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基于航次和卫星观测的南海集合卡曼滤波资料同化研究; Applications of Ensemble Kalman Filter to hydrographic and satellite data in the South Chin Sea
刘大年
Subtype硕士
Thesis Advisor施平
2015
Degree Discipline物理海洋学
Keyword集合卡曼滤波 航次观测 卫星观测 适应性观测误差 温盐约束
Abstract集合卡曼滤波(EnKF)是一种强非线性的同化方法,它不用人为构建背景误差协方差矩阵,且不需伴随模式,因而近年来随着计算机计算能力的提高而高速发展。在实际应用中,EnKF同化经常遇到两个关键问题:样本离散度的维持(防止滤波发散)以及模式偏差校正。 针对上述两个问题,本文提出了一种观测误差适应的方法来防止滤波发散问题;同时,从背景误差协方差和温盐偏差关系入手,在同化中引入温盐控制来减小模式偏差对同化结果的影响。对于改进的同化方案进行了试验验证,开展了2008年夏季南海北部开放航次CTD的温盐廓线数据同化试验。并用卫星高度计观测数据,OSCAR流速数据,走航ADCP数据作为独立观测数据检验。结果证明新的EnKF同化方法能够有效的减小温盐均方根误差,同时改善高度场和流场的模拟。 其次,为了弥补温盐同化观测在时空上精度的不足,开展了2000年8月至2001年7月EnKF卫星观测资料的同化试验研究。为...
Other AbstractEnsembele Kalman filter (EnKF) is widely used recently, which has been proven its efficiency for strongly non-linear dynamical systems. One of the merits of the EnKF is that it can provide direct estimates of the forecast covariances from the forecast ensemble and then explicitly update that ensemble to be consistent with the uncertainty of the analysis. However, there are two major challenges for its application. One is how to maintain the model spread; the other is how to deal with the model bias. To solve above two questions, an adaptive observational error strategy is used to prevent filter from diverging. In the meantime, aiming at the limited improvement in some sites caused by the T and S biases in the model, a T-S constraint scheme is adopted to improve the assimilation performance...
Document Type学位论文
Identifierhttp://ir.scsio.ac.cn/handle/344004/14696
Collection学位论文(硕士)
Recommended Citation
GB/T 7714
刘大年. 基于航次和卫星观测的南海集合卡曼滤波资料同化研究, Applications of Ensemble Kalman Filter to hydrographic and satellite data in the South Chin Sea[D],2015.
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