Sea surface wind field smart fusion base on machine learning method
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摘要: 基于多源资料进行海面风场的同化融合或插值融合，目前受到计算能力的较大制约。本文提出在多源卫星数据和ERA-5再分析数据重叠区域，训练基于XGBoost的机器学习ERA-5数据修正融合模型。然后基于该模型快速修正ERA-5数据（机器学习推理）。由于机器学习推理的快速性，ERA-5全区域修正融合的时间仅需2 s左右，可以较小计算代价构建整个海面融合风场。本文以10 m风速、10 m风向、U10分量和V10分量等典型风场变量展开，考虑了海陆分布差异使用陆地掩膜消除陆地区域，分别构建D_S_A_XGBoost、D_S_O_XGBoost、U_V_A_XGBoost、U_V_O_XGBoost 4个ERA-5修正模型，并最终生成海面融合风场。通过修正前后的ERA-5再分析数据与卫星数据进行比较，上述4个模型均减小了ERA-5再分析数据与卫星数据的差距。特别是在风速方面，不论是均方根误差（RMSE）还是绝对误差（MAE）都得到有效降低。在风向方面上，RMSEd以及MAEd也呈现降低趋势。在利用热带大气海洋观测计划（Tropical Atmosphere Ocean Array，TAO）浮标数据对4种XGBoost模型进行评价发现，U_V_O_XGBoost模型对于ERA-5数据的修正结果最好，其相关性达到0.893，提高了约0.011，结果表明本文在保证风场精度的情况下较大地提高了融合速度。Abstract: The assimilation fusion or interpolation fusion of the sea surface wind field based on multi-source data is currently restricted by computing power. This paper proposes to train the XGBoost-based machine learning ERA-5 data correction fusion model in the overlapping area of the multi-source satellite data and the ERA-5 reanalysis data, and then use the model to quickly correct (machine learning inference) ERA-5 data, of which the ERA-5 whole area correction fusion it only takes about 2 seconds. Due to the rapidity of machine learning inference, the entire sea surface fusion wind field can be constructed at a lower computational cost. This paper expands on typical wind field variables such as 10 m wind speed, 10 m wind direction, U10 component and V10 component, taking into account the difference in sea and land distribution, using land masks to eliminate land areas, and constructing D_S_A_XGBoost, D_S_O_XGBoost, U_V_A_XGBoost, U_V_O_XGBoost corrections model, and finally generate sea surface fusion wind field. By comparing the ERA-5 reanalysis data before and after the correction with the satellite data, the above four models all reduce the gap between the ERA-5 reanalysis data and the satellite data. Especially in terms of wind speed, both root mean square error (RMSE) and mean absolute error (MAE) are effectively reduced. In terms of wind direction, RMSEd and MAEd also show a decreasing trend. Using Tropical Atmosphere Ocean Array (TAO) buoy data to evaluate the four XGBoost models, it is found that the U_V_O_XGBoost model has the best correction results for ERA-5 data, and its correlation reaches 0.893, an increase of about 0.011, and the results show that the fusion speed is greatly improved under the condition of ensuring the accuracy of wind field.
