DOI: 10.1175/JCLI-D-13-00565.1
Scopus记录号: 2-s2.0-84899107978
论文题名: Reliability of regression-corrected climate forecasts
作者: Tippett M.K. ; Delsole T. ; Barnston A.G.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2014
卷: 27, 期: 9 起始页码: 3393
结束页码: 3404
语种: 英语
Scopus关键词: Face recognition
; Forecasting
; Parameter estimation
; Reliability
; Analytical description
; Estimated parameter
; Forecast probabilities
; Hypothesis testing
; Probability forecasts
; Regression parameters
; Reliability estimates
; Seasonal precipitations
; Regression analysis
; climate modeling
; climate prediction
; least squares method
; NOAA satellite
; precipitation (climatology)
; regression analysis
英文摘要: Regression is often used to calibrate climate model forecasts with observations. Reliability is an aspect of forecast quality that refers to the degree of correspondence between forecast probabilities and observed frequencies of occurrence. While regression-corrected climate forecasts are reliable in principle, the estimated regression parameters used in practice are affected by sampling error. The low skill and small sample sizes typically encountered in climate prediction imply substantial sampling error in the estimated regression parameters. Here the reliability of regression-corrected climate forecasts is analyzed for the case of joint-Gaussian distributed ensemble forecasts and observations with regression parameters estimated by least squares. Hypothesis testing of the regression parameters provides direct information about the skill and reliability of the uncorrected ensemble-based probability forecasts. However, the regression-corrected probability forecasts with estimated parameters are systematically "overconfident"because sampling error causes a positive bias in the regression forecast signal variance, despite the fact that the estimates of the regression parameters are themselves unbiased. An analytical description of the reliability diagram of a generic regression-corrected climate forecast is derived and is shown to depend on sample size and population correlation skill, with small sample size and low skill being factors that increase overconfidence. The analytical reliability estimate is shown to capture the effect of sampling error in synthetic data experiments and in a 29-yr dataset of NOAA Climate Forecast System version 2 predictions of seasonal precipitation totals over the Americas. The impact of sampling error on the reliability of regression-corrected forecast has been previously unrecognized and affects all regression-based forecasts. The use of regression parameters estimated by shrinkage methods such as ridge regression substantially reduces overconfidence. © 2014 American Meteorological Society. 资助项目: NSF, National Aeronautics and Space Administration
; NOAA, National Aeronautics and Space Administration
; NOAA, National Aeronautics and Space Administration
; NOAA, National Aeronautics and Space Administration
; 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/51148
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY, United States; Center of Excellence for Climate Change Research, Department of Meteorology, King Abdulaziz University, Jeddah, Saudi Arabia; George Mason University, Fairfax, VA, United States; Center for Ocean-Land-Atmosphere Studies, Calverton, MD, United States; International Research Institute for Climate and Society, Palisades, NY, United States
Recommended Citation:
Tippett M.K.,Delsole T.,Barnston A.G.. Reliability of regression-corrected climate forecasts[J]. Journal of Climate,2014-01-01,27(9)