Evolving Ensembles

Paul J. Roebber University of Wisconsin–Milwaukee, Milwaukee, Wisconsin

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Abstract

An ensemble forecast method using evolutionary programming, including various forms of genetic exchange, disease, mutation, and the training of solutions within ecological niches, is presented. A 2344-member ensemble generated in this way is tested for 60-h minimum temperature forecasts for Chicago, Illinois.

The ensemble forecasts are superior in both ensemble average root-mean-square error and Brier skill score to those obtained from a 21-member operational ensemble model output statistics (MOS) forecast. While both ensembles are underdispersive, spread calibration produces greater gains in probabilistic skill for the evolutionary program ensemble than for the MOS ensemble. When a Bayesian model combination calibration is used, the skill advantage for the evolutionary program ensemble relative to the MOS ensemble increases for root-mean-square error, but decreases for Brier skill score. Further improvement in root-mean-square error is obtained when the raw evolutionary program and MOS forecasts are pooled, and a new Bayesian model combination ensemble is produced.

Future extensions to the method are discussed, including those capable of producing more complex forms, those involving 1000-fold increases in training populations, and adaptive methods.

Corresponding author address: Paul J. Roebber, Atmospheric Science Group, Department of Mathematical Sciences and School of Freshwater Sciences, University of Wisconsin–Milwaukee, 3200 North Cramer Ave., Milwaukee, WI 53211. E-mail: roebber@uwm.edu

Abstract

An ensemble forecast method using evolutionary programming, including various forms of genetic exchange, disease, mutation, and the training of solutions within ecological niches, is presented. A 2344-member ensemble generated in this way is tested for 60-h minimum temperature forecasts for Chicago, Illinois.

The ensemble forecasts are superior in both ensemble average root-mean-square error and Brier skill score to those obtained from a 21-member operational ensemble model output statistics (MOS) forecast. While both ensembles are underdispersive, spread calibration produces greater gains in probabilistic skill for the evolutionary program ensemble than for the MOS ensemble. When a Bayesian model combination calibration is used, the skill advantage for the evolutionary program ensemble relative to the MOS ensemble increases for root-mean-square error, but decreases for Brier skill score. Further improvement in root-mean-square error is obtained when the raw evolutionary program and MOS forecasts are pooled, and a new Bayesian model combination ensemble is produced.

Future extensions to the method are discussed, including those capable of producing more complex forms, those involving 1000-fold increases in training populations, and adaptive methods.

Corresponding author address: Paul J. Roebber, Atmospheric Science Group, Department of Mathematical Sciences and School of Freshwater Sciences, University of Wisconsin–Milwaukee, 3200 North Cramer Ave., Milwaukee, WI 53211. E-mail: roebber@uwm.edu
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