TR32-Database: Database of Transregio 32

[590] - Atmospheric Downscaling using Genetic Programming

All available metadata of the dataset are listed below. Some features are available, e.g. download of dataset or additional description file.

Features
Citation
Zerenner, T., 2012. Atmospheric Downscaling using Genetic Programming. PhD Report, Meteorological Institute, University Bonn, Bonn, Germany. Accessed from http://www.tr32db.uni-koeln.de/data.php?dataID=590 at 2017-10-23.
Identification
Title(s):Main Title: Atmospheric Downscaling using Genetic Programming
Description(s):Abstract: The coupling of atmospheric, land-surface and subsurface hydrological models requires appropriate up- and downscaling procedures. Hydrological models are usually based on high spatiotemporal resolution. Applying atmospheric models as the same high resolution is in the majority of cases computationally not feasible. In the rst phase of the TR 32 a downscaling scheme has been developed to gain high-resolution forcing data for land-surface and hydrological models from the coarser atmospheric model output. This scheme consist of three steps. Step 1 is a bi-quadratic spline interpolation. In step 2 deterministic downscaling rules are applied, which are based on dependencies between the atmospheric variables in the lower boundary layer and high-resolution data on surface properties. Finally in step 3 the missing small-scale variability is added as noise. The performance of the scheme is very di erent for each of the variables and strongly dependent on the prevailing weather conditions. This is due to the fact that so far deterministic rules could be found only for some variables and under certain conditions. Up to now the interdependencies between the surface parameters and the atmospheric variables have been only exploited by calculating linear Pearson correlations between the surface properties and the atmospheric variables at one and the same grid point. In order to broaden the search space we now want to consider also non-linear dependencies, temporally delayed dependencies and relations between di erent grid points. There are in nite possible interdependencies and rules, which makes it impossible to search by hand. Therefore we will set up an automatic program based on the method of genetic programming to enable the computer to automatically learn possible downscaling rules from data.
Responsible Party
Creator(s):Author: Tanja Zerenner
Publisher:CRC/TR32 Database (TR32DB)
Topic
TR32 Topic:Atmosphere
Subject(s):CRC/TR32 Keywords: PhD Report
File Details
File Name:Report1_Zerenner_2012.pdf
Data Type:Text
File Size:1866 kB (1.822 MB)
Date(s):Available: 2012-07-16
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
Constraints
Download Permission:OnlyTR32
General Access and Use Conditions:For internal use only.
Access Limitations:For internal use only.
Licence:TR32DB Data policy agreement
Geographic
North:-no map data
East:-
South:-
West:-
Measurement Region:Other
Measurement Location:--Other--
Specific Informations - Report
Report Date:16th of July, 2012
Report Type:PhD Report
Report City:Bonn, Germany
Report Institution:Meteorological Institute, University Bonn
Number Of Pages:10
Period of Pages:1 - 10
Further Informations:TR32 Student Report Phase II
Metadata Details
Metadata Creator:Tanja Zerenner
Metadata Created:2013-12-04
Metadata Last Updated:2013-12-04
Subproject:C4
Funding Phase:2
Metadata Language:English
Metadata Version:V40
Dataset Metrics
Page Visits:128
Metadata Downloads:0
Dataset Downloads:1
Dataset Activity
Features