Editorial Type:
Article
Article Type:
Research Article

Performance of IMERG as a Function of Spatiotemporal Scale

Jackson Tan Universities Space Research Association, and NASA Goddard Space Flight Center, Greenbelt, Maryland

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Walter A. Petersen Earth Sciences Office, NASA Marshall Space Flight Center, Huntsville, Alabama

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Pierre-Emmanuel Kirstetter School of Civil Engineering and Environmental Sciences, University of Oklahoma, and NOAA/National Severe Storms Laboratory, Norman, Oklahoma

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Yudong Tian Earth System Sciences Interdisciplinary Center, University of Maryland, College Park, College Park, and NASA Goddard Space Flight Center, Greenbelt, Maryland

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Print Publication:
01 Feb 2017
Collections:
Global Precipitation Measurement (GPM): Science and Applications
DOI:
https://doi.org/10.1175/JHM-D-16-0174.1
Page(s):
307–319
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Abstract

The Integrated Multisatellite Retrievals for GPM (IMERG), a global high-resolution gridded precipitation dataset, will enable a wide range of applications, ranging from studies on precipitation characteristics to applications in hydrology to evaluation of weather and climate models. These applications focus on different spatial and temporal scales and thus average the precipitation estimates to coarser resolutions. Such a modification of scale will impact the reliability of IMERG. In this study, the performance of the Final Run of IMERG is evaluated against ground-based measurements as a function of increasing spatial resolution (from 0.1° to 2.5°) and accumulation periods (from 0.5 to 24 h) over a region in the southeastern United States. For ground reference, a product derived from the Multi-Radar/Multi-Sensor suite, a radar- and gauge-based operational precipitation dataset, is used. The TRMM Multisatellite Precipitation Analysis (TMPA) is also included as a benchmark. In general, both IMERG and TMPA improve when scaled up to larger areas and longer time periods, with better identification of rain occurrences and consistent improvements in systematic and random errors of rain rates. Between the two satellite estimates, IMERG is slightly better than TMPA most of the time. These results will inform users on the reliability of IMERG over the scales relevant to their studies.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JHM-D-16-0174.s1.

Corresponding author e-mail: Jackson Tan, jackson.tan@nasa.gov

This article is included in the Global Precipitation Measurement (GPM) special collection.

Abstract

The Integrated Multisatellite Retrievals for GPM (IMERG), a global high-resolution gridded precipitation dataset, will enable a wide range of applications, ranging from studies on precipitation characteristics to applications in hydrology to evaluation of weather and climate models. These applications focus on different spatial and temporal scales and thus average the precipitation estimates to coarser resolutions. Such a modification of scale will impact the reliability of IMERG. In this study, the performance of the Final Run of IMERG is evaluated against ground-based measurements as a function of increasing spatial resolution (from 0.1° to 2.5°) and accumulation periods (from 0.5 to 24 h) over a region in the southeastern United States. For ground reference, a product derived from the Multi-Radar/Multi-Sensor suite, a radar- and gauge-based operational precipitation dataset, is used. The TRMM Multisatellite Precipitation Analysis (TMPA) is also included as a benchmark. In general, both IMERG and TMPA improve when scaled up to larger areas and longer time periods, with better identification of rain occurrences and consistent improvements in systematic and random errors of rain rates. Between the two satellite estimates, IMERG is slightly better than TMPA most of the time. These results will inform users on the reliability of IMERG over the scales relevant to their studies.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JHM-D-16-0174.s1.

Corresponding author e-mail: Jackson Tan, jackson.tan@nasa.gov

This article is included in the Global Precipitation Measurement (GPM) special collection.

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