Application of the conditional nonlinear optimal perturbation method to the predictability study of the Kuroshio large meander
文献类型：期刊论文
| 作者 | Wang Qiang1,2,3; Mu Mu1,2; Dijkstra, Henk A.4 |
| 刊名 | ADVANCES IN ATMOSPHERIC SCIENCES
 |
| 出版日期 | 2012 |
| 卷号 | 29期号:1页码:118-134 |
| 关键词 | conditional nonlinear optimal perturbation Kuroshio large meander predictability model parameters |
| ISSN号 | 0256-1530 |
| 通讯作者 | Mu, M (reprint author), Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Wave, Qingdao 266071, Peoples R China. |
| 英文摘要 | A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations. The results show that the model was able to capture the essential features of these path variations. We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander (KLM) state using the conditional nonlinear optimal parameter perturbation (CNOP-P) method. Because of their relatively large uncertainties, three model parameters were considered: the interfacial friction coefficient, the wind-stress amplitude, and the lateral friction coefficient. We determined the CNOP-Ps optimized for each of these three parameters independently, and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 (SPG2) algorithm. Similarly, the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation (CNOP-I) method. Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days. But the prediction error caused by CNOP-I is greater than that caused by CNOP-P. The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored. Hence, to enhance the forecast skill of the KLM in this model, the initial conditions should first be improved, the model parameters should use the best possible estimates. |
| 学科主题 | Meteorology & Atmospheric Sciences |
| 收录类别 | SCI |
| 原文出处 | 10.1007/s00376-011-0199-0 |
| 语种 | 英语 |
| WOS记录号 | WOS:000298387000011 |
| 公开日期 | 2013-09-24 |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/12132]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 作者单位 | 1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Wave, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, Inst Atomspher Phys, State Key Lab Numer Modeling Atmospher Sci & Geop, Beijing 100029, Peoples R China 3.Chinese Acad Sci, Grad Univ, Beijing 100049, Peoples R China 4.Univ Utrecht, Dept Phys & Astron, Inst Marine & Atmospher Res Utrecht, NL-3584 CC Utrecht, Netherlands |
| 推荐引用方式 GB/T 7714 | Wang Qiang,Mu Mu,Dijkstra, Henk A.. Application of the conditional nonlinear optimal perturbation method to the predictability study of the Kuroshio large meander[J]. ADVANCES IN ATMOSPHERIC SCIENCES,2012,29(1):118-134. |
| APA | Wang Qiang,Mu Mu,&Dijkstra, Henk A..(2012).Application of the conditional nonlinear optimal perturbation method to the predictability study of the Kuroshio large meander.ADVANCES IN ATMOSPHERIC SCIENCES,29(1),118-134. |
| MLA | Wang Qiang,et al."Application of the conditional nonlinear optimal perturbation method to the predictability study of the Kuroshio large meander".ADVANCES IN ATMOSPHERIC SCIENCES 29.1(2012):118-134. |
入库方式： OAI收割
来源：海洋研究所
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