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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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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 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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