基于混合密度网络的NSCAT散射计海面风场反演
Mixture density network based sea surface wind field retrieval from NSCAT scatterometer data
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摘要: 以NSCAT散射计数据为例,介绍了一种神经网络反演海面风场的方法.风速的反演是基于多层感知器网络;多解风向的反演是基于多层感知器网络和混合密度模型组合而成的混合密度网络,其中的核函数采用高斯函数的形式.通过与欧洲中期天气预报模式风场和现场浮标数据对比,证明了该神经网络反演海面风场的有效性.Abstract: A new algorithm about the sea surface wind field retrieval is presented based on neural network using NSCAT scatterometer data.The net work for retrieving wind speed is a multi-layer perceptron.The wind direction is derived using a mixture density net work which is composed of multi-layer perceptron and mixture density model with Gaussian kernel functions.The effectiveness of this kind of neural net work is demonst rated by comparing the result with ECMWF model wind and JMA buoy data.
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Key words:
- scatterometer /
- sea surface wind field retrieval /
- mixture density network
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