DOI: 10.1175/2011JCLI4143.1
Scopus记录号: 2-s2.0-84858613589
论文题名: Quantifying the predictive skill in long-range forecasting. Part I: Coarse-grained predictions in a simple ocean model
作者: Giannakis D. ; Majda A.J.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2012
卷: 25, 期: 6 起始页码: 1793
结束页码: 1813
语种: 英语
Scopus关键词: Barotropic
; Coarse Graining
; Coarse-grained
; Empirical Orthogonal Function
; Ensemble prediction
; Ensembles
; Ergodic signals
; Gyres
; Jet configuration
; K-means clustering
; Lead time
; Long-range forecasting
; Mixing dynamics
; Ocean circulation
; Ocean model
; Optimal partitions
; Partition algorithms
; Phase spaces
; Principal Components
; Probability forecasts/models/distribution
; Relative entropy
; Streamfunctions
; Three-state models
; Climatology
; Information theory
; Jets
; Phase space methods
; Principal component analysis
; Forecasting
; algorithm
; barotropic motion
; empirical analysis
; ensemble forecasting
; gyre
; jet
; long range forecast
; oceanic circulation
; principal component analysis
; probability
; signal processing
; weather forecasting
英文摘要: An information-theoretic framework is developed to assess the long-range coarse-grained predictive skill in a perfect-model environment. Central to the scheme is the notion that long-range forecasting involves regimes; specifically, that the appropriate initial data for ensemble prediction is the affiliation of the system to a coarse-grained partition of phase space representing regimes. The corresponding ensemble prediction probabilities, which are computable using ergodic signals from the model, are then used to quantify through relative entropy the information beyond climatology in the partition. As an application, the authors study the predictability of circulation regimes in an equivalent barotropic double-gyre ocean model using a partition algorithm based on K-means clustering and running-average coarse graining. Besides the established rolled up and extensional phases of the eastward jet, optimal partitions for triennial-scale forecasts feature a jet configuration dominated by the second empirical orthogonal function (EOF) of the streamfunction, as well as phases in which the jet interacts with eddies in higher EOFs. Due to mixing dynamics, the skill beyond threestate models is lost for forecast lead times longer than three years, but significant skill remains in the energy and the leading principal component of the streamfunction for septennial forecasts. © 2012 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/52511
Appears in Collections: 气候变化事实与影响
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作者单位: Courant Institute of Mathematical Sciences, New York University, New York, NY, United States
Recommended Citation:
Giannakis D.,Majda A.J.. Quantifying the predictive skill in long-range forecasting. Part I: Coarse-grained predictions in a simple ocean model[J]. Journal of Climate,2012-01-01,25(6)