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Inversion of heavy precipitation in Hunan based on FY-3D/MWRI data |
WANG Taoran1,2( ), WU Ying1( ), MA Jingwen1, HUANG Yuanyuan1, FU Qijia1 |
1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China 2. Meteorological Bureau of Yiyang City, Hunan Province, Yiyang 413099, China |
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Abstract Using level-1 (L1) brightness temperature data from the Microwave Radiation Imager (MWRI) on board Fengyun-3D (FY-3D) satellite and the corresponding Level-2 (L2) precipitation products, this study established a precipitation rate inversion model for land surface heavy precipitation in Hunan Province based the polarization corrected temperature (PCT) and scatter index (SI). The proposed model was validated using individual examples. The results indicate that the precipitation rates retrieved from the L1 brightness temperature data of the FY-3D satellite were generally consistent with the results obtained from the L2 precipitation products. Compared to actual data, the retrieved precipitation rates were slightly higher in low precipitation areas but smaller in centers of high precipitation areas. The ascending orbit-based inversion model exhibited a correlation coefficient, mean absolute error (MAE), and root mean square error (RMSE) of 0.876 1, 0.771 1, and 1.151 4 mm/h, respectively. Conversely, the descending orbit-based inversion model presented a correlation coefficient, MAE, and RMSE of 0.911 3, 1.130 4, and 1.832 2 mm/h, respectively. The inversion results showed a larger precipitation distribution range than that of L2 products. Compared to the measurements at ground meteorological stations, the inversion model demonstrated higher accuracy than L2 products. This study successfully determined the distribution of land surface heavy precipitation in Hunan through inversion. The results of this study can provide a reference for investigating the relationship between microwave brightness temperature and precipitation and estimating land surface heavy precipitation.
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Keywords
FY-3D/MWRI
PCT-SI algorithm
heavy precipitation
precipitation rate inversion
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Issue Date: 03 September 2025
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