Downscaling near-surface atmospheric fields with multi-objective Genetic Programming
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- @Article{Zerenner:2016:EMS,
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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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journal = "Environmental Modelling \& Software",
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year = "2016",
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volume = "84",
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number = "Supplement C",
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pages = "85--98",
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keywords = "genetic algorithms, genetic programming, Statistical
downscaling, Disaggregation, Evolutionary computation,
Machine learning, Pareto optimality, Coupled
modelling",
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ISSN = "1364-8152",
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URL = "http://www.sciencedirect.com/science/article/pii/S1364815216302122",
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DOI = "doi:10.1016/j.envsoft.2016.06.009",
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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",
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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