Open AccessImproved Global Rainfall Retrieval Using the Special Sensor Microwave Imager (SSM/I)
Author(s) -
Daniel Vila,
Ralph Ferraro,
Hilawe Semunegus
Publication year - 2010
Publication title -
journal of applied meteorology and climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.079
H-Index - 134
eISSN - 1558-8432
pISSN - 1558-8424
DOI - 10.1175/2009jamc2294.1
Subject(s) - satellite , environmental science , special sensor microwave/imager , microwave , precipitation , meteorology , rain gauge , remote sensing , defense meteorological satellite program , climatology , scale (ratio) , computer science , geology , geography , telecommunications , cartography , brightness temperature , engineering , aerospace engineering
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.