The application of a genetic algorithm to the optimization of a mesoscale meteorological mode

Improvement in mesoscale atmospheric model simulations is generally thought to be primarily a matter of finer spatial resolution. While this is often true, there is a limit to the improvement one can obtain by simply decreasing the grid size of a numerical model. Further improvements in forecasts can be achieved with better model parameterizations, but this leaves the mesoscale modeler with the task of determining which parameterizations; to use for a specific problem and what values to use for individual model parameters. The accuracy of a given numerical simulation is often a matter of a judicious choice of these values. In the following, we demonstrate that a simple genetic algorithm can perform this parameter optimization for a mesoscale atmospheric model. Computational requirements and behavior of the optimization algorithm are discussed.