Revised Test of Precipitation Forecasting Product of RMAPS-ST System Based on U-Net
DOI:
https://doi.org/10.14738/aivp.121.16248Keywords:
Gridded data, Deep learning, Precipitation correction, U-NetAbstract
Using the U-Net algorithm idea in the field of artificial intelligence, a deep learning framework is designed and the loss function is redefined. This learning framework revised the precipitation forecast results of RMAPS-ST after training on two summer samples. Approved the revised precipitation forecast for June-July 2022 and precipitation on August 18, 2022, The test results show that the revised forecast level is significantly improved, especially for magnitude greater than 0.1mm/3 hours, and the CSI score is increased by an average of 30% compared with before revision.
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Copyright (c) 2024 Gao Hua, Liang Jie, Bao Dongling, Zaiwen Wang, Yajie Qi, Zheng Zuofang
This work is licensed under a Creative Commons Attribution 4.0 International License.