Editorial Type:
Article
Article Type:
Research Article

Improved Global Rainfall Retrieval Using the Special Sensor Microwave Imager (SSM/I)

Daniel Vila Cooperative Institute of Climate Studies, and Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

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Ralph Ferraro Satellite Climate Studies Branch, Center for Satellite Applications and Research, NOAA/NESDIS, Camp Springs, and Cooperative Institute of Climate Studies, Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

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Hilawe Semunegus Remote Sensing and Applications Division, National Climatic Data Center, NOAA/NESDIS, Asheville, North Carolina

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Print Publication:
01 May 2010
Collections:
International Precipitation Working Group (IPWG)
DOI:
https://doi.org/10.1175/2009JAMC2294.1
Page(s):
1032–1043
Restricted access

Abstract

Global monthly rainfall estimates have been produced from more than 20 years of measurements from the Defense Meteorological Satellite Program series of Special Sensor Microwave Imager (SSM/I). This is the longest passive microwave dataset available to analyze the seasonal, annual, and interannual rainfall variability on a global scale. The primary algorithm used in this study is an 85-GHz scattering-based algorithm over land, while a combined 85-GHz scattering and 19/37-GHz emission is used over ocean. The land portion of this algorithm is one of the components of the blended Global Precipitation Climatology Project rainfall climatology. Because previous SSM/I processing was performed in real time, only a basic quality control (QC) procedure had been employed to avoid unrealistic values in the input data. A more sophisticated, statistical-based QC procedure on the daily data grids (antenna temperature) was developed to remove unrealistic values not detected in the original database and was employed to reprocess the rainfall product using the current version of the algorithm for the period 1992–2007. Discrepancies associated with the SSM/I-derived monthly rainfall products are characterized through comparisons with various gauge-based and other satellite-derived rainfall estimates. A substantial reduction in biases was observed as a result of this QC scheme. This will yield vastly improved global rainfall datasets.

Corresponding author address: Daniel Vila, ESSIC, University of Maryland Research Park (M-Square), 5825 University Research Ct., Suite 4001, College Park, MD 20740-3823. Email: dvila@essic.umd.edu

This article included in the International Precipitation Working Group (IPWG) special collection.

Abstract

Global monthly rainfall estimates have been produced from more than 20 years of measurements from the Defense Meteorological Satellite Program series of Special Sensor Microwave Imager (SSM/I). This is the longest passive microwave dataset available to analyze the seasonal, annual, and interannual rainfall variability on a global scale. The primary algorithm used in this study is an 85-GHz scattering-based algorithm over land, while a combined 85-GHz scattering and 19/37-GHz emission is used over ocean. The land portion of this algorithm is one of the components of the blended Global Precipitation Climatology Project rainfall climatology. Because previous SSM/I processing was performed in real time, only a basic quality control (QC) procedure had been employed to avoid unrealistic values in the input data. A more sophisticated, statistical-based QC procedure on the daily data grids (antenna temperature) was developed to remove unrealistic values not detected in the original database and was employed to reprocess the rainfall product using the current version of the algorithm for the period 1992–2007. Discrepancies associated with the SSM/I-derived monthly rainfall products are characterized through comparisons with various gauge-based and other satellite-derived rainfall estimates. A substantial reduction in biases was observed as a result of this QC scheme. This will yield vastly improved global rainfall datasets.

Corresponding author address: Daniel Vila, ESSIC, University of Maryland Research Park (M-Square), 5825 University Research Ct., Suite 4001, College Park, MD 20740-3823. Email: dvila@essic.umd.edu

This article included in the International Precipitation Working Group (IPWG) special collection.

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