Evaluating the feasibility of grammar-based GP in combining meteorological forecast models

The purpose of this paper is to evaluate the feasibility of grammatical evolution (GE) in combining meteorological models into more accurate single forecast of rainfall amount. A set of GE experiments was performed comparing six proposed ensemble forecast grammars on three benchmark problems. We also proposed a manner of designing benchmark problems by creating arbitrary combinations of meteorological models, as well as modeling the effect of weather patterns over models as explicit functions. The results showed that the GE algorithm obtained a superior performance relative to three traditional statistical methods for all the benchmark problems. A comparison among the developed grammars showed that our most complex grammar, which allows non-linear combinations of models and an unrestricted use of patterns, turned out to be the overall best performing proposal.