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Adaptive Evolutionary Programming

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  • 1 Atmospheric Science Group, Department of Mathematical Sciences, and School of Freshwater Sciences, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin
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Abstract

Previous work has shown that evolutionary programming is an effective method for constructing skillful forecast ensembles. Here, two prototype adaptive methods are developed and tested, using minimum temperature forecast data for Chicago, Illinois, to determine whether they are capable of incorporating improvements to forecast inputs (as might occur with changes to operational forecast models and data assimilation methods) and to account for short-term changes in predictability (as might occur for particular flow regimes). Of the two methods, the mixed-mode approach, which uses a slow mode to evolve the overall ensemble structure and a fast mode to adjust coefficients, produces the best results. When presented with better operational guidance, the mixed-mode method shows a reduction of 0.57°F in root-mean-square error relative to a fixed evolutionary program ensemble. Several future investigations are needed, including the optimization of training intervals based on flow regime and improvements to the adjustment of fast-mode coefficients. Some remarks on the appropriateness of this method for other ensemble forecast problems are also provided.

Corresponding author address: Paul J. Roebber, Department of Mathematical Sciences, University of Wisconsin–Milwaukee, P.O. Box 413, Milwaukee, WI 53201-0413. E-mail: roebber@uwm.edu

Abstract

Previous work has shown that evolutionary programming is an effective method for constructing skillful forecast ensembles. Here, two prototype adaptive methods are developed and tested, using minimum temperature forecast data for Chicago, Illinois, to determine whether they are capable of incorporating improvements to forecast inputs (as might occur with changes to operational forecast models and data assimilation methods) and to account for short-term changes in predictability (as might occur for particular flow regimes). Of the two methods, the mixed-mode approach, which uses a slow mode to evolve the overall ensemble structure and a fast mode to adjust coefficients, produces the best results. When presented with better operational guidance, the mixed-mode method shows a reduction of 0.57°F in root-mean-square error relative to a fixed evolutionary program ensemble. Several future investigations are needed, including the optimization of training intervals based on flow regime and improvements to the adjustment of fast-mode coefficients. Some remarks on the appropriateness of this method for other ensemble forecast problems are also provided.

Corresponding author address: Paul J. Roebber, Department of Mathematical Sciences, University of Wisconsin–Milwaukee, P.O. Box 413, Milwaukee, WI 53201-0413. E-mail: roebber@uwm.edu
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