Improving WRF-ARW Wind Speed Predictions using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{conf/sgai/Martinez-ArellanoNB12,
  author =       "Giovanna Martinez-Arellano and Lars Nolle and 
                 John A. Bland",
  title =        "Improving {WRF-ARW} Wind Speed Predictions using
                 Genetic Programming",
  booktitle =    "Research and Development in Intelligent Systems
                 {XXIX}",
  year =         "2012",
  editor =       "Max Bramer and Miltos Petridis",
  pages =        "347--360",
  address =      "Cambridge, UK",
  month =        dec # " 11-13",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4471-4739-8",
  URL =          "http://dx.doi.org/10.1007/978-1-4471-4739-8",
  DOI =          "doi:10.1007/978-1-4471-4739-8_27",
  language =     "English",
  bibdate =      "2013-01-22",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/sgai/sgai2012.html#Martinez-ArellanoNB12",
  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.",
  notes =        "SGAI Conf. Incorporating Applications and Innovations
                 in Intelligent Systems XX Proceedings of AI-2012, The
                 Thirty-second SGAI International Conference on
                 Innovative Techniques and Applications of Artificial
                 Intelligence",
}

Genetic Programming entries for Giovanna Martinez-Arellano Lars Nolle John A Bland

Citations