Abstract
Numerical weather prediction models can produce wind speed forecasts at a very high space resolution. However, running these models with that amount of precision is time and resource consuming. In this paper, the integration of the Weather Research and Forecasting – Advanced Research WRF (WRF-ARW) mesoscale model with four different downscaling approaches is presented. Three of the proposed methods are mathematical based approaches that need a predefined model to be applied. The fourth approach, based on genetic programming (GP), will implicitly find the optimal model to downscale WRF forecasts, so no previous assumptions about the model need to be made. WRFARW forecasts and observations at three different sites of the state of Illinois in the USA are analysed before and after applying the downscaling techniques. Results have shown that GP is able to successfully downscale the wind speed predictions, reducing significantly the inherent error of the numerical models.
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Martinez-Arellano, G., Nolle, L., Bland, J. (2012). Improving WRF-ARW Wind Speed Predictions using Genetic Programming. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXIX. SGAI 2012. Springer, London. https://doi.org/10.1007/978-1-4471-4739-8_27
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DOI: https://doi.org/10.1007/978-1-4471-4739-8_27
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