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

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@Article{Zerenner:2016:EMS,

• author = "Tanja Zerenner and Victor Venema and Petra Friederichs and Clemens Simmer",
• title = "Downscaling near-surface atmospheric fields with multi-objective Genetic Programming",
• journal = "Environmental Modelling \& Software",
• year = "2016",
• volume = "84",
• number = "Supplement C",
• pages = "85--98",
• keywords = "genetic algorithms, genetic programming, Statistical downscaling, Disaggregation, Evolutionary computation, Machine learning, Pareto optimality, Coupled modelling",
• ISSN = "1364-8152",
• URL = "http://www.sciencedirect.com/science/article/pii/S1364815216302122",
• DOI = "doi:10.1016/j.envsoft.2016.06.009",
• abstract = "We present a new Genetic Programming based method to derive downscaling rules (i.e., functions or short programs) generating realistic high-resolution fields of atmospheric state variables near the surface given coarser-scale atmospheric information and high-resolution information on land surface properties. Such downscaling rules can be applied in coupled subsurface-land surface-atmosphere simulations or to generate high-resolution atmospheric input data for off-line applications of land surface and subsurface models. Multiple features of the high-resolution fields, such as the spatial distribution of subgrid-scale variance, serve as objectives. The downscaling rules take an interpretable form and contain on average about 5 mathematical operations. The method is applied to downscale ten m-temperature fields from 2.8km to 400m grid resolution. A large part of the spatial variability is reproduced, also in stable night time situations, which generate very heterogeneous near-surface temperature fields in regions with distinct topography",
• notes = "See also \cite{journals/corr/ZerennerVFS14}. Also known as \cite{ZERENNER201685}",

}

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

Citations