Open AccessUsing Evolutionary Programming to Generate Skillful Extreme Value Probabilistic Forecasts
Author(s) -
Paul J. Roebber
Publication year - 2013
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/mwr-d-12-00285.1
Subject(s) - probabilistic logic , computer science , ensemble forecasting , range (aeronautics) , limit (mathematics) , imperfect , dynamical systems theory , lorenz system , evolutionary programming , evolutionary algorithm , extreme value theory , artificial intelligence , machine learning , mathematics , statistics , chaotic , physics , mathematical analysis , linguistics , philosophy , materials science , quantum mechanics , composite material
A new method for producing large member ensemble forecasts, using a variation of evolutionary programming (EP), is presented. A series of increasingly complex datasets are used to demonstrate the method and its potential utility. First, EP performance is considered using training and test “atmospheric” data derived from the Lorenz low-order dynamical system. Next, a modified form of the intermediate-order Lorenz model representing 500-hPa height is used. Finally, EP performance is evaluated using real 500-hPa data and day-3 forecasts of the reforecast model. As expected, short observational records limit the potential of the EP method by preventing proper training. A kind of perfect-prog approach, in which the EP ensemble is trained using a large “observational” sample constructed from the imperfect model, is shown to be a potentially viable means of counteracting limits to the observed record. The EP ensembles are shown to outperform dynamical model ensembles at the extremes, and to be competitive with dynamical models across a wide range of values of the variables of interest. In particular, the EP ensembles improve resolution compared to dynamical model ensembles, which suggests that further skill might be obtainable by also improving reliability using additional postprocessing. Future applications of the ensemble EP method are briefly discussed.