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Computing synthetic satellite images from 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
2004 (English)In: Proceedings of SPIE - IS and 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, 296-304 p.Conference paper, Published paper (Other academic)
Abstract [en]

Satellite images are important sources of information for meteorologists to predict rapid weather changes, for example storms, now and in the near-future (Nowcasting). It is not possible to use traditional numerical weather forecasts for this purpose since these are computed with a time-lag of several hours. This means that the most recent weather changes are not taken into account. This paper presents a method to compute synthetic satellite images from simulated forecast files. The cloud information in numerical forecast data sets is of much more interest if it can be visualized with a well-known representation like the satellite image. The proposed method uses artificial neural network technology to construct a model which is trained with data from numerical forecasts and classified satellite data captured at the same points in time. The cloud cover parameters in the forecast data set are tied to the cloud classification in the satellite image using a point-to-point representation. The results show that this is a useful method to compute synthetic satellite images. The level of detail in the resulting images is lower than in a real satellite image, but detailed enough to provide information about the principal features of the cloud cover.

Place, publisher, year, edition, pages
2004. 296-304 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-13395DOI: 10.1117/12.526829OAI: oai:DiVA.org:liu-13395DiVA: diva2:20598
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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