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An Application-Oriented Framework for Feature Tracking in Atmospheric Sciences

Sakurai, Daisuke and Hege, Hans-Christian and Kuhn, Alexander and Rust, Henning and Kern, Bastian and Breitkopf, Tom-Lukas (2017) An Application-Oriented Framework for Feature Tracking in Atmospheric Sciences. In: IEEE Symposium on Large Data Analysis and Visualization 2017, LDAV 2017 - Proceedings, pp. 96-67. The 7th IEEE Symposium on Large Data Analysis and Visualization, 2. Okt. 2017, Phoenix, Arizona, Vereinigte Staaten von Amerika. DOI: 10.1109/LDAV.2017.8231857 ISBN 978 1 5386 0617 9

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Abstract

In atmospheric sciences, sizes of data sets grow continuously due to increasing resolutions. A central task is the comparison of spatiotemporal fields, to assess different simulations and to compare simulations with observations. A significant information reduction is possible by focusing on geometric-topological features of the fields or on derived meteorological objects. Due to the huge size of the data sets, spatial features have to be extracted in time slices and traced over time. Fields with chaotic component, i.e. without 1:1 spatiotemporal correspondences, can be compared by looking upon statistics of feature properties. Feature extraction, however, requires a clear mathematical definition of the features - which many meteorological objects still lack. Traditionally, object extractions are often heuristic, defined only by implemented algorithms, and thus are not comparable. This work surveys our framework designed for efficient development of feature tracking methods and for testing new feature definitions. The framework supports well-established visualization practices and is being used by atmospheric researchers to diagnose and compare data.

Item URL in elib:https://elib.dlr.de/114613/
Document Type:Conference or Workshop Item (Poster)
Title:An Application-Oriented Framework for Feature Tracking in Atmospheric Sciences
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Sakurai, DaisukeZuse Institute, BerlinUNSPECIFIED
Hege, Hans-ChristianZuse Institute, BerlinUNSPECIFIED
Kuhn, AlexanderNVIDIA GmbH, BerlinUNSPECIFIED
Rust, HenningFreie Universität BerlinUNSPECIFIED
Kern, BastianDLR, IPAhttps://orcid.org/0000-0002-7646-9273
Breitkopf, Tom-LukasZuse Institute, BerlinUNSPECIFIED
Date:2 October 2017
Journal or Publication Title:IEEE Symposium on Large Data Analysis and Visualization 2017, LDAV 2017 - Proceedings
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.1109/LDAV.2017.8231857
Page Range:pp. 96-67
ISBN:978 1 5386 0617 9
Status:Published
Keywords:Feature extraction, Atmospheric modelling, Radar tracking, Data visualization, Algorithm design and analysis, Data mining, Visualization
Event Title:The 7th IEEE Symposium on Large Data Analysis and Visualization
Event Location:Phoenix, Arizona, Vereinigte Staaten von Amerika
Event Type:international Conference
Event Dates:2. Okt. 2017
Organizer:IEEE
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Atmosphären- und Klimaforschung
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Earth System Modelling
Deposited By: Kern, Dr. Bastian
Deposited On:19 Oct 2017 13:53
Last Modified:19 Nov 2018 14:12

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