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Visual Analysis of Lightning Data Using Space–Time-Cube

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Cartography from Pole to Pole

Abstract

This paper describes a framework for a visual analysis of lightning data described by 3D coordinates and the precise occurrence time. First lightning cells are detected and tracked. After that we developed a GUI (interactive graphic user interface) in order to enable the visual exploration of movement patterns and other characteristics of lightning cells. In particular we present different visual concepts for the dynamic lightning cells and tracks within a Space–Time-Cube and a 3D view. Furthermore a statistical analysis is presented. The developed GUI which aims to support decision making includes the visual and statistical representation of cell features as centroid, extension, density, size etc., within a specific temporal and spatial range of interest.

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References

  • Andrienko G, Andrienko N, Dykes J, Fabrikant SI, Wachowicz M (2008) Geovisualization of dynamics, movement and change: key issues and developing approaches in visualization research. Inf Vis 7(3–4):173–180

    Article  Google Scholar 

  • Betz HD, Schmidt K, Oettinger WP, Montag B (2008) Cell-tracking with lightning data from LINET. Adv Geosci 17:55–61

    Article  Google Scholar 

  • Betz HD, Schmidt K, Oettinger WP (2009) LINET—an international VLF/LF lightning detection network in Europe. In: Betz HD, Schumann U, Laroche P (eds) Lightning: principles, instruments and applications. Springer, Dordrecht, pp 115–140

    Google Scholar 

  • Bonelli P, Marcacci P (2008) Thunderstorm nowcasting by means of lightning and radar data: algorithms and applications in northern Italy. Nat Hazards Earth Syst Sci 8(5):1187–1198

    Article  Google Scholar 

  • Dixon M, Wiener G (1993) TITAN: thunderstorm identification, tracking, analysis, and nowcasting-A radar-based methodology. J Atmos Ocean Technol 10(6):785–797

    Article  Google Scholar 

  • Galton A (2005) Dynamic collectives and their collective dynamics. In: Cohn AG, Mark DM (eds) Spatial information theory. Springer, Berlin, pp 300–315

    Chapter  Google Scholar 

  • Han J, Kamber M (2006) Data mining: concepts and techniques. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Handwerker J (2002) Cell tracking with TRACE3D—a new algorithm. Atmos Res 61(1):15–34

    Article  Google Scholar 

  • Hering A, Morel C, Galli G, Sénési S, Ambrosetti P, Boscacci M (2004) Nowcasting thunderstorms in the Alpine region using a radar based adaptive thresholding scheme. In: Proceedings of ERAD

    Google Scholar 

  • Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall, Inc., Upper Saddle River

    Google Scholar 

  • Johnson J, MacKeen PL, Witt A, Mitchell EDW, Stumpf GJ, Eilts MD, Thomas KW (1998) The storm cell identification and tracking algorithm: an enhanced WSR-88D algorithm. Weather Forecast 13(2):263–276

    Article  Google Scholar 

  • Kraak MJ (2003) The space–time cube revisited from a geovisualization perspective. In: Proceedings of 21st international cartographic conference, pp 1988–1996

    Google Scholar 

  • Krisp J, Peters S (2010) Visualizing dynamic 3D densities: a Lava-lamp approach. In: 13th AGILE international conference on geographic information science

    Google Scholar 

  • Krisp JM, Peters S, Murphy CE, Fan H (2009) Visual bandwidth selection for kernel density maps. Photogrammetrie Fernerkundung Geoinf 5:445–454

    Article  Google Scholar 

  • Krisp J, Peters S, Burkert F, Butenuth M (2010) Visual identification of scattered crowd movement patterns using a directed kernel density estimation. In: SPM2010 mobile Tartu

    Google Scholar 

  • Krisp JM, Peters S, Polous K, Fan H, Meng L (2012) Getting in and out of a taxi: spatio-temporal hotspot analysis for floating taxi data in Shanghai. In: Networks for mobility 2012, Stuttgart

    Google Scholar 

  • Li L, Schmid W, Joss J (1995) Nowcasting of motion and growth of precipitation with radar over a complex orography. J Appl Meteorol 34(6):1286–1300

    Article  Google Scholar 

  • MacEachren AM, Kraak MJ (2001) Research challenges in geovisualization. Cartogr Geogr Inf Sci 28(1):3–12

    Article  Google Scholar 

  • Mackaness WA, Ruas A, Sarjakoski LT (2007) Generalisation of geographic information: cartographic modelling and applications. Elsevier Science, Amsterdam, p 386

    Google Scholar 

  • Meyer V (2010) Thunderstorm tracking and monitoring on the basis of three-dimensional lightning data and conventional and polarimetric radar data. In: DLR, Deutsches Zentrum für Luft- und Raumfahrt. p 128

    Google Scholar 

  • Peters S, Krisp JM (2010) Density calculation for moving points. In: 13th AGILE international conference on geographic information science

    Google Scholar 

  • Peters S, Meng L, Betz HD (2013) Analytics approach for lightning data analysis and cell nowcasting. In: EGU general assembly conference abstracts, pp 32–33

    Google Scholar 

  • Steinacker R, Dorninger M, Wölfelmaier F, Krennert T (2000) Automatic tracking of convective cells and cell complexes from lightning and radar data. Meteorol Atmos Phys 72(2):101–110

    Article  Google Scholar 

  • Virrantaus K, Fairbairn D, Kraak M-J (2009) ICA research agenda on cartography and GI science. Cartogr J 46(2):63–75

    Article  Google Scholar 

  • Zinner T, Mannstein H, Tafferner A (2008) Cb-TRAM: tracking and monitoring severe convection from onset over rapid development to mature phase using multi-channel Meteosat-8 SEVIRI data. Meteorol Atmos Phys 101(3):191–210

    Article  Google Scholar 

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Acknowledgments

The authors gratefully acknowledge Nowcast Company for providing lightning test dataset and the support of the Graduate Center Civil Geo and Environmental Engineering at Technische Universität München, Germany.

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Correspondence to Stefan Peters .

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Peters, S., Betz, HD., Meng, L. (2014). Visual Analysis of Lightning Data Using Space–Time-Cube. In: Buchroithner, M., Prechtel, N., Burghardt, D. (eds) Cartography from Pole to Pole. Lecture Notes in Geoinformation and Cartography(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32618-9_12

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