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Eigenskies: a method of visualizing weather prediction data
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
2003 (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. Groehn; Katy Boerner, 2003Conference paper, Published paper (Other academic)
Abstract [en]

Visualizing a weather prediction data set by actually synthesizing an image of the sky is a difficult problem. In this paper we present a method for synthesizing realistic sky images from weather prediction and climate prediction data. Images of the sky are combined with a number of weather parameters (like pressure and temperature) to train an artificial neural network (ANN) to predict the appearance of the sky from certain weather parameters. Hourly measurements from a period of eight months are used. The principal component analysis (PCA) method is used to decompose images of the sky into their eigen components -- the eigenskies. In this way the image information is compressed into a small number of coefficients while still preserving the main information in the image. This means that the fine details of the cloud cover cannot be synthesized using this method. The PCA coefficients together with measured weather parameters at the same time form a data point that is used to train the ANN. The results show that the method gives adequate results and although some discrepancies exist, the main appearance is correct. It is possible to distinguish between different types of weather. A rainy day looks rainy and a sunny day looks sunny.

Place, publisher, year, edition, pages
2003.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-13392DOI: 10.1117/12.473931OAI: oai:DiVA.org:liu-13392DiVA: diva2:20595
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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