Visual Analytics of Multi-sensor Weather Information - Georeferenciation of Doppler Weather Radar and Weather Stations

Aitor Moreno, Andoni Galdós, Andoni Mujika, Álvaro Segura

Abstract

This work presents a geovisual tool which integrates and georeferences data coming from some of the weather instruments installed in the Basque Country: a Doppler weather radar and the weather station network composed of around 100 multi-sensors stations (temperature, precipitation, wind...). The visualization of the raw data coming from the weather radar is based on the generation of a set of 3D textured concentric cones (one per elevation scan). The resulting 3D model is then integrated in the 3D digital terrain of the Basque Country. For the weather stations, we have provided a Kriging based interpolation method to produce textures from the scalar data measured at the weather stations. These textures are then mapped in the same 3D digital terrain as before. The integrated visualization of the weather information enhances the understanding of the data. To illustrate the proposed methods a use case is provided: matching the precipitation measured at ground level with the radar scans.

References

  1. Andrienko, G., Andrienko, N., Demsar, U., Dransch, D., Dykes, J., Fabrikant, S. I., Jern, M., Kraak, M.-J., Schumann, H., and Tominski, C. (2010). Space, time and visual analytics. International Journal of Geographical Information Science, 24(10):1577-1600.
  2. Congote, J., Segura, A., Kabongo, L., Moreno, A., Posada, J., and Ruiz, O. E. (2011). Interactive visualization of volumetric data with webgl in real-time. In 16th International Conference on Web 3D Technology, Web3D 2011, pages 137-146.
  3. Cressie, N. A. (1993). Statistics for Spatial Data. WileyInterscience.
  4. Ernvik, A. (2002). 3D Visualization of Weather Radar Data. Technical Report 3252, Linkping University, Department of Electrical Engineering.
  5. Ginn, E. W. L. (1999). From PPI to Dual Doppler Images - 40 Years of Radar Observations at the Hong Kong Observatory. In Proceedings of the 32nd Session of the ESCAP/WMO Typhoon Committee.
  6. Hartkamp, A., de Beurs, K., Stein, A., and White, J. (1999). Interpolation techniques for climate variables. In CIMMYT, editor, NRG-GIS Series 99-01, chapter 26.
  7. Huijbregts, C. and Matheron, G. (1971). Universal kriging. In Proc. of International Symposium on Techniques for Decision-Making in Mineral Industry, page 159 169.
  8. James, C. N., Brodzik, S. R., Edmon, H., Houze, R. A., and Yuter, S. E. (2000). Radar data processing and visualization over complex terrain. Wea. Forecasting, 15:327 - 338.
  9. Jenson, S. K. and Dominque, J. O. (1988). Extracting topographic structure from digital elevation data foar geographic information system analysis. Photogrammetric Engineering and Remote Sensing, 54(11).
  10. Kraak, M.-J. and Ormeling, F. (2002). Cartography: Visualization of Geospatial Data. Pearson Education.
  11. Mair, A. and Fares, A. (2011). Comparison of Rainfall Interpolation Methods in a Mountainous Region of a Tropical Island. Journal of Hydrologic Engineering, 16(4):371+.
  12. Peng, C. and Lingda, W. (2007). 3D representation of radar coverage in complex environment. International Journal of Computer Science and Network Security, 7(7):139 - 145.
  13. Peuquet, D. J. and Marble, D. F. (1990). Introductory Readings in Geographic Information Systems. Taylor and Francis.
  14. Sundaram, V., Ru, Y., Benes, B., Zhao, L., Song, C. X., Park, T., Bertoline, G. R., , and Huber, M. (2008). An integrated system for near real-time 3D visualization of NEXRAD Level II Data using TeraGrid. In TeraGrid 08 - The 3rd Annual TeraGrid Conference, Las Vegas, NV., pages 1 - 8.
  15. Tomaszewski, B. M., Robinson, A. C., Weaver, C., Stryker, M., and Maceachren, A. M. (2007). Geovisual analytics and crisis management. In Proceedings of the 4th International ISCRAM Conference, May 13-16, 2007, pages 173-179.
  16. Toussaint, M., Malkomes, M., Hagen, M., Hller, H., and Meischner, P. (2000). A real time data visualization and analysis environment, scientific data management of large weather radar archives. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 25(1012):1001 - 1003. First European Conference on Radar Meteorology.
  17. Van Ho, Q., Lundblad, P., A°ström, T., and Jern, M. (2012). A web-enabled visualization toolkit for geovisual analytics. Information Visualization, 11(1):22-42.
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Paper Citation


in Harvard Style

Moreno A., Galdós A., Mujika A. and Segura Á. (2014). Visual Analytics of Multi-sensor Weather Information - Georeferenciation of Doppler Weather Radar and Weather Stations . In Proceedings of the 5th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2014) ISBN 978-989-758-005-5, pages 329-336. DOI: 10.5220/0004677603290336


in Bibtex Style

@conference{ivapp14,
author={Aitor Moreno and Andoni Galdós and Andoni Mujika and Álvaro Segura},
title={Visual Analytics of Multi-sensor Weather Information - Georeferenciation of Doppler Weather Radar and Weather Stations},
booktitle={Proceedings of the 5th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2014)},
year={2014},
pages={329-336},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004677603290336},
isbn={978-989-758-005-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2014)
TI - Visual Analytics of Multi-sensor Weather Information - Georeferenciation of Doppler Weather Radar and Weather Stations
SN - 978-989-758-005-5
AU - Moreno A.
AU - Galdós A.
AU - Mujika A.
AU - Segura Á.
PY - 2014
SP - 329
EP - 336
DO - 10.5220/0004677603290336