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图 4 海陆风场风速统计直方图
Fig. 4 Histogram of wind speed statistics for sea and land wind fields
图 8 浮标数据与ERA-5数据的风速对比
Fig. 8 Comparison of wind speed between buoy data and ERA-5 data
表 1 卫星评价数据信息（全区域）
Tab. 1 Satellite data information used in the test (whole region)
卫星 时间 数据数量 HY-2B 2021年1月31日 00:00:00 7 026 2021年1月31日 12:00:00 6 233 CFOSAT 2021年1月31日 00:00:00 6 419 2021年1月31日 12:00:00 12 928 MetOp-B 2021年1月31日 00:00:00 54 982 2021年1月31日 12:00:00 55 586 
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表 2 全区域训练模型评价结果
Tab. 2 Evaluation results of the whole regional training model
卫星 时间 模型 风向/（°） 风速/（m∙s−1） RMSEd MAEd RMSE MAE HY-2B 2021年1月31日
00:00:00原始 42.941 12.480 1.313 0.917 U_V_A_XGBoost 42.133 11.614 1.166 0.803 D_S_A_XGBoost 39.497 12.490 1.083 0.774 2021年1月31日
12:00:00原始 50.959 11.917 1.238 0.946 U_V_A_XGBoost 48.910 10.757 1.118 0.853 D_S_A_XGBoost 43.517 10.989 1.133 0.845 卫星 时间 模型 风向/（°） 风速/（m∙s−1） RMSEd MAEd RMSE MAE CFOSAT 2021年1月31日
00:00:00原始 37.334 7.685 1.814 1.461 U_V_A_XGBoost 35.012 7.150 1.465 1.150 D_S_A_XGBoost 35.529 8.060 1.434 1.107 2021年1月31日
12:00:00原始 79.938 14.234 1.340 1.030 U_V_A_XGBoost 78.729 13.814 1.194 0.903 D_S_A_XGBoost 76.858 15.671 1.269 0.952 卫星 时间 模型 风向/（°） 风速/（m∙s−1） RMSEd MAEd RMSE MAE MetOp-B 2021年1月31日
00:00:00原始 25.232 9.860 1.270 0.940 U_V_A_XGBoost 24.408 10.122 1.118 0.806 D_S_A_XGBoost 24.391 10.063 1.053 0.771 2021年1月31日
12:00:00原始 32.589 8.530 1.190 0.883 U_V_A_XGBoost 31.433 8.534 1.076 0.786 D_S_A_XGBoost 33.728 9.318 1.011 0.735 注：加粗数字表示最优结果。
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表 3 卫星评价数据信息（陆地掩码）
Tab. 3 Satellite data information used in the test (land mask)
卫星 时间 数据数量 HY-2B 2021年1月31日 00:00:00 7 026 2021年1月31日 12:00:00 6 233 CFOSAT 2021年1月31日 00:00:00 6 419 2021年1月31日 12:00:00 12 928 MetOp-B 2021年1月31日 00:00:00 54 977 2021年1月31日 12:00:00 55 575
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表 4 陆地掩码训练模型评价结果
Tab. 4 Evaluation results of land mask training model
卫星 时间 模型 风向/（°） 风速/（m∙s−1） RMSEd MAEd RMSE MAE HY-2B 2021年1月31日
00:00:00原始 42.941 12.480 1.313 0.917 U_V_O_XGBoost 41.342 11.508 1.182 0.814 D_S_O_XGBoost 38.860 12.409 1.076 0.767 2021年1月31日
12:00:00原始 50.959 11.917 1.238 0.946 U_V_O_XGBoost 49.686 10.843 1.120 0.851 D_S_O_XGBoost 42.023 10.990 1.120 0.834 卫星 时间 模型 风向/（°） 风速/（m∙s−1） RMSEd MAEd RMSE MAE CFOSAT 2021年1月31日
00:00:00原始 37.334 7.685 1.814 1.461 U_V_O_XGBoost 34.275 7.214 1.461 1.146 D_S_O_XGBoost 36.178 8.031 1.431 1.103 2021年1月31日
12:00:00原始 79.938 14.234 1.340 1.030 U_V_O_XGBoost 79.149 13.827 1.219 0.914 D_S_O_XGBoost 76.327 15.197 1.241 0.937 卫星 时间 模型 风向/（°） 风速/（m∙s−1） RMSEd MAEd RMSE MAE MetOp-B 2021年1月31日
00:00:00原始 25.213 9.860 1.265 0.937 U_V_O_XGBoost 25.183 10.093 1.140 0.815 D_S_O_XGBoost 24.996 10.079 1.093 0.787 2021年1月31日
12:00:00原始 32.586 8.525 1.182 0.880 U_V_O_XGBoost 31.501 8.627 1.111 0.806 D_S_O_XGBoost 33.056 9.357 1.058 0.763 注：加粗数字表示最优结果。
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表 5 不同机器学习算法风场融合结果
Tab. 5 Wind field fusion results of different machine learning algorithms
相关系数 均方根误差 标准差 ERA-5 0.882 0.980 1.938 XGBoost 0.893 0.890 1.936 Random Forest 0.890 0.915 1.955 Adaboost 0.892 0.906 1.978
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