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Improvements of Rain/No-Rain Classification Methods for Microwave Radiometer over Coasts by Dynamic Surface-Type Classification

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  • 1 Graduate School of Science, Kyoto University, Kyoto, Japan
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Abstract

The rain/no-rain classification for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) fails to detect rain over coasts, where the microwave footprint encompasses a mixture of radiometrically cold ocean and radiometrically warm land. A static land–ocean–coast mask is used to determine the surface type of each satellite footprint. The coast mask is conservatively wide to account for the largest footprints, preventing use of the more appropriate ocean or land algorithm for coastal regions.

The purpose of this paper is to develop a classification whereby the smallest region possible is defined as coast. In this endeavor, two major improvements are applied to the land–ocean–coast classification. First, the surface classification based on microwave footprints of the high frequency actually used in rain detection is employed. Second, the footprint area of the surface classification is established using an effective field-of-view size and scan geometry of the TMI. These improvements are applied to the Global Satellite Mapping of Precipitation TMI algorithm. The classification result is validated using the TRMM precipitation radar. The validation shows that these improvements lead to better rain detection in the coastal region.

Corresponding author address: Tomoaki Mega, Graduate School of Engineering, Osaka University, Communication Engineering E3-416, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan. E-mail: mega@comf5.comm.eng.osaka-u.ac.jp

This article is included in the Precipitation Retrieval Algorithms for GPM special collection.

Abstract

The rain/no-rain classification for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) fails to detect rain over coasts, where the microwave footprint encompasses a mixture of radiometrically cold ocean and radiometrically warm land. A static land–ocean–coast mask is used to determine the surface type of each satellite footprint. The coast mask is conservatively wide to account for the largest footprints, preventing use of the more appropriate ocean or land algorithm for coastal regions.

The purpose of this paper is to develop a classification whereby the smallest region possible is defined as coast. In this endeavor, two major improvements are applied to the land–ocean–coast classification. First, the surface classification based on microwave footprints of the high frequency actually used in rain detection is employed. Second, the footprint area of the surface classification is established using an effective field-of-view size and scan geometry of the TMI. These improvements are applied to the Global Satellite Mapping of Precipitation TMI algorithm. The classification result is validated using the TRMM precipitation radar. The validation shows that these improvements lead to better rain detection in the coastal region.

Corresponding author address: Tomoaki Mega, Graduate School of Engineering, Osaka University, Communication Engineering E3-416, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan. E-mail: mega@comf5.comm.eng.osaka-u.ac.jp

This article is included in the Precipitation Retrieval Algorithms for GPM special collection.

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