DOI: 10.1175/JCLI-D-13-00089.1
Scopus记录号: 2-s2.0-84888033879
论文题名: On the sensitivity of field reconstruction and prediction using empirical orthogonal functions derived from Gappy data
作者: Taylor M.H. ; Losch M. ; Wenzel M. ; Schröter J.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2013
卷: 26, 期: 22 起始页码: 9194
结束页码: 9205
语种: 英语
Scopus关键词: Empirical Orthogonal Function
; Interpolation schemes
; Principal components analysis
; Singular vectors
; Statistical techniques
; Covariance matrix
; Forecasting
; Interpolation
; Principal component analysis
; Orthogonal functions
; accuracy assessment
; chlorophyll a
; climate prediction
; concentration (composition)
; empirical analysis
; error analysis
; interpolation
; principal component analysis
; reconstruction
; remote sensing
; sensitivity analysis
; vector
英文摘要: Empirical orthogonal function (EOF) analysis is commonly used in the climate sciences and elsewhere to describe, reconstruct, and predict highly dimensional data fields. When data contain a high percentage of missing values (i.e., gappy), alternate approaches must be used in order to correctly derive EOFs. The aims of this paper are to assess the accuracy of several EOF approaches in the reconstruction and prediction of gappy data fields, using the Galapagos Archipelago as a case study example. EOF approaches included least squares estimation via a covariance matrix decomposition [least squares EOF (LSEOF)], data interpolating empirical orthogonal functions (DINEOF), and a novel approach called recursively subtracted empirical orthogonal functions (RSEOF). Model-derived data of historical surface chlorophyll-a concentrations and sea surface temperature, combined with a mask of gaps from historical remote sensing estimates, allowed for the creation of true and observed fields by which to gauge the performance of EOF approaches. Only DINEOF and RSEOF were found to be appropriate for gappy data reconstruction and prediction. DINEOF proved to be the superior approach in terms of accuracy, especially for noisy data with a high estimation error, although RSEOF may be preferred for larger data fields because of its relatively faster computation time. © 2013 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/51565
Appears in Collections: 气候变化事实与影响
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作者单位: Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany
Recommended Citation:
Taylor M.H.,Losch M.,Wenzel M.,et al. On the sensitivity of field reconstruction and prediction using empirical orthogonal functions derived from Gappy data[J]. Journal of Climate,2013-01-01,26(22)