Atmospheric Downscaling using Genetic Programming

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Title:Main Title: Atmospheric Downscaling using Genetic Programming
Description:Abstract: We are using a machine learning approach, Genetic Programming (GP), for discovering downscaling rules from a training data set (high-resolution model runs). This report covers our rst steps to adapting GP to our downscaling problem. Exact reproduction of the ne-scale elds from the coarse data will not be possible. We introduce multi-objective tness functions aiming at reproducing a 'realistic structure' rather than the exact eld. Furthermore we integrate functions operating in space to make the GP able to account for the spatial nature of our data in a more exible way. We show that especially the integration of spatial standard deviation into the tness function gives very promising results. In the rst part of this report the di erent modi cations of our original GP algorithm are described and tested on the problem of downscaling near-surface temperature in clear sky nights. The second part covers a rst test for downscaling of near-surface wind speed. The last part describes an approach for generalizing the multi-objective tness assignment, the Strength Pareto approach.
Responsible Party
Creator:Tanja Zerenner (Author)
Publisher:CRC/TR32 Database (TR32DB)
Publication Year:2013
Topic
TR32 Topic:Atmosphere
Related Subproject:C4
Subject:Keyword: PhD Report
File Details
Filename:Report3_Zerenner_2013.pdf
Data Type:Text - Text
File Size:1.9 MB
Date:Available: 28.08.2013
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:15 (1 - 15)
Further Information:TR32 Student Report Phase II
Metadata Details
Metadata Creator:Tanja Zerenner
Metadata Created:04.12.2013
Metadata Last Updated:04.12.2013
Subproject:C4
Funding Phase:2
Metadata Language:English
Metadata Version:V50
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