Extracting Robust Predictors From a Factor Field: An Empirically Optimal Screening Method - Lei Fan (Geophysical Research Letters)
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.