detected was 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 passive retrievals. These results can pinpoint deficiencies in precipitation algorithms that may propagate into widely used gridded products.

Technical Abstract: Precipitation profiles from the Global Precipitation Measurement (GPM) Core Observatory 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. 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 DPRKu and GPROF. The authors detected the existence of lag due to the time it takes for satellite-observed precipitation to reach the ground. These results can pinpoint deficiencies in precipitation algorithms that may propagate into widely used gridded products.