PrecipGradeNet: A New Paradigm and Model for Precipitation Retrieval with Grading of Precipitation Intensity

نویسندگان

چکیده

Near-real-time precipitation retrieval plays an important role in the study of evolutionary process and prevention disasters caused by heavy precipitation. Compared with ground-based observations, infrared estimations from geostationary satellites have great advantages terms geographical coverage temporal resolution. However, retrieved multispectral data still faces challenges accuracy, especially extreme cases. In this paper, we propose a new paradigm for satellite construct model called PrecipGradeNet. This uses FY-4A L1 FDI as input, IMERG training target, improves accuracy grading intensity through Res-UNet, semantic segmentation network. To evaluate model, compare results L2 QPE operational product to is considered ground truth. We fall area identification, interval discrimination, quantification. Experimental show that PrecipGradeNet has better overall performance compared identification POD increased 48% CSI HSS improved 21% 14%. also light 114% 64% 52%, quantification, RMSE CC 16% 15%. addition, avoids bias low high Therefore, proposed paper potential improve estimation products. suggests application methods may provide path correct satellite-based

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2022

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs15010227