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ISPRS Int. J. Geo-Inf. 2013, 2(3), 817-836; doi:10.3390/ijgi2030817

Visual Analysis for Nowcasting of Multidimensional Lightning Data

Department of Cartography, Technical University Munich, München 80333, Germany
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Received: 17 June 2013 / Revised: 22 July 2013 / Accepted: 14 August 2013 / Published: 26 August 2013
(This article belongs to the Special Issue Geovisualization and Analysis of Dynamic Phenomena)
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Abstract

Globally, most weather-related damages are caused by thunderstorms. Besides floods, strong wind, and hail, one of the major thunderstorm ground effects is lightning. Therefore, lightning investigations, including detection, cluster identification, tracking, and nowcasting are essential. To enable reliable decisions, current and predicted lightning cluster- and track features as well as analysis results have to be represented in the most appropriate way. Our paper introduces a framework which includes identification, tracking, nowcasting, and in particular visualization and statistical analysis of dynamic lightning data in three-dimensional space. The paper is specifically focused on enabling users to conduct the visual analysis of lightning data for the purpose of identification and interpretation of spatial-temporal patterns embedded in lightning data, and their dynamics. A graphic user interface (GUI) is developed, wherein lightning tracks and predicted lightning clusters, including their prediction certainty, can be investigated within a 3D view or within a Space-Time-Cube. In contrast to previous work, our approach provides insight into the dynamics of past and predicted 3D lightning clusters and cluster features over time. We conclude that an interactive visual exploration in combination with a statistical analysis can provide new knowledge within lightning investigations and, thus, support decision-making in weather forecast or lightning damage prevention. View Full-Text
Keywords: lightning visualization; cartographic techniques for visual analysis; geospatial visual analytics; Space-Time-Cube; point clustering; cognitive geovisualization; interactive mapping; data mining; big data lightning visualization; cartographic techniques for visual analysis; geospatial visual analytics; Space-Time-Cube; point clustering; cognitive geovisualization; interactive mapping; data mining; big data
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Peters, S.; Meng, L. Visual Analysis for Nowcasting of Multidimensional Lightning Data. ISPRS Int. J. Geo-Inf. 2013, 2, 817-836.

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