A generative deep learning approach to stochastic downscaling of precipitation forecasts
Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affecting precipitation distribution and intensity occur below the resolved scale of global we...
主要な著者: | Harris, L, McRae, A, Chantry, M, Dueben, P, Palmer, T |
---|---|
フォーマット: | Journal article |
出版事項: |
American Geophysical Union
2022
|
類似資料
-
Machine learning emulation of gravity wave drag in numerical weather forecasting
著者:: Chantry, M, 等
出版事項: (2021) -
Deep learning for downscaling tropical cyclone rainfall to hazard-relevant spatial scales
著者:: Vosper, E, 等
出版事項: (2023) -
Downscaling Taiwan precipitation with a residual deep learning approach
著者:: Li-Huan Hsu, 等
出版事項: (2024-05-01) -
Machine Learning Emulation of Gravity Wave Drag in Numerical Weather Forecasting
著者:: Matthew Chantry, 等
出版事項: (2021-07-01) -
Customized deep learning for precipitation bias correction and downscaling
著者:: F. Wang, 等
出版事項: (2023-01-01)