[715] - Backscatter differential phase - estimation and variability

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!
Troemel, S., Kumjian, M. R. ., Ryzhkov, A., Simmer, C., Diederich, M., 2013. Backscatter differential phase - estimation and variability. Journal of Atmospheric and Oceanic Technology, 52, 2529 - 2548. DOI: 10.1175/JAMC-D-13-0124.1.
Citation Options
Export as: Select the file format for your download.Citation style: Select the displayed citation style.
Title(s):Main Title: Backscatter differential phase - estimation and variability
Description(s):Abstract: Based on simulations and observations made with polarimetric radars operating at X, C, and S bands, the backscatter differential phase δ has been explored. δ has been identified as an important polarimetric variable, which should not be ignored in precipitation estimations based on KDP, especially at shorter radar wavelengths. Moreover, δ bears important information about the dominant size of raindrops and wet snowflakes in the melting layer. New methods for estimating δ in rain and in the melting layer are suggested. The method for estimating δ in rain is based on a modified version of the ZPHI algorithm, and provides reasonably robust estimates of δ and KDP in pure rain except in regions where the total measured differential phase DP behaves erratically, such as areas affected by nonuniform beam filling (NBF) or low signal-to noise ratio. The method for estimating δ in the melting layer results in reliable estimates of δ in stratiform precipitation and requires azimuthal averaging of radial profiles of DP at high antenna elevations. Comparisons with large disdrometer datasets collected in Oklahoma and Germany confirm a strong interdependence between δ and differential reflectivity ZDR. Because δ is immune to attenuation, partial beam blockage, and radar miscalibration, the strong correlation between ZDR and δ is of interest for quantitative precipitation estimation: δ and ZDR are differently affected by the particle size distribution (PSD) and thus may complement each other for PSD moment estimation. Furthermore, the magnitude of δ can be utilized as an important calibration parameter for improving microphysical models of the melting layer.
Identifier(s):DOI: 10.1175/JAMC-D-13-0124.1
Responsible Party
Creator(s):Author: Silke Troemel
Author: Matthew R. Kumjian
Author: Alexander Ryzhkov
Author: Clemens Simmer
Author: Malte Diederich
Publisher:American Meteorological Society
TR32 Topic:Atmosphere
Subject(s):CRC/TR32 Keywords: Backscatter, Remote Sensing, Precipitation
File Details
File Name:2013_Troemel_JoAOT.pdf
Data Type:Text
Size(s):54 Pages
File Size:3488 kB (3.406 MB)
Date(s):Date Submitted: 2013-03-01
Issued: 2013-11-01
Mime Type:application/pdf
Data Format:PDF
Download Permission:OnlyTR32
General Access and Use Conditions:For internal use only
Access Limitations:For internal use only
Licence:TR32DB Data policy agreement
North:-no map data
Measurement Region:Other
Measurement Location:--Other--
Specific Informations - Publication
Article Type:Journal
Source:Journal of Atmospheric and Oceanic Technology
Page Range:2529 - 2548
Metadata Details
Metadata Creator:Clemens Simmer
Metadata Created:2013-12-03
Metadata Last Updated:2013-12-03
Funding Phase:2
Metadata Language:English
Metadata Version:V40
Dataset Metrics
Page Visits:438
Metadata Downloads:0
Dataset Downloads:4
Dataset Activity