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An intelligent algorithm for constructing quasi-real-time sea surface wind field
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Xiaoyan Liu1, 2, Xiaojiang Song1, 2, *, Anboyu Guo1, 2, Sai Hao1, 2, Wei Peng1, 2
Haiyang Xuebao | 2024, 46(6) : 51 - 65
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Haiyang Xuebao | 2024, 46(6): 51-65
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An intelligent algorithm for constructing quasi-real-time sea surface wind field
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Xiaoyan Liu1, 2, Xiaojiang Song1, 2, *, Anboyu Guo1, 2, Sai Hao1, 2, Wei Peng1, 2
Affiliations
  • 1. National Marine Enviroment Forecasting Center, Beijing 100081 China
  • 2. Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Beijing 100081, China
Published: 2024-06-30 doi: 10.12284/hyxb2024051
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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
Xiaoyan Liu, Xiaojiang Song, Anboyu Guo, Sai Hao, Wei Peng. An intelligent algorithm for constructing quasi-real-time sea surface wind field[J]. Haiyang Xuebao, 2024 , 46 (6) : 51 -65 . DOI: 10.12284/hyxb2024051
Year 2024 volume 46 Issue 6
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doi: 10.12284/hyxb2024051
  • Receive Date:2024-01-24
  • Online Date:2025-11-26
  • Published:2024-06-30
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  • Received:2024-01-24
  • Revised:2024-05-10
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Affiliations
    1. National Marine Enviroment Forecasting Center, Beijing 100081 China
    2. Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Beijing 100081, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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Genus
种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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