Downscaling Near-surface Atmospheric Fields with Multi-objective Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Zerenner:2017:GECCO,
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author = "Tanja Zerenner and Victor Venema and
Petra Friederichs and Clemens Simmer",
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title = "Downscaling Near-surface Atmospheric Fields with
Multi-objective Genetic Programming",
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booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference Companion",
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series = "GECCO '17",
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year = "2017",
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isbn13 = "978-1-4503-4939-0",
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address = "Berlin, Germany",
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pages = "11--12",
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size = "2 pages",
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URL = "http://doi.acm.org/10.1145/3067695.3084375",
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DOI = "doi:10.1145/3067695.3084375",
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acmid = "3084375",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, SPEA,
atmospheric sciences, geosciences,
soil-vegetation-atmosphere system, spatial
variability",
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month = "15-19 " # jul,
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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}",
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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