Downscaling near-surface atmospheric fields with multi-objective Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Misc{journals/corr/ZerennerVFS14,
  author =       "Tanja Zerenner and Victor Venema and 
                 Petra Friederichs and Clemens Simmer",
  title =        "Downscaling near-surface atmospheric fields with
                 multi-objective Genetic Programming",
  year =         "2014",
  keywords =     "genetic algorithms, genetic programming",
  volume =       "abs/1407.1768",
  bibdate =      "2014-08-01",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/corr/corr1407.html#ZerennerVFS14",
  URL =          "http://arxiv.org/abs/1407.1768",
  abstract =     "The coupling of models for the different components of
                 the Soil-Vegetation-Atmosphere-System is required to
                 investigate component interactions and feedback
                 processes. However, the component models for
                 atmosphere, land-surface and subsurface are usually
                 operated at different resolutions in space and time
                 owing to the dominant processes. The computationally
                 often more expensive atmospheric models, for instance,
                 are typically employed at a coarser resolution than
                 land-surface and subsurface models. Thus up- and
                 downscaling procedures are required at the interface
                 between the atmospheric model and the
                 land-surface/subsurface models. We apply
                 multi-objective Genetic Programming (GP) to a training
                 data set of high-resolution atmospheric model runs to
                 learn equations or short programs that reconstruct the
                 fine-scale fields (e.g., 400 m resolution) of the
                 near-surface atmospheric state variables from the
                 coarse atmospheric model output (e.g., 2.8 km
                 resolution). Like artificial neural networks, GP can
                 flexibly incorporate multivariate and nonlinear
                 relations, but offers the advantage that the solutions
                 are human readable and thus can be checked for physical
                 consistency. Using the Strength Pareto Approach for
                 multi-objective fitness assignment allows us to
                 consider multiple characteristics of the fine-scale
                 fields during the learning procedure",
}

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

Citations