Downscaling Near-surface Atmospheric Fields with Multi-objective Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4067

@InProceedings{Zerenner:2017:GECCO,
  author =       "Tanja Zerenner and Victor Venema and 
                 Petra Friederichs and Clemens Simmer",
  title =        "Downscaling Near-surface Atmospheric Fields with
                 Multi-objective Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "11--12",
  size =         "2 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3084375",
  DOI =          "doi:10.1145/3067695.3084375",
  acmid =        "3084375",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, SPEA,
                 atmospheric sciences, geosciences,
                 soil-vegetation-atmosphere system, spatial
                 variability",
  month =        "15-19 " # jul,
  abstract =     "Coupled models of the soil-vegetation-atmosphere
                 systems are increasingly used to investigate
                 interactions between the system components. Due to the
                 different spatial and temporal scales of relevant
                 processes and computational restrictions, the
                 atmospheric model generally has a lower spatial
                 resolution than the land surface and subsurface models.
                 We employ multi-objective Genetic Programming (MOGP)
                 using the Strength Pareto Evolutionary Algorithm (SPEA)
                 to bridge this scale gap. We generate high-resolution
                 atmospheric fields using the coarse atmospheric model
                 output and high-resolution land surface information
                 (e.g., topography) as predictors. High-resolution
                 atmospheric simulations serve as reference. It is
                 impossible to perfectly reconstruct the reference
                 fields with the available information. Thus, we
                 simultaneously optimize the root mean square error
                 (RMSE) and two objective functions quantifying spatial
                 variability. Minimization solely with respect to the
                 RMSE provides too smooth high-resolution fields.
                 Additional objectives help to recover spatial
                 variability. We apply MOGP to the downscaling of 10 m
                 temperature. Our approach reproduces a larger part of
                 the variability and is applicable for a wider range of
                 weather conditions than a linear regression based
                 downscaling. Original publication: T. Zerenner, V.
                 Venema, P. Friederichs, and C. Simmer. Downscaling
                 near-surface atmospheric fields with multiobjective
                 Genetic Programming. Environmental Modelling and
                 Software, 84(2016), 85--98. \cite{Zerenner:2016:EMS}",
  notes =        "Also known as \cite{Zerenner:2017:DNA:3067695.3084375}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for Tanja Zerenner Victor Venema Petra Friederichs Clemens Simmer

Citations