Atmospheric Downscaling using Genetic Programming

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Title:Main Title: Atmospheric Downscaling using Genetic Programming
Description: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:Tanja Zerenner (Author)
Publisher:CRC/TR32 Database (TR32DB)
Publication Year:2014
Topic
TR32 Topic:Atmosphere
Related Subproject:C4
Subject:Keyword: PhD Report
File Details
Filename:report4_zerenner.pdf
Data Type:Text - Text
File Size:5.6 MB
Date:Available: 18.02.2014
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
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Download Permission:Only Project Members
General Access and Use Conditions:For internal use only.
Access Limitations:For internal use only.
Licence:[TR32DB] Data policy agreement
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Specific Information - 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 (1 - 8)
Further Information:TR32 Student Report Phase II
Metadata Details
Metadata Creator:Tanja Zerenner
Metadata Created:18.02.2014
Metadata Last Updated:18.02.2014
Subproject:C4
Funding Phase:2
Metadata Language:English
Metadata Version:V50
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