A deep learning method for bias correction of wind field in the South China Sea
To address the systematic bias in the Global Forecast System (GFS) wind field forecasts, we utilize deep learning techniques. The developed MU - Diffusion framework, based on a diffusion model and MultiUnet (a multitasking Unet model), establishes a nonlinear relationship between GFS and the fifth-g...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2024.1429057/full |
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| Summary: | To address the systematic bias in the Global Forecast System (GFS) wind field forecasts, we utilize deep learning techniques. The developed MU - Diffusion framework, based on a diffusion model and MultiUnet (a multitasking Unet model), establishes a nonlinear relationship between GFS and the fifth-generation EC atmospheric reanalysis (ERA5) data. Focusing on the South China Sea region, this method corrects both wind speed and direction simultaneously. Using 2022 GFS data, we achieved average enhancements of 42% in wind speed and 38.3% in wind direction compared to the initial GFS data. Tests in typhoon conditions also confirm the excellent performance of this architecture. |
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| ISSN: | 2296-7745 |