TR32-Database: Database of Transregio 32

[1024] - Downscaling near-surface atmospheric fields with multi-objective 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., 2014. Downscaling near-surface atmospheric fields with multi-objective Genetic Programming. PhD Report, Meteorological Institute, University Bonn, Bonn, Germany. Accessed from http://www.tr32db.uni-koeln.de/data.php?dataID=1024 at 2017-09-20.
Identification
Title(s):Main Title: Downscaling near-surface atmospheric fields with multi-objective Genetic Programming
Description(s):Abstract: The coupling of models for the different components of the Soil-Vegetation-Atmosphere-System is required to investigate component interactions and feedback processes. However, the component models for atmosphere, land-surface and subsurface are usually operated at different resolutions in space and time owing to the dominant processes. The computationally often more expensive atmospheric models, for instance, are typically employed at a coarser resolution than land-surface and subsurface models. Thus up- and downscaling procedures are required at the interface between the atmospheric model and the land-surface/subsurface models. We apply multi-objective Genetic Programming (GP) to a training data set of high-resolution atmospheric model runs to learn equations or short programs that reconstruct the fine-scale fields (e.g., 400 m resolution) of the near-surface atmospheric state variables from the coarse atmospheric model output (e.g., 2.8 km resolution). Like artificial neural networks, GP can flexibly incorporate multivariate and nonlinear relations, but offers the advantage that the solutions are human readable and thus can be checked for physical consistency. Using the Strength Pareto Approach for multi-objective fitness assignment allows us to consider multiple characteristics of the fine-scale fields during the learning procedure.
Responsible Party
Creator(s):Author: Tanja Zerenner
Publisher:CRC/TR32 Database (TR32DB)
Topic
TR32 Topic:Atmosphere
Related Sub-project(s):C4
Subject(s):CRC/TR32 Keywords: PhD Report, Downscaling and Disaggregation, Spatial Heterogeneity
File Details
File Name:report5_paperdraft.pdf
Data Type:Text
File Size:4476 kB (4.371 MB)
Date(s):Available: 2014-09-08
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:7th of July, 2014
Report Type:PhD Report
Report City:Bonn, Germany
Report Institution:Meteorological Institute, University Bonn
Number Of Pages:18
Period of Pages:1 - 18
Further Informations:TR32 Student Report Phase II
Metadata Details
Metadata Creator:Tanja Zerenner
Metadata Created:2014-09-08
Metadata Last Updated:2014-09-08
Subproject:C4
Funding Phase:2
Metadata Language:English
Metadata Version:V40
Dataset Metrics
Page Visits:105
Metadata Downloads:0
Dataset Downloads:2
Dataset Activity
Features