The process of weather forecasting produced by numerical weather prediction (NWP) models is complex and not always accurate. Moreover, it is generally defined by its very nature as a process that has to deal with uncertainties. In previous works, a new weather prediction scheme, Genetic Ensemble (G-Ensemble), was presented, which uses evolutionary computing methods. Particularly, it uses Genetic Algorithms (GA) in order to find the most timely 'optimal' values of model closure parameters that appear in physical parametrization schemes, which are coupled with NWP models. The presented scheme showed significant improvement of weather prediction quality and, moreover, the waiting time for an enhanced weather prediction result was reduced by executing a parallel G-Ensemble scheme over HPC platforms. In this work, however, we test the same scheme with different GA configurations regarding its Crossover type and ratio, and by variating its initial population size in order to get better predictions. The main concern behind this work is to provide a more detailed study on how the GA used in G-Ensemble scheme could be tuned depending on the available computational resources in operational scenarios. Finally, experimental results are discussed of a weather prediction case using historical data of a well known weather catastrophe: Hurricane Katrina that occurred in 2005 in the Gulf of Mexico. Obtained results provide significant enhancement in weather prediction.
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