[1707] - Fourth semi-annual report for IRTG D5: A high-resolution multi-scale space-time precipitation model from direct measurements and remote sensing

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.
VERY IMPORTANT!
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!
Features
Citation
Schinagl, K., 2017. Fourth semi-annual report for IRTG D5: A high-resolution multi-scale space-time precipitation model from direct measurements and remote sensing. PhD Report, University of Bonn, Bonn. Accessed from https://www.tr32db.uni-koeln.de/data.php?dataID=1707 at 2020-03-29.
Citation Options
Export as: Select the file format for your download.Citation style: Select the displayed citation style.
Identification
Title(s):Main Title: Fourth semi-annual report for IRTG D5: A high-resolution multi-scale space-time precipitation model from direct measurements and remote sensing
Description(s):Abstract: A key element of numerical weather prediction is quantitative precipitation estimation. Polarimetric radar provides manifold possibilities to gain information. Our objective is to exploit the relationship of its observations to the drop size distribution (DSD). How can we derive knowledge from observations of the polarimetric variables about the underlying DSD in a systematic way? What is the role of uncertainties caused by observation error, model error or numerical simulations? Our aim is to evaluate the capabilities of a Bayesian model for the estimation of a three-parameter gamma DSD, $N(D) = N_0 D^{\mu} \exp(-\Lambda D)$, from polarimetric radar variables. To this end, we use a simulated environment, based on the numerical weather prediction (NWP) model COSMO-DE. In this report, we summarize the work done in this work package in the last semester. The gamma DSD is derived from COSMO data and we describe a lookup table (LUT) approach for the scattering calculations.
Responsible Party
Creator(s):Author: Katharina Schinagl
Publisher:CRC/TR32 Database (TR32DB)
Topic
TR32 Topic:Remote Sensing
Related Sub-project(s):D5
Subject(s):CRC/TR32 Keywords: Precipitation, Geostatistics, Inverse Modelling, PhD Report, Remote Sensing, COSMO
Topic Category:ClimatologyMeteorologyAtmosphere
File Details
File Name:report4.pdf
Data Type:Text
Size(s):26 Pages
File Size:6880 kB (6.719 MB)
Date(s):Created: 2017-03-21
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
Constraints
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
Geographic
North:-no map data
East:-
South:-
West:-
Measurement Region:Germany
Measurement Location:--Germany--
Specific Informations - Report
Report Date:21st of March, 2017
Report Type:PhD Report
Report City:Bonn
Report Institution:University of Bonn
Number Of Pages:26
Period of Pages:1 - 26
Metadata Details
Metadata Creator:Katharina Schinagl
Metadata Created:2017-09-29
Metadata Last Updated:2017-09-29
Subproject:D5
Funding Phase:3
Metadata Language:English
Metadata Version:V42
Metadata Export
Metadata Export:
Select the XML download format.
Dataset Metrics
Page Visits:480
Metadata Downloads:0
Dataset Downloads:0
Dataset Activity
Features