Topology-based Feature Detection in Climate Data

Kappe, Christopher P.; Böttinger, Michael; Leitte, Heike

Topology-based Feature Detection in Climate Data

Files

009-016.pdf (5.78 MB)

Date

2019

Authors

Kappe, Christopher P.
Böttinger, Michael
Leitte, Heike

Publisher

The Eurographics Association

Abstract

The weather and climate research community needs to analyze increasingly large datasets, mostly obtained by observations or produced by simulations. Ensemble simulation techniques, which are used to capture uncertainty, add a further dimension to the multivariate time-dependent 3D data, even tightening the challenge of finding relevant information in the data for answering the respective research questions. In this paper we propose a topology-based method to support the visual analysis of climate data by detecting regions with particularly strong local minima or maxima and highlighting them with colored contours. Combined with preceding clustering of the data fields, typical spatial patterns characterizing the climate variability are detected and visualized. We demonstrate the utility of our method with a study of global temperature anomalies of a 150-years ensemble simulation consisting of 100 members.

        @inproceedings{10.2312:envirvis.20191099
,
booktitle = {Workshop on Visualisation in Environmental Sciences (EnvirVis)
},
editor = {Bujack, Roxana and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
},
title = {{Topology-based Feature Detection in Climate Data
}},
author = {Kappe, Christopher P. and 
Böttinger, Michael and 
Leitte, Heike
},
year = {2019
},
publisher = {The Eurographics Association
},
ISBN = {978-3-03868-086-4
},
DOI = {10.2312/envirvis.20191099
}
}