Revised Test of Precipitation Forecasting Product of RMAPS-ST System Based on U-Net

Authors

  • Gao Hua Institute of Urban Meteorology (IUM), China Meteorological Administration (CMA), Beijing
  • Liang Jie School of Mathematics and Statistics,Guizhou University
  • Bao Dongling Institute of Urban Meteorology (IUM), China Meteorological Administration (CMA), Beijing
  • Zaiwen Wang Institute of Urban Meteorology (IUM), China Meteorological Administration (CMA), Beijing
  • Yajie Qi Institute of Urban Meteorology (IUM), China Meteorological Administration (CMA), Beijing
  • Zheng Zuofang Institute of Urban Meteorology (IUM), China Meteorological Administration (CMA), Beijing

DOI:

https://doi.org/10.14738/aivp.121.16248

Keywords:

Gridded data, Deep learning, Precipitation correction, U-Net

Abstract

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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Published

2024-03-01

How to Cite

Hua, G., Jie, L., Dongling, B., Wang, Z., Qi, Y., & Zuofang, Z. (2024). Revised Test of Precipitation Forecasting Product of RMAPS-ST System Based on U-Net. European Journal of Applied Sciences, 12(1), 561–567. https://doi.org/10.14738/aivp.121.16248