基于微波辐射计SSM/I的海面风速反演算法研究及应用

海面风速是海洋环境的重要参数,微波辐射计是卫星监测海面风速的重要手段。通过微波辐射计SSM/I（Special Sensor Microwave/Imager）亮温与浮标实测风速建立的匹配数据集，利用人工神经网络构建海面风速反演模型。比较不同模型的反演效果，得出七通道单参数神经网络模型SANN（Singleparameter Artificial Neural Network ）反演的效果和浮标实测风速较为接近，均方根误差RMSE（Root Mean Square Error）为1.40m/s。因此选择该模型反演全球的月平均风速，并将反演结果与NOAA产品风速比较。结果表明：两者在整体分布和纬度平均上非常接近，均方根误差为1.03m/s。可见，该算法用于海面风速反演还是可行的。

Study on algorithms for retrieving sea surface wind speed and its application based on microwave radiometer SSM/I

The sea surface wind speed is an important parameter of marine environment and satellite microwave radiometer is an important tool to monitor this parameter. In this paper, a model for retrieving the sea surface wind speed is developed using the artificial neural network (ANN), through the data sets generated between the microwave radiometer SSM/I brightness temperatures and the in situ buoy measurements. By comparing the retrieval results of different models, it is concluded that the result of the seven channel SANN retrieval model is closer to the buoy measured wind speed with the root mean square error (RMSE) of 1.40m/s. Therefore, this model is chosen to retrieve the global monthly average wind speed, and the retrieval results are compared with the NOAA products. The results show that, both are very close in the overall and latitude average distribution with the RMSE of 1.03 m/s. It can be seen that the algorithm for the sea surface wind speed retrieval is feasible.