IOCAS-IR  > 海洋环流与波动重点实验室
Thesis Advisor穆穆
Degree Grantor中国科学院大学
Place of Conferral北京
Degree Discipline物理海洋学
Keyword源区黑潮 可预报性研究 目标观测研究 Cnop方法
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%。
Other AbstractWith 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.
Subject Area物理海洋学
Document Type学位论文
Recommended Citation
GB/T 7714
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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