The Influence of Surface and Precipitation Characteristics on TRMM Microwave Imager Rainfall Retrieval Uncertainty

N. Carr * School of Meteorology, University of Oklahoma, Norman, Oklahoma
Advanced Radar Research Center, National Weather Center, Norman, Oklahoma

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P.-E. Kirstetter Advanced Radar Research Center, National Weather Center, Norman, Oklahoma
NOAA/National Severe Storms Laboratory, Norman, Oklahoma

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Y. Hong School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, Oklahoma

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J. J. Gourley NOAA/National Severe Storms Laboratory, Norman, Oklahoma

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M. Schwaller NASA Goddard Space Flight Center, Greenbelt, Maryland

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W. Petersen ** NASA Wallops Flight Facility, Wallops Island, Virginia

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Nai-Yu Wang I.M. Systems Group, College Park, Maryland

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Ralph R. Ferraro NOAA/NESDIS, College Park, Maryland

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Xianwu Xue School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, Oklahoma

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Abstract

Characterization of the error associated with quantitative precipitation estimates (QPEs) from spaceborne passive microwave (PMW) sensors is important for a variety of applications ranging from flood forecasting to climate monitoring. This study evaluates the joint influence of precipitation and surface characteristics on the error structure of NASA’s Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) surface QPE product (2A12). TMI precipitation products are compared with high-resolution reference precipitation products obtained from the NOAA/NSSL ground radar–based Multi-Radar Multi-Sensor (MRMS) system. Surface characteristics were represented via a surface classification dataset derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). This study assesses the ability of 2A12 to detect, classify, and quantify precipitation at its native resolution for the 2011 warm season (March–September) over the southern continental United States. Decreased algorithm performance is apparent over dry and sparsely vegetated regions, a probable result of the surface radiation signal mimicking the scattering signature associated with frozen hydrometeors. Algorithm performance is also shown to be positively correlated with precipitation coverage over the sensor footprint. The algorithm also performs better in pure stratiform and convective precipitation events, compared to events containing a mixture of stratiform and convective precipitation within the footprint. This possibly results from the high spatial gradients of precipitation associated with these events and an underrepresentation of such cases in the retrieval database. The methodology and framework developed herein apply more generally to precipitation estimates from other passive microwave sensors on board low-Earth-orbiting satellites and specifically could be used to evaluate PMW sensors associated with the recently launched Global Precipitation Measurement (GPM) mission.

Corresponding author address: Nick Carr, Advanced Radar Research Center, National Weather Center, 120 David L. Boren Blvd., Rm. 4630, Norman, OK 73072-7303. E-mail: n.carr2@ou.edu

Abstract

Characterization of the error associated with quantitative precipitation estimates (QPEs) from spaceborne passive microwave (PMW) sensors is important for a variety of applications ranging from flood forecasting to climate monitoring. This study evaluates the joint influence of precipitation and surface characteristics on the error structure of NASA’s Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) surface QPE product (2A12). TMI precipitation products are compared with high-resolution reference precipitation products obtained from the NOAA/NSSL ground radar–based Multi-Radar Multi-Sensor (MRMS) system. Surface characteristics were represented via a surface classification dataset derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). This study assesses the ability of 2A12 to detect, classify, and quantify precipitation at its native resolution for the 2011 warm season (March–September) over the southern continental United States. Decreased algorithm performance is apparent over dry and sparsely vegetated regions, a probable result of the surface radiation signal mimicking the scattering signature associated with frozen hydrometeors. Algorithm performance is also shown to be positively correlated with precipitation coverage over the sensor footprint. The algorithm also performs better in pure stratiform and convective precipitation events, compared to events containing a mixture of stratiform and convective precipitation within the footprint. This possibly results from the high spatial gradients of precipitation associated with these events and an underrepresentation of such cases in the retrieval database. The methodology and framework developed herein apply more generally to precipitation estimates from other passive microwave sensors on board low-Earth-orbiting satellites and specifically could be used to evaluate PMW sensors associated with the recently launched Global Precipitation Measurement (GPM) mission.

Corresponding author address: Nick Carr, Advanced Radar Research Center, National Weather Center, 120 David L. Boren Blvd., Rm. 4630, Norman, OK 73072-7303. E-mail: n.carr2@ou.edu
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