[618] - A Gaussian Markov random eld approach for radar rainfall information

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

By downloading files from this dataset you accept the license terms of TR32DB Data policy agreement and TR32DBData Protection Statement.
Adequate reference when this dataset will be discussed or used in any publication or presentation is mandatory. In this case please contact the dataset creator.
Due to the speed of the filesystem and depending on the size of the archive and the file to be extracted, it may take up to thirty (!) minutes until a download is ready! Beware of that when confirming since you may not close the tab because otherwise, you will not get your file!
Krebsbach, K., 2012. A Gaussian Markov random eld approach for radar rainfall information. PhD Report, Meteorological Institute, University of Bonn, Germany, Bonn, Germany. Accessed from https://www.tr32db.uni-koeln.de/data.php?dataID=618 at 2019-07-20.
Citation Options
Export as: Select the file format for your download.Citation style: Select the displayed citation style.
Title(s):Main Title: A Gaussian Markov random eld approach for radar rainfall information
Description(s):Abstract: Spatially distributed, high-resolution precipitation rates are key ingredients for modeling soil-vegetation processes, water and solute transports in mesoscale catchments, and for short-range weather prediction. The ultimate goal of our study is to develop a space-time, multilevel statistical model that merges rain radar measurements with other observations of precipitation. This is a challenging task since it aims at combining data sources with a variety of error structures, and temporal resolutions. E.g., in-situ measurements are quite accurate, but available only at sparse and irregularly distributed locations, whereas remote measurements cover complete areas but su er from spatially and temporally inhomogeneous systematic errors. The rst step towards such a space-time precipitation model is to develop a statistical model for precipitation based on radar measurements. Precipitation rates over a region of about 230 x 230 km2 are provided by a composite of the two polarimetric X-band radars in Germany. The two radars are located in a distance of about 60 km in Bonn and Jülich, respectively. For the statistical model formulation we use a Gaussian Markov random eld as underlying process. A Markov random eld is a suitable model to account for spatial dependencies if the region where dependencies are observed can be reduced to a small neighborhood without losing information. This makes large data problems computationally feasible, since the neighborhood structure stands in one-to-one relation with a sparse precision matrix. We start with the spatial analysis of rainfall intensities derived from radar reectivities. The images consist of 460 x 461 points with a resolution of 500m x 500m that lie in the overlap of the Jülich and Bonn X-band radars and cover the Rur catchment. We derive a stationary and isotropic GMRF by tting its correlation function to the empirical correlation function of the data.
Responsible Party
Creator(s):Author: Katharina Krebsbach
Publisher:CRC/TR32 Database (TR32DB)
TR32 Topic:Atmosphere
Subject(s):CRC/TR32 Keywords: PhD Report
File Details
File Name:Report3_Krebsbach_2012.pdf
Data Type:Text
File Size:324 kB (0.316 MB)
Date(s):Available: 2012-09-30
Mime Type:application/pdf
Data Format:PDF
Download Permission:OnlyTR32
General Access and Use Conditions:According to the TR32DB data policy agreement.
Access Limitations:According to the TR32DB data policy agreement.
Licence:TR32DB Data policy agreement
North:-no map data
Measurement Region:NorthRhine-Westphalia
Measurement Location:--NorthRhine-Westphalia--
Specific Informations - Report
Report Date:30th of September, 2012
Report Type:PhD Report
Report City:Bonn, Germany
Report Institution:Meteorological Institute, University of Bonn, Germany
Number Of Pages:7
Period of Pages:1 - 7
Further Informations:TR32 Student Report Phase II
Metadata Details
Metadata Creator:Katharina Krebsbach
Metadata Created:2013-12-16
Metadata Last Updated:2013-12-16
Funding Phase:2
Metadata Language:English
Metadata Version:V40
Dataset Metrics
Page Visits:411
Metadata Downloads:0
Dataset Downloads:4
Dataset Activity