An intelligent algorithm for constructing quasi-real-time sea surface wind field

Liu Xiaoyan; Song Xiaojiang; Guo Anboyu; Hao Sai; Peng Wei

doi:10.12284/hyxb2024051

Volume 46 Issue 6

Jun. 2024

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Liu Xiaoyan，Song Xiaojiang，Guo Anboyu, et al. An intelligent algorithm for constructing quasi-real-time sea surface wind field[J]. Haiyang Xuebao，2024, 46(6)：51–65 doi: 10.12284/hyxb2024051

Citation:

Liu Xiaoyan，Song Xiaojiang，Guo Anboyu, et al. An intelligent algorithm for constructing quasi-real-time sea surface wind field[J]. Haiyang Xuebao，2024, 46(6)：51–65 doi: 10.12284/hyxb2024051

Citation:

Liu Xiaoyan，Song Xiaojiang，Guo Anboyu, et al. An intelligent algorithm for constructing quasi-real-time sea surface wind field[J]. Haiyang Xuebao，2024, 46(6)：51–65 doi: 10.12284/hyxb2024051

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An intelligent algorithm for constructing quasi-real-time sea surface wind field

doi: 10.12284/hyxb2024051

Liu Xiaoyan^{1, 2
,},
Song Xiaojiang^{1, 2
,
,},
Guo Anboyu^{1, 2},
Hao Sai^{1, 2},
Peng Wei^{1, 2}

1.
National Marine Enviroment Forecasting Center, Beijing 100081 China
2.
Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Beijing 100081, China

Received Date: 2024-01-24
Rev Recd Date: 2024-05-10

Available Online: 2024-07-12

Publish Date: 2024-06-01

Abstract

Abstract

In this paper, the correction model of CMA-GFS numerical model wind field is constructed based on the deep learning U-Net network, and the construction of the quasi-real-time sea surface wind field is rapidly accomplished by interpolation method using the corrected wind field with the correction model as the background field (CMA-GFS_Unet), and using the scatterometer sea surface wind data from the four satellites, namely, HY-2B/2C/2D and MetOp-B as the observation data. This intelligent algorithm can realize the generation of global sea surface fusion wind field (Fusion_QRT) with a spatial resolution of 0.25° and a temporal resolution of 6 hours in quasi-real time with a lag of 3 hours. The CMA-GFS, CMA-GFS_Unet and Fusion_QRT wind fields are evaluated using the CCMP fusion wind field data and the 10 m wind vector data from the Chinese offshore buoys, respectively.The results show that the quality of the CMA-GFS_Unet wind field has been significantly improved, and the quality of the wind speed of the Fusion_QRT wind field has been further improved but the quality of the wind direction has been slightly reduced. The mean absolute errors (MAEs) of wind speed are 1.13 m/s, 0.89 m/s and 0.84 m/s for the three wind fields by using CCMP data as reference, and the CMA-GFS_Unet and Fusion_QRT wind fields have improved by 21.3% and 25.7% compared to the CMA-GFS, respectively; while the MAEs of wind direction are 17.5°, 15.5° and 16°, and have improved by11.3% and 8.6%, respectively.The MAEs of wind speed are 1.50 m/s, 1.36 m/s and 1.28 m/s for the three wind fields by using buoy data as reference, and have improved by 9.5% and 14.7% , respectively; while the MAEs of wind direction are 23.3°, 22.7° and 24.0°, and have improved by 3.0% and −3.9% , respectively.
- U-Net,
- CCMP,
- CMA-GFS,
- HY-2B/2C/2D,
- MetOp-B,
- quasi-real-time,
- sea surface wind field

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References(33)

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