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源区黑潮流量季节性下降的可预报性和目标观测研究
张坤
学位类型博士
导师穆穆
2017-05-15
学位授予单位中国科学院大学
学位授予地点北京
学位专业物理海洋学
关键词源区黑潮 可预报性研究 目标观测研究 Cnop方法
摘要本文基于ROMS模式和条件非线性最优扰动(CNOP)方法开展了源区黑潮流量季节性下降的可预报性和目标观测研究,主要内容和结论如下:
首先,使用ROMS成功模拟了源区黑潮及其流量变化,建立了非线性优化系统并求解了条件非线性最优初始扰动(CNOP),考察了初始误差对源区黑潮流量季节性下降事件预报的影响及误差增长机制。结果表明:初始误差对源区黑潮流量预报有重要影响。CNOPs大值在水平方向位于在吕宋岛以东128°E附近,在垂向上则位于海洋上层1000米内。误差在预报时间段内快速增长,并最终以中尺度涡形式影响源区黑潮。斜压不稳定是误差快速增长的主要动力学机制。
其次,利用垂直积分能量方案识别了源区黑潮目标观测的最优敏感区OSen,确定了最优能量方案(总能量)和敏感区范围大小(占总水平格点数0.5%)。进一步地,设计敏感性试验验证了OSen的有效性,考察了初始误差空间位置和结构对源区黑潮流量预报的影响。结果表明,在OSen区域内的初始误差更易引起预报技巧的显著下降;其中,具有CNOP空间结构的初始误差导致最差预报结果。因此,在OSen内实施目标观测能更有效地提高源区黑潮流量的预报技巧。
最后,在不同设置下构建了随机观测网和适应性观测网,通过观测系统模拟试验(OSSEs)系统地评估了两类观测网在改善源区黑潮流量预报方面的有效性,并最终确定了最优适应性观测网。OSSEs结果表明,适应性观测网比随机观测网更有效,在适应性观测策略下存在最优观测设置方案。当观测站点数目为6或8并且观测站点间距为140 km或165 km时,适应性观测网在目标观测中有最佳表现。研究表明,最优适应性观测网可使得源区黑潮流量预报技巧平均提高超过40%。
本研究阐明了初始误差影响源区黑潮流量季节性下降预报不确定性的机制,揭示了斜压不稳定对误差快速增长的重要性,确定了源区黑潮目标观测敏感区和最优适应性观测网,能够为源区黑潮的数值模拟、业务预报与目标观测提供理论和方法指导。
其他摘要With the Regional Ocean Modeling System (ROMS), predictability of the seasonal reduction of the upstream Kuroshio transport (UKT) and its adaptive observation are studied by utilizing the conditional nonlinear optimal perturbation (CNOP) approach. Main conclusions are listed as follows:
Firstly, the upstream Kuroshio and its transport variation are well simulated by ROMS. To investigate the impacts of initial errors on UKT prediction and its growth mechanism, CNOPs are calculated through the nonlinear optimization system built with ROMS adjoint model. The results show that initial errors can significantly affect UKT prediction. The large-amplitudes of CNOPs are located around 128°E horizontally and in the upper 1000 m vertically. At the prediction time, CNOPs develop into eddy-like structures affecting the upstream Kuroshio. Meanwhile, the error-evolution shows two characteristics: westward propagation and fast growth. Further studies indicate that baroclinic instability is main reason causing the fast error-growth.
Secondly, the optimal sensitive area (OSen) of adaptive observation for predicting UKT variation is determined using the vertically integrated energy scheme, with eventually choosing total energy (TE) scheme and the sensitive area size as 0.5% of the model domain. Subsequently, sensitive experiments are conducted to evaluate the sensitivity of OSen and further investigate the impacts of spatial patterns and locations of initial errors on UKT prediction. The results show that initial errors in OSen tend to result in worse prediction results. Moreover, initial errors with CNOP-like patterns are more likely to cause larger prediction errors. Therefore, adaptive observation in OSen can improve UKT prediction more effectively.
Finally, random and adaptive observation networks with different observation settings are constructed and their effects on improving UKT prediction are evaluated by observation system simulation experiments (OSSEs). The results show that adaptive observation strategy is more effective than random observation strategy. The adaptive observation networks with six or eight observation sites and observation distance of 140 km or 165 km generally have the best performances. These optimal adaptive observation networks can improve UKT prediction by approximately 40%, with relatively higher observation efficiency and smaller prediction benefit deviations.
This study reveals the impacts of initial errors on UKT prediction and consturcts the optimal adaptive observation networks with appropriate observation parameters. It is expected that in the future the numerical simulation and forecast of the Kuroshio can benefit from the results provided above.
学科领域物理海洋学
语种中文
文献类型学位论文
条目标识符http://ir.qdio.ac.cn/handle/337002/136550
专题海洋环流与波动重点实验室
作者单位1.中国科学院大学
2.中国科学院海洋研究所
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张坤. 源区黑潮流量季节性下降的可预报性和目标观测研究[D]. 北京. 中国科学院大学,2017.
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