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Genetic ensemble (G-Ensemble) for meteorological prediction enhancement

Ihshaish, Hisham; Cortes, Ana; Senar, Miquel

Authors

Ana Cortes

Miquel Senar



Contributors

Hamid R. Arabnia
Editor

Abstract

The need for reliable predictions in environmental modelling is long known. Particularly, the predicted weather and meteorological information about the future atmospheric state is crucial and necessary for almost all other areas of environmental modelling. Additionally, right decisions to prevent damages and save lives could be taken depending on a reliable meteorological prediction process. Lack and uncertainty of input data and parameters constitute the main source of errors for most of these models. In recent years, evolutionary optimisation methods have become popular to solve the input parameter problem of environmental models. We propose a new prediction scheme that uses a Genetic Algorithm for parameter estimation in Numerical Weather Prediction Models (NWP) to enhance prediction results. The new approach is called Genetic Ensemble (G- Ensemble) and it has been tested using historical data of a well known weather catastrophe: Hurricane Katrina that occurred in 2005 in the Gulf of Mexico. Obtained results provide significant improvements in weather prediction.

Citation

Ihshaish, H., Cortes, A., & Senar, M. (2011, July). Genetic ensemble (G-Ensemble) for meteorological prediction enhancement. Paper presented at The 2011 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA2011)

Presentation Conference Type Conference Paper (unpublished)
Conference Name The 2011 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA2011)
Start Date Jul 18, 2011
End Date Jul 21, 2011
Publication Date Jan 1, 2011
Peer Reviewed Peer Reviewed
Keywords numerical weather prediction, evolutionary computing, genetic algorithm, ensemble prediction, parameter estimation
Additional Information Title of Conference or Conference Proceedings : Proceedings of The 2011 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA2011)

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