DOI: 10.1175/JCLI-D-16-0249.1
Scopus记录号: 2-s2.0-85012281820
论文题名: Statistical seasonal prediction based on regularized regression
作者: DelSole T. ; Banerjee A.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2017
卷: 30, 期: 4 起始页码: 1345
结束页码: 1361
语种: 英语
Scopus关键词: Atmospheric temperature
; Constrained optimization
; Forecasting
; Oceanography
; Optimization
; Principal component analysis
; Surface waters
; Forecasting techniques
; Principal components analysis
; Seasonal forecasting
; Statistical forecasting
; Statistical techniques
; Regression analysis
; climate prediction
; principal component analysis
; regression analysis
; sea surface temperature
; seasonal variation
; teleconnection
; Pacific Ocean
; Texas
; United States
英文摘要: This paper proposes a regularized regression procedure for finding a predictive relation between one variable and a field of other variables. The procedure estimates a linear prediction model under the constraint that the regression coefficients have smooth spatial structure. The smoothness constraint is imposed using a novel approach based on the eigenvectors of the Laplace operator over the domain, which results in a constrained optimization problem equivalent to either ridge regression or least absolute shrinkage and selection operator (LASSO) regression, which can be solved by standard numerical software. In addition, this paper explores an unconventional procedure whereby regression models are estimated from dynamical model output and then verified against observations-the reverse of the traditional order. The methodology is illustrated by constructing statistical prediction models of summer Texas-area temperature based on concurrent Pacific sea surface temperature (SST). None of the regularized regression models have statistically significant skill when estimated from observations. In contrast, when estimated from dynamical model output, the regression models have skill with respect to dynamical model data because of the substantially larger sample size available from dynamical model output. In addition, the regression models estimated from dynamical model data can predict observed anomalies with significant skill, even though no observations were used directly to estimate the regression models. The results indicate that dynamical models had no significant skill because they could not accurately predict the SST itself, not because they could not capture realistic SST teleconnections. © 2017 American Meteorological Society. 资助项目: NSF, National Science Foundation
; NSF, National Science Foundation
; NSF, National Science Foundation
; NASA, National Aeronautics and Space Administration
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/49857
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Atmospheric, Ocean and Earth Sciences, George Mason University, Fairfax, VA, United States; Center for Ocean-Land-Atmosphere Studies, George Mason University, Fairfax, VA, United States; Department of Computer Science and Engineering, University of Minnesota, Twin Cities, MN, United States
Recommended Citation:
DelSole T.,Banerjee A.. Statistical seasonal prediction based on regularized regression[J]. Journal of Climate,2017-01-01,30(4)