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METHODS article

Front. Mar. Sci.
Sec. Ocean Observation
Volume 11 - 2024 | doi: 10.3389/fmars.2024.1429057

A Deep Learning Method for Bias Correction of Wind Field in the South China Sea

Provisionally accepted
Cong Pang Cong Pang 1Tao Song Tao Song 1,2*Handan Sun Handan Sun 1李 昕 李 昕 1Danya Xu Danya Xu 3*
  • 1 China University of Petroleum (East China), Dongying, China
  • 2 Polytechnic University of Madrid, Madrid, Madrid, Spain
  • 3 Guangdong Laboratory of Marine Science and Engineering, Guangdong, China

The final, formatted version of the article will be published soon.

    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.

    Keywords: deep learning, mu-diffusion, South China Sea, Bias Correction, Wind field, GFS, ERA5

    Received: 07 May 2024; Accepted: 28 Nov 2024.

    Copyright: © 2024 Pang, Song, Sun, 昕 and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Tao Song, China University of Petroleum (East China), Dongying, China
    Danya Xu, Guangdong Laboratory of Marine Science and Engineering, Guangdong, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.