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Hypothesis Generation in Climate Research with Interactive Visual Data Exploration

Johannes Kehrer, Florian Ladstädter, Philipp Muigg, Helmut Doleisch, Andrea Steiner, Helwig Hauser

ARTICLE, IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG), Oct, 2008

Abstract

One of the most prominent topics in climate research is the investigation, detection, and allocation of climate change. In this paper, we aim at identifying regions in the atmosphere (e.g., certain height layers) which can act as sensitive and robust indicators for climate change. We demonstrate how interactive visual data exploration of large amounts of multi-variate and time-dependent climate data enables the steered generation of promising hypotheses for subsequent statistical evaluation. The use of new visualization and interaction technology -- in the context of a coordinated multiple views framework -- allows not only to identify these promising hypotheses, but also to efficiently narrow down parameters that are required in the process of computational data analysis. Two datasets, namely an ECHAM5 climate model run and the ERA-40 reanalysis incorporating observational data, are investigated. Higher-order information such as linear trends or signal-to-noise ratio is derived and interactively explored in order to detect and explore those regions which react most sensitively to climate change. As one conclusion from this study, we identify an excellent potential for usefully generalizing our approach to other, similar application cases, as well.

Published

IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG)

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BibTeX

@article{kehrer08hypothesisGeneration,
  author = {Johannes Kehrer and Florian Ladst{\"a}dter and Philipp Muigg and 
			Helmut Doleisch and Andrea Steiner and Helwig Hauser},
  title = {Hypothesis Generation in Climate Research with 
			Interactive Visual Data Exploration},
  year = {2008},
  abstract = {One of the most prominent topics in climate research is the 
	investigation, detection, and allocation of climate change. In 
	this paper, we aim at identifying regions in the atmosphere (e.g., 
	certain height layers) which can act as sensitive and robust 
	indicators for climate change. We demonstrate how interactive 
	visual data exploration of large amounts of multi-variate and 
	time-dependent climate data enables the steered generation of 
	promising hypotheses for subsequent statistical evaluation. 
	The use of new visualization and interaction technology -- in the 
	context of a coordinated multiple views framework -- allows not 
	only to identify these promising hypotheses, but also to efficiently 
	narrow down parameters that are required in the process of 
	computational data analysis. Two datasets, namely an ECHAM5 climate 
	model run and the ERA-40 reanalysis incorporating observational data, 
	are investigated. Higher-order information such as linear trends or 
	signal-to-noise ratio is derived and interactively explored in order 
	to detect and explore those regions which react most sensitively to 
	climate change. As one conclusion from this study, we identify an 
	excellent potential for usefully generalizing our approach to other, 
	similar application cases, as well.},
  month = {Oct},
  journal = {IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG)},
  event = {IEEE Visualization 2008},
  location = {Columbus, Ohio, USA},
  volume = {14},
  number = {6},
  pages = {1579--1586},
  URL = {http://dx.doi.org/10.1109/TVCG.2008.139},



}






 Last Modified: Jean-Paul Balabanian, 2013-05-29