Implementation of a Rainfall Normalization Module for GSMaP Microwave Imagers and Sounders
نویسندگان
چکیده
This paper introduces the Method of Microwave Rainfall Normalization (MMN) for Global Satellite Mapping Precipitation (GSMaP) algorithm in its latest version (V05, 8), released December 2021. The method aims to mitigate discrepancy GSMaP rainfall estimates among passive microwave (PMW) imagers/sounders (MWIs/MWSs) due differences sensor specifications and retrieval algorithms. basic idea MMN module is calibrate target PMW sensors with reference (the Measurement (GPM) Imager (GMI) Tropical Measuring Mission (TRMM) (TMI)) using cumulative distribution function (CDF) rain rate. Differences between CDF normalization table MWSs are greater than MWIs different More (less) MWS detected over ocean (land) GMI rainfall. Matchup data a compared evaluate performance. monthly mean bias error were improved almost all sensors. study leaves open possibility further inter-calibration improvement detection heavy retrievals.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2022
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs14184445