Scopus记录号: 2-s2.0-85047132344
论文题名: Linear dynamical modes as new variables for data-driven ENSO forecast
作者: Gavrilov A. ; Seleznev A. ; Mukhin D. ; Loskutov E. ; Feigin A. ; Kurths J.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2019
卷: 52, 期: 2019-03-04 起始页码: 2199
结束页码: 2216
语种: 英语
英文关键词: Data dimensionality reduction
; Empirical modeling
; ENSO forecast
; Nonlinear stochastic modeling
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. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature. 资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/122407
Appears in Collections: 气候变化事实与影响
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作者单位: Institute of Applied Physics of RAS, 46 Ul’yanov Str., Nizhny Novgorod, 603950, Russian Federation; Potsdam Institute for Climate Impact Research, Telegraphenberg A31, Potsdam, 14473, Germany
Recommended Citation:
Gavrilov A.,Seleznev A.,Mukhin D.,et al. Linear dynamical modes as new variables for data-driven ENSO forecast[J]. Climate Dynamics,2019-01-01,52(2019-03-04)