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Visualizing weather with synthetic high-dynamic range images
Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9466-9826
Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.ORCID iD: 0000-0001-7557-4904
2004 (English)In: Proceedings of SPIE-IS&T Electronic Imaging, Visualization and Data Analysis / [ed] Robert F. Erbacher; Philip C. Chen; Jonathan C. Roberts; Matti T. Gr÷hn; Katy B÷rner, 2004, 107-116 p.Conference paper, Published paper (Other academic)
Abstract [en]

The appearance of the sky has a fundamental effect on the way human beings perceive an environment. This paper presents a method to compute synthetic high-dynamic-range fisheye images from weather parameter data sets. These images can then be used in global-illumination systems (e. g. Radiance) to define the lighting conditions at an arbitrary weather state. Applications of this technology can be found in flight simulators and in architectural visualization. The method combines artificial neural networks and principal component analysis to associate the appearance of the sky with the state of a weather parameter vector. A model is trained with examples of sky images and weather data from a period of seven months. This model is then used to generate artificial sky images corresponding to a specific weather parameter vector. This is a novel method which contrary to many previous methods is able to synthesize a sky image which varies with the current weather state. The results show that, although it is not possible to represent the cloud details, it is possible to distinguish between different weather states.

Place, publisher, year, edition, pages
2004. 107-116 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-13394DOI: 10.1117/12.525997OAI: oai:DiVA.org:liu-13394DiVA: diva2:20597
Available from: 2005-10-14 Created: 2005-10-14 Last updated: 2016-08-31
In thesis
1. Image Based Visualization Methods for Meteorological Data
Open this publication in new window or tab >>Image Based Visualization Methods for Meteorological Data
2004 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Visualization is the process of constructing methods, which are able to synthesize interesting and informative images from data sets, to simplify the process of interpreting the data. In this thesis a new approach to construct meteorological visualization methods using neural network technology is described. The methods are trained with examples instead of explicitely designing the appearance of the visualization.

This approach is exemplified using two applications. In the fist the problem to compute an image of the sky for dynamic weather, that is taking account of the current weather state, is addressed. It is a complicated problem to tie the appearance of the sky to a weather state. The method is trained with weather data sets and images of the sky to be able to synthesize a sky image for arbitrary weather conditions. The method has been trained with various kinds of weather and images data. The results show that this is a possible method to construct weather visaualizations, but more work remains in characterizing the weather state and further refinement is required before the full potential of the method can be explored. This approach would make it possible to synthesize sky images of dynamic weather using a fast and efficient empirical method.

In the second application the problem of computing synthetic satellite images form numerical forecast data sets is addressed. In this case a mode is trained with preclassified satellite images and forecast data sets to be able to synthesize a satellite image representing arbitrary conditions. The resulting method makes it possible to visualize data sets from numerical weather simulations using synthetic satellite images, but could also be the basis for algorithms based on a preliminary cloud classification.

Place, publisher, year, edition, pages
Institutionen för teknik och naturvetenskap, 2004. 92 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1137
Keyword
Visualization, Meteorological Data, Artificial Neural Networks, High-Dynamic-Range images, Satellite Data, Classification
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-4325 (URN)91-85297-00-3 (ISBN)
Presentation
2004-12-17, K3, Campus Norrköping, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Note
Report code: LiU-Tek-Lic-2004:66.Available from: 2005-10-14 Created: 2005-10-14 Last updated: 2016-08-31

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Olsson, BjörnYnnerman, AndersLenz, Reiner

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