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DOI | 10.1007/s00382-018-4255-7 |
Linear dynamical modes as new variables for data-driven ENSO forecast | |
Gavrilov, Andrey1; Seleznev, Aleksei1; Mukhin, Dmitry1; Loskutov, Evgeny1; Feigin, Alexander1; Kurths, Juergen1,2 | |
2019-02-01 | |
发表期刊 | CLIMATE DYNAMICS
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ISSN | 0930-7575 |
EISSN | 1432-0894 |
出版年 | 2019 |
卷号 | 52页码:2199-2216 |
文章类型 | Article |
语种 | 英语 |
国家 | Russia; Germany |
英文摘要 | A new data-driven model for analysis and prediction of spatially distributed time series is proposed. The model is based on a linear dynamical mode (LDM) decomposition of the observed data which is derived from a recently developed nonlinear dimensionality reduction approach. The key point of this approach is its ability to take into account simple dynamical properties of the observed system by means of revealing the system's dominant time scales. The LDMs are used as new variables for empirical construction of a nonlinear stochastic evolution operator. The method is applied to the sea surface temperature anomaly field in the tropical belt where the El Nino Southern Oscillation (ENSO) is the main mode of variability. The advantage of LDMs versus traditionally used empirical orthogonal function decomposition is demonstrated for this data. Specifically, it is shown that the new model has a competitive ENSO forecast skill in comparison with the other existing ENSO models. |
英文关键词 | Empirical modeling Data dimensionality reduction Nonlinear stochastic modeling ENSO forecast |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000460902200052 |
WOS关键词 | SEA-SURFACE TEMPERATURE ; PREDICTING CRITICAL TRANSITIONS ; PRINCIPAL COMPONENT ANALYSIS ; EL-NINO ; PACIFIC ; REDUCTION ; TELECONNECTIONS ; PREDICTABILITY ; DIMENSIONALITY ; NETWORKS |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/35951 |
专题 | 气候变化 |
作者单位 | 1.RAS, Inst Appl Phys, 46 Ulyanov Str, Nizhnii Novgorod 603950, Russia; 2.Potsdam Inst Climate Impact Res, Telegraphenberg A31, D-14473 Potsdam, Germany |
推荐引用方式 GB/T 7714 | Gavrilov, Andrey,Seleznev, Aleksei,Mukhin, Dmitry,et al. Linear dynamical modes as new variables for data-driven ENSO forecast[J]. CLIMATE DYNAMICS,2019,52:2199-2216. |
APA | Gavrilov, Andrey,Seleznev, Aleksei,Mukhin, Dmitry,Loskutov, Evgeny,Feigin, Alexander,&Kurths, Juergen.(2019).Linear dynamical modes as new variables for data-driven ENSO forecast.CLIMATE DYNAMICS,52,2199-2216. |
MLA | Gavrilov, Andrey,et al."Linear dynamical modes as new variables for data-driven ENSO forecast".CLIMATE DYNAMICS 52(2019):2199-2216. |
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