TY - GEN
T1 - An application-oriented framework for feature tracking in atmospheric sciences
AU - Sakurai, Daisuke
AU - Hege, Hans Christian
AU - Kuhn, Alexander
AU - Rust, Henning
AU - Kern, Bastian
AU - Breitkopf, Tom Lukas
N1 - Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2017/12/19
Y1 - 2017/12/19
N2 - In atmospheric sciences, sizes of data sets grow continuously due to increasing resolutions. A central task is the comparison of spa-tiotemporal 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.
AB - In atmospheric sciences, sizes of data sets grow continuously due to increasing resolutions. A central task is the comparison of spa-tiotemporal 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.
UR - http://www.scopus.com/inward/record.url?scp=85048398430&partnerID=8YFLogxK
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U2 - 10.1109/LDAV.2017.8231857
DO - 10.1109/LDAV.2017.8231857
M3 - Conference contribution
AN - SCOPUS:85048398430
T3 - 2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017
SP - 96
EP - 97
BT - 2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2017
Y2 - 2 October 2017
ER -