PrecipGradeNet: A New Paradigm and Model for Precipitation Retrieval with Grading of Precipitation Intensity
Near-real-time precipitation retrieval plays an important role in the study of the evolutionary process of precipitation and the prevention of disasters caused by heavy precipitation. Compared with ground-based precipitation observations, the infrared precipitation estimations from geostationary sat...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/1/227 |
| _version_ | 1797431211326439424 |
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| author | Danfeng Zhang Yuqing He Xiaoqing Li Lu Zhang Na Xu |
| author_facet | Danfeng Zhang Yuqing He Xiaoqing Li Lu Zhang Na Xu |
| author_sort | Danfeng Zhang |
| collection | DOAJ |
| description | Near-real-time precipitation retrieval plays an important role in the study of the evolutionary process of precipitation and the prevention of disasters caused by heavy precipitation. Compared with ground-based precipitation observations, the infrared precipitation estimations from geostationary satellites have great advantages in terms of geographical coverage and temporal resolution. However, precipitation retrieved from multispectral infrared data still faces challenges in terms of accuracy, especially in extreme cases. In this paper, we propose a new paradigm for satellite multispectral infrared data retrieval of precipitation and construct a new model called PrecipGradeNet. This model uses FY-4A L1 FDI data as the input, IMERG precipitation data as the training target, and improves the precipitation retrieval accuracy by grading the precipitation intensity through Res-UNet, a semantic segmentation network. To evaluate the precipitation retrieval of the model, we compare the retrieval results with the FY-4A L2 QPE operational product to the IMERG precipitation. IMERG is considered as the ground truth. We evaluate the precipitation retrieval from the precipitation fall area identification, the precipitation intensity interval discrimination, and the precipitation quantification. Experimental results show that PrecipGradeNet has better overall performance compared with the FY-4A QPE product in precipitation fall area identification with POD increased by 48% and CSI and HSS improved by 21% and 14%. PrecipGradeNet also has better performance in light precipitation with POD increased by 114% and CSI and HSS improved by 64% and 52%, and better overall precipitation quantification, with RMSE and CC improved by 16% and 15%. In addition, PrecipGradeNet avoids the overall bias in the low and extreme high precipitation cases. Therefore, the new paradigm proposed in this paper has the potential to improve the retrieval accuracy of satellite precipitation estimation products. This study suggests that the application of semantic segmentation methods may provide a new path to correct the intensity bias of the satellite-based precipitation products. |
| first_indexed | 2024-03-09T09:41:38Z |
| format | Article |
| id | doaj.art-5b88ddce74fd401189f1d31034431db3 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T09:41:38Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-5b88ddce74fd401189f1d31034431db32023-12-02T00:51:56ZengMDPI AGRemote Sensing2072-42922022-12-0115122710.3390/rs15010227PrecipGradeNet: A New Paradigm and Model for Precipitation Retrieval with Grading of Precipitation IntensityDanfeng Zhang0Yuqing He1Xiaoqing Li2Lu Zhang3Na Xu4School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaInnovation Center for Fengyun Meteorological Satellite, National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, ChinaInnovation Center for Fengyun Meteorological Satellite, National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, ChinaInnovation Center for Fengyun Meteorological Satellite, National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, ChinaNear-real-time precipitation retrieval plays an important role in the study of the evolutionary process of precipitation and the prevention of disasters caused by heavy precipitation. Compared with ground-based precipitation observations, the infrared precipitation estimations from geostationary satellites have great advantages in terms of geographical coverage and temporal resolution. However, precipitation retrieved from multispectral infrared data still faces challenges in terms of accuracy, especially in extreme cases. In this paper, we propose a new paradigm for satellite multispectral infrared data retrieval of precipitation and construct a new model called PrecipGradeNet. This model uses FY-4A L1 FDI data as the input, IMERG precipitation data as the training target, and improves the precipitation retrieval accuracy by grading the precipitation intensity through Res-UNet, a semantic segmentation network. To evaluate the precipitation retrieval of the model, we compare the retrieval results with the FY-4A L2 QPE operational product to the IMERG precipitation. IMERG is considered as the ground truth. We evaluate the precipitation retrieval from the precipitation fall area identification, the precipitation intensity interval discrimination, and the precipitation quantification. Experimental results show that PrecipGradeNet has better overall performance compared with the FY-4A QPE product in precipitation fall area identification with POD increased by 48% and CSI and HSS improved by 21% and 14%. PrecipGradeNet also has better performance in light precipitation with POD increased by 114% and CSI and HSS improved by 64% and 52%, and better overall precipitation quantification, with RMSE and CC improved by 16% and 15%. In addition, PrecipGradeNet avoids the overall bias in the low and extreme high precipitation cases. Therefore, the new paradigm proposed in this paper has the potential to improve the retrieval accuracy of satellite precipitation estimation products. This study suggests that the application of semantic segmentation methods may provide a new path to correct the intensity bias of the satellite-based precipitation products.https://www.mdpi.com/2072-4292/15/1/227geostationary satelliteFY-4A satellitemultispectral infrared imageryquantitative precipitation estimationsemantic segmentationRes-UNet |
| spellingShingle | Danfeng Zhang Yuqing He Xiaoqing Li Lu Zhang Na Xu PrecipGradeNet: A New Paradigm and Model for Precipitation Retrieval with Grading of Precipitation Intensity Remote Sensing geostationary satellite FY-4A satellite multispectral infrared imagery quantitative precipitation estimation semantic segmentation Res-UNet |
| title | PrecipGradeNet: A New Paradigm and Model for Precipitation Retrieval with Grading of Precipitation Intensity |
| title_full | PrecipGradeNet: A New Paradigm and Model for Precipitation Retrieval with Grading of Precipitation Intensity |
| title_fullStr | PrecipGradeNet: A New Paradigm and Model for Precipitation Retrieval with Grading of Precipitation Intensity |
| title_full_unstemmed | PrecipGradeNet: A New Paradigm and Model for Precipitation Retrieval with Grading of Precipitation Intensity |
| title_short | PrecipGradeNet: A New Paradigm and Model for Precipitation Retrieval with Grading of Precipitation Intensity |
| title_sort | precipgradenet a new paradigm and model for precipitation retrieval with grading of precipitation intensity |
| topic | geostationary satellite FY-4A satellite multispectral infrared imagery quantitative precipitation estimation semantic segmentation Res-UNet |
| url | https://www.mdpi.com/2072-4292/15/1/227 |
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