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Alternative TitleThe Study on Oceanic Vector Wind Field Retrieve Technique based on Neural Networks of Microwave Scatterometer
陈坤堂; 董晓龙; 徐星欧; 郎姝燕
Source Publication遥感技术与应用
Keyword微波散射计 海面风场反演 神经网络 中高风速 海洋二号卫星(Hy-2)
Abstract研究利用神经网络方法处理微波散射计数据,反演海面风场。重点研究海洋二号(HY-2)卫星微波散射计数据反演,特别是中高风速条件下的风场反演。其中风速的反演基于后向传播(Back Propagation,BP)神经网络;多解风向的反演基于混合密度(Mixture Density Network, MDN)神经网络,求解过程中的核函数采用高斯分布;网络训练的目标风场采用欧洲中期天气预报中心(European Centre for Medium-range Weather Foresting,ECMWF)模式风场。通过与ECMWF风场的比较,利用神经网络方法反演的风场可以满足HY-2微波散射计风场反演的精度要求。同时通过与国家卫星海洋应用中心发布的HY-2微波散射计L2B级风场产品相比较,表明该方法反演的风场更接近ECMWF模式风场。
Other AbstractThe neural networks are used to retrieve wind fields for microwave scatterometer data,especially for data gained by the scatterometer onboard HY-2Asatellite(HSCAT)under high wind speed conditions. The retrieval of wind speed is based on Back Propagation(BP)neural network,while multiple solutions of wind direction inversion is realized by Mixture Density Network(MDN)neural network.During the process,Gaussian kernel function is employed.The wind field used in network training is from corresponding European Centre for Medium-range Weather Foresting(ECMWF).It is proved that wind fields retrieved in this paper could get results meeting the accuracy requirement for HSCAT by comparison with ECMWF wind fields.Results are also compared with the L2Bwind field products distributed by the National Satellite Oceanic Application Service,it is shown that the method in this paper gave results with closer values than L2Bproducts.
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Cited Times:3[CSCD]   [CSCD Record]
Document Type期刊论文
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GB/T 7714
陈坤堂,董晓龙,徐星欧,等. 微波散射计反演海面风场的神经网络方法研究[J]. 遥感技术与应用,2017,32(4):683-690.
APA 陈坤堂,董晓龙,徐星欧,&郎姝燕.(2017).微波散射计反演海面风场的神经网络方法研究.遥感技术与应用,32(4),683-690.
MLA 陈坤堂,et al."微波散射计反演海面风场的神经网络方法研究".遥感技术与应用 32.4(2017):683-690.
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