Wind speed inversion of high frequency radar based on artifical neural network

Cai Jiajia; Zeng Yuming; Zhou Hao; Wen Biyang

doi:10.3969/j.issn.0253-4193.2019.11.014

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Cai Jiajia，Zeng Yuming，Zhou Hao, et al. Wind speed inversion of high frequency radar based on artifical neural network[J]. Haiyang Xuebao，2019, 41(11)：150–155，doi:10.3969/j.issn.0253−4193.2019.11.014

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Wind speed inversion of high frequency radar based on artifical neural network

doi: 10.3969/j.issn.0253-4193.2019.11.014

Electronic Information School, Wuhan University, Wuhan 430072, China

Received Date: 2018-09-16
Rev Recd Date: 2018-11-15

Available Online: 2021-04-21

Publish Date: 2019-11-25

Abstract

Abstract

Wind speed is one of the important ocean state parameters. Accurate extraction of sea surface wind speed is an important guarantee for achieving marine environmental monitoring and coastal engineering applications. At present, as an emerging marine environment monitoring device, high frequency radar still has challenges in wind speed extraction. This paper proposes a wind speed extraction method based on artificial neural network which can be trained by historical sea state data measured by buoys to achieve non-linear mapping among wind and effective wave height, wave period, wind direction, and time. The test results show the stability of the trained network both in time and space and the trained network was applied to the wind speed inversion of the high frequency surface wave radar, OSMAR-S. The correlation coefficient between the inversion wind speed and the measured wind speed of the buoy reaches 0.849, and the root mean square error is 2.11 m/s. This result is significantly better than the conventional SMB method which inverts the wind speed from wave height, and verifies the feasibility of this method in high frequency radar wind speed inversion.
- wind speed inversion,
- artificial neural network,
- high frequency surface wave radar

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References

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