一种机器学习海面风场快速融合的方法

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，RMSE_d以及MAE_d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。

Sea surface wind field smart fusion base on machine learning method

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, RMSE_d and MAE_d 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.