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In this presentation, we show how a simple evolutionary programming (EP) algorithm can optimize a given set of parameters in a mesoscale atmospheric model with respect to agreement between simulation and observations. This is illustrated using the Regional Atmospheric Modeling System (RAMS). As an initial test case, data from a RAMS simulation with a default set of parameters, rather than actual data, is used to create a single sounding as the 'observation'. The process begins with the random assignment of parameters to the model. The model is run, and the results are used to calculate an objective function based on the weighted rms error between the simulated and 'observed' soundings. Multiple runs are done with different parameter sets, and the sets with the lowest objective functions are selected to become 'parents' of the next set. Ideally, the model parameters will evolve toward those of the default simulation as the objective function is minimized. This type of experiment also tests the ability of EP to find the global minimum of the objective function since the optimum is known.
Our primary goal was to demonstrate that an EP algorithm can provide a systematic and objective method for optimizing a mesoscale atmospheric model. We are now working towards exploring the path the model follows as it is pushed towards its target, and how such a scheme could be applied operationally.