[842] - Atmospheric Downscaling using Genetic Programming

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Zerenner, T., 2013. Atmospheric Downscaling using Genetic Programming. PhD Report, Meteorological Institute, University Bonn, Bonn, Germany. Accessed from https://www.tr32db.uni-koeln.de/data.php?dataID=842 at 2019-08-20.
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Title(s):Main Title: Atmospheric Downscaling using Genetic Programming
Description(s):Abstract: We have implemented the strength Pareto approach for multi-objective fitness assignment into the GPLAB package. In the first chapter of this report we provide an overview of the added functions and most important changes. In the second chapter we describe set-up and test of the Pareto GP with three objectives on the problem of downscaling nightly near-surface temperature fields. As training data we have used instantaneous model output fields (not means over a whole night as in the previous report) and a wider range of weather conditions including days with rain and precipitation. The first test of the Pareto based GP gives promising results. Adjustment and expansion of the multiple objectives might allow further improvements.
Responsible Party
Creator(s):Author: Tanja Zerenner
Publisher:CRC/TR32 Database (TR32DB)
TR32 Topic:Atmosphere
Subject(s):CRC/TR32 Keywords: PhD Report
File Details
File Name:report4_zerenner.pdf
Data Type:Text
File Size:5781 kB (5.646 MB)
Date(s):Available: 2014-02-18
Mime Type:application/pdf
Data Format:PDF
Download Permission:OnlyTR32
General Access and Use Conditions:For internal use only.
Access Limitations:For internal use only.
Licence:TR32DB Data policy agreement
North:-no map data
Measurement Region:Other
Measurement Location:--Other--
Specific Informations - Report
Report Date:28th of August, 2013
Report Type:PhD Report
Report City:Bonn, Germany
Report Institution:Meteorological Institute, University Bonn
Number Of Pages:8
Period of Pages:1 - 8
Further Informations:TR32 Student Report Phase II
Metadata Details
Metadata Creator:Tanja Zerenner
Metadata Created:2014-02-18
Metadata Last Updated:2014-02-18
Funding Phase:2
Metadata Language:English
Metadata Version:V40
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Dataset Downloads:5
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