项目名称: 模式参数误差对黑潮路径变异预报的影响
项目编号: No.41306023
项目类型: 青年科学基金项目
立项/批准年度: 2014
项目学科: 天文学、地球科学
项目作者: 王强
作者单位: 中国科学院海洋研究所
项目金额: 26万元
中文摘要: 本项目拟基于多层斜压模式,利用条件非线性最优参数扰动(CNOP-P)方法,深入研究模式参数误差对黑潮路径变异预报的非线性影响及其机理。主要包含两个方面:(1)利用CNOP-P方法,考察导致黑潮路径变异最大预报误差的参数误差模态,并探讨该参数误差引起的预报误差的非线性发展过程,研究模式参数误差对黑潮路径变异预报结果的影响;(2)分析不同物理过程,尤其是斜压过程在参数误差引起的预报误差发展中的作用,探讨影响预报误差发展的关键物理过程,揭示模式参数误差对黑潮路径变异预报影响的物理机制,为发展和改进模式,进而提高黑潮路径变异的预报技巧提供科学的指导。
中文关键词: 黑潮;模式参数;可预报性;不确定性;
英文摘要: In this study, the conditional nonlinear optimal parameter perturbation (CNOP-P) approach will be used to investigate the nonlinear effects of model parameter errors on the prediction of the Kuroshio path variations using a multi-layer baroclinic model, and also the mechanisms. This project will cover the following two main aspects: (1) The CNOP-P method is used to examine the mode of parameter errors which results in the largest forecast error of the Kuroshio path variations. The nonlinear evolution of the forecast error caused by the model parameter errors and the effects of model parameter errors on the forecast will be investigated. (2) The roles of different physical processes, especially the baroclinic processes on the forecast error developments caused by the model parameter errors will be analyzed. The key physical processes, which affect the forecast error developments, will be explored and the physical mechanism of the influences of model parameter errors on the prediction of the Kuroshio path variations will be revealed. This study will provide a scientific guidance for improving the model and enhancing the forecast skills of Kuroshio path variations.
英文关键词: Kuroshio;model parameter;predictability;uncertainty;