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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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