摘要
本文基于Jason-2高度计数据,在12个不同季节的cycle数据中组合1~6个cycle的有效波高、风速和海况偏差为训练集,选取Jason-2的另外3个不同季节的cycle数据集为测试集。经检验分析,确定3个cycle对应的BP神经网络模型。将该模型应用于HY-2高度计海况偏差的估计,通过海况偏差与有效波高及风速的拟合优度、解释方差和残差对比分析,结果表明:神经网络BP模型可以有效应用于HY-2的海况偏差估计并明显优于传统海况偏差参数模型。
In this paper,which is based on the Jason-2altimeter data,with the data of the significant wave height（SWH）,wind speed（U）and sea state bias（SSB）combination of 1-6cycle in 12 different seasons in the cycle data as the training set,select the other 3cycles of Jason-2data as the test set.By the test analysis,the BP neural network model which corresponds to 3cycles for estimating the SSBis established.The model is applied to the estimations of SSBin the HY-2altimeter,and the performances of the model can be evaluated by the goodness of fit between Uand SWH by SSB,explained variance and residual contrast analysis.It suggests that the BP neutral network model can be effectively applied to the HY-2estimations of SSBand significantly better than the traditional parameter model of sea state bias.
出处
《海洋学报》
CAS
CSCD
北大核心
2017年第7期124-130,共7页
基金
国家自然科学基金"雷达高度计海况偏差校正综合模型研究"(41176157)
国家自然科学青年基金"降雨条件下HY-2高度计有效波高反演技术研究"(41406197)
海洋环境安全保障重点专项"三维成像雷达高度计海洋信息提取技术及应用(2016YFC1401004)
