An application-oriented framework for feature tracking in atmospheric sciences

Daisuke Sakurai, Hans Christian Hege, Alexander Kuhn, Henning Rust, Bastian Kern, Tom Lukas Breitkopf

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages96-97
Number of pages2
ISBN (Electronic)9781538606179
DOIs
Publication statusPublished - Dec 19 2017
Externally publishedYes
Event7th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2017 - Phoenix, United States
Duration: Oct 2 2017 → …

Publication series

Name2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017
Volume2017-December

Conference

Conference7th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2017
CountryUnited States
CityPhoenix
Period10/2/17 → …

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All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Information Systems
  • Media Technology
  • Library and Information Sciences

Cite this

Sakurai, D., Hege, H. C., Kuhn, A., Rust, H., Kern, B., & Breitkopf, T. L. (2017). An application-oriented framework for feature tracking in atmospheric sciences. In 2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017 (pp. 96-97). (2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017; Vol. 2017-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LDAV.2017.8231857