Extracting Robust Predictors From a Factor Field: An Empirically Optimal Screening Method
Abstract 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, cro...
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| Format: | Article |
| Language: | English |
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Wiley
2019-07-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2019GL083481 |
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| _version_ | 1849336554350182400 |
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| author | Lei Fan |
| author_facet | Lei Fan |
| author_sort | Lei Fan |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-29f9db5ee26243a4846f9a6ccef0d821 |
| institution | Kabale University |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2019-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-29f9db5ee26243a4846f9a6ccef0d8212025-08-20T03:44:57ZengWileyGeophysical Research Letters0094-82761944-80072019-07-0146148355836210.1029/2019GL083481Extracting Robust Predictors From a Factor Field: An Empirically Optimal Screening MethodLei Fan0Key Laboratory of Physical Oceanography/Collaborative Innovation Center of Marine Science and Technology Ocean University of China Qingdao ChinaAbstract 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.https://doi.org/10.1029/2019GL083481statstical predictioncross validationempirically optimalartifical skillpredictor screeningcorrelation map |
| spellingShingle | Lei Fan Extracting Robust Predictors From a Factor Field: An Empirically Optimal Screening Method Geophysical Research Letters statstical prediction cross validation empirically optimal artifical skill predictor screening correlation map |
| title | Extracting Robust Predictors From a Factor Field: An Empirically Optimal Screening Method |
| title_full | Extracting Robust Predictors From a Factor Field: An Empirically Optimal Screening Method |
| title_fullStr | Extracting Robust Predictors From a Factor Field: An Empirically Optimal Screening Method |
| title_full_unstemmed | Extracting Robust Predictors From a Factor Field: An Empirically Optimal Screening Method |
| title_short | Extracting Robust Predictors From a Factor Field: An Empirically Optimal Screening Method |
| title_sort | extracting robust predictors from a factor field an empirically optimal screening method |
| topic | statstical prediction cross validation empirically optimal artifical skill predictor screening correlation map |
| url | https://doi.org/10.1029/2019GL083481 |
| work_keys_str_mv | AT leifan extractingrobustpredictorsfromafactorfieldanempiricallyoptimalscreeningmethod |