GSTDTAP  > 气候变化
DOI10.1029/2019GL083481
Extracting Robust Predictors From a Factor Field: An Empirically Optimal Screening Method
Fan, Lei1,2
2019-07-28
发表期刊GEOPHYSICAL RESEARCH LETTERS
ISSN0094-8276
EISSN1944-8007
出版年2019
卷号46期号:14页码:8355-8362
文章类型Article
语种英语
国家Peoples R China
英文摘要

Extracting predictors from a predictand-predictor correlation map is a common problem for climate prediction, but its skill is affected by sampling errors and the subjective selection of predictors; hence, it is difficult to ensure that the selected predictors are optimal. Additionally, cross validation tends to overestimate the actual prediction skill because of artificial skill. In view of these problems, the author proposes an empirically optimal screening (EOS) method to extract predictors from a correlation map. Based on hindcast cross validation, EOS empirically and objectively identifies an optimal correlation threshold for data screening. To mitigate artificial skill, cross validation completely separates the training and testing samples, not only for parameter fitting but also prior predictor selection. By using EOS, researchers avoid subjectively determining predictors directly from correlation maps, and EOS further refines potential predictors before the verification of physical mechanisms.


Plain Language Summary Statistical methods are commonly used in climate prediction. Researchers often calculate a correlation map between the target and a predictor field then subjectively select some highly correlated areas as predictors and verify their physical links to the target using numerical experiments. However, such a procedure requires researchers with deep physical insight and it cannot ensure that the selected predictors are optimal. Additionally, the prediction skill tends to be overestimated because of the issue of artificial skills. In view of these problems, the author proposes an empirically optimal screening (EOS) method to objectively extract predictors from a correlation map. EOS is a further process beyond the correlation map, and it provides a statistically optimal model for a forecaster's reference before the verification of physical mechanisms, thereby avoiding the subjectivity involved in selecting predictors directly from a correlation map.


英文关键词statstical prediction cross validation empirically optimal artifical skill predictor screening correlation map
领域气候变化
收录类别SCI-E
WOS记录号WOS:000481818900054
WOS关键词INDIAN MONSOON ; VARIABILITY ; RAINFALL
WOS类目Geosciences, Multidisciplinary
WOS研究方向Geology
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/185317
专题气候变化
作者单位1.Ocean Univ China, Collaborat Innovat Ctr Marine Sci & Technol, Key Lab Phys Oceanog, Qingdao, Shandong, Peoples R China;
2.Ocean Univ China, Coll Ocean & Atmospher Sci, Qingdao, Shandong, Peoples R China
推荐引用方式
GB/T 7714
Fan, Lei. Extracting Robust Predictors From a Factor Field: An Empirically Optimal Screening Method[J]. GEOPHYSICAL RESEARCH LETTERS,2019,46(14):8355-8362.
APA Fan, Lei.(2019).Extracting Robust Predictors From a Factor Field: An Empirically Optimal Screening Method.GEOPHYSICAL RESEARCH LETTERS,46(14),8355-8362.
MLA Fan, Lei."Extracting Robust Predictors From a Factor Field: An Empirically Optimal Screening Method".GEOPHYSICAL RESEARCH LETTERS 46.14(2019):8355-8362.
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