An empirical method for joint inversion of wave and wind parameters based on SAR and wave spectrometer data

Yong Wan Xiaona Zhang Shuyan Lang Ennan Ma Yongshou Dai

Yong Wan, Xiaona Zhang, Shuyan Lang, Ennan Ma, Yongshou Dai. An empirical method for joint inversion of wave and wind parameters based on SAR and wave spectrometer data[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-024-2320-0
Citation: Yong Wan, Xiaona Zhang, Shuyan Lang, Ennan Ma, Yongshou Dai. An empirical method for joint inversion of wave and wind parameters based on SAR and wave spectrometer data[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-024-2320-0

doi: 10.1007/s13131-024-2320-0

An empirical method for joint inversion of wave and wind parameters based on SAR and wave spectrometer data

Funds: The project supported by Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources under contract No. 2023CFO016; the National Natural Science Foundation of China under contract No. 61931025; the Innovation Fund Project for Graduate Student of China University of Petroleum (East China) and the Fundamental Research Funds for the Central Universities under contract No. 23CX04042A.
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  • Figure  1.  Collocated data of SAR and SWIM (space window: 100 km, time window: 1.5 h) within a SAR revisit period (6 d).

    Figure  2.  Comparison of wave and wind parameters between ERA5 data and buoy data. a. Significant wave height, b. mean wave period, c. wind direction, and d. wind speed.

    Figure  3.  Collocated data from SAR, SWIM, and NDBC buoys.

    The black square denotes the quick-look of the Sentinel-1B WV mode SAR image, acquired at 14:16 on September 11, 2019; the red five stars and yellow triangles represent the positions of SAR-matched Buoys 46011, 46054, and SWIM.

    Figure  4.  The flow chart of the XGBoost regression. FFT, fast Fourier transform.

    Figure  5.  Rank the importance of input feature parameters. a. The degree to which the feature influences each output parameter; b. the total influence of the feature on the output parameter.

    Figure  6.  Heat map of the correlation coefficient between feature parameters.

    Figure  7.  Scatter plot of the output parameters of the model versus the wave and wind parameters provided by the ERA5 data. a. Wind direction, b. wind speed, c. significant wave height, d. mean wave period.

    Figure  8.  Comparison of inversion results of different methods. a. Significant wave height (SWH), b. wind speed (WS).

    Table  1.   Optimal hyperparameter for XGBoost in this paper

    HyperparameterValue
    number of estimators60
    max_depth20
    learning_rate0.1
    reg_lambda1
    reg_alpha0
    min_child_weight1
    下载: 导出CSV

    Table  2.   Comparison results of model output parameters with sea wave and wind parameters provided by ECMWF data

    Parameter RMSE Bias R
    WD 27.446° 22.239° 0.899
    WS 1.092 m/s 0.805 m/s 0.906
    SWH 0.212 m 0.147 m 0.968
    MWP 0.525 s 0.393 s 0.888
    下载: 导出CSV

    Table  3.   The model outputs are compared with the wave and wind parameters provided by the NDBC buoy data

    Parameter RMSE Bias R
    WD 27.698° 20.800° 0.870
    WS 1.315 m/s 1.253 m/s 0.892
    SWH 0.314 m 0.293 m 0.932
    MWP 0.888 s 0.805 s 0.853
    下载: 导出CSV

    Table  4.   The model output parameters are compared with the wave and wind parameters provided by scatterometer and altimeter data

    Parameter RMSE Bias R
    WS 1.219 m/s 0.951 m/s 0.906
    SWH 0.326 m 0.221 m 0.942
    WD 26.147° 22.115° 0.893
    下载: 导出CSV

    Table  5.   The model output parameters are compared with those provided by scatterometer and altimeter

    Method RMSE Bias
    WS SWH WS SWH
    Model 1.219 m/s 0.326 m 0.951 m/s 0.221 m
    SAR 1.548 m/s 0.434 m 1.136 m/s 0.342 m
    SWIM 1.237 m/s 0.386 m 0.936 m/s 0.238 m
    Method 1 1.358 m/s 0.413 m 1.025 m/s 0.329 m
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-10-09
  • 录用日期:  2023-12-25
  • 网络出版日期:  2024-05-14

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