Environmental Earth Sciences

, Volume 72, Issue 10, pp 3767–3780 | Cite as

Concept and workflow for 3D visualization of atmospheric data in a virtual reality environment for analytical approaches

  • Carolin Helbig
  • Hans-Stefan Bauer
  • Karsten Rink
  • Volker Wulfmeyer
  • Michael Frank
  • Olaf Kolditz
Thematic Issue


In the future, climate change will strongly influence our environment and living conditions. Weather and Climate simulations that predict possible changes produce big data sets. The combination of various variables of climate models with spatial data from different sources helps to identify correlations and to study key processes. In this paper, the results of the Weather Research and Forecasting model are visualized for two regions. For this purpose, a continuous workflow that leads from the integration of heterogeneous raw data to 3D visualizations that can be displayed on a desktop computer or in an interactive virtual reality environment is developed. These easy-to-understand visualizations of complex data are the basis for scientific communication and for the evaluation and verification of models as well as for interdisciplinary discussions of the research results.


Visualization Climate modeling WRF Virtual reality Visualization concept Visualization workflow  OpenGeoSys Data Explorer 



The first author would like to express her gratitude to the European Social Fund (ESF) as part of the program ”Europa fördert Sachsen” for the funding of the scholarship. We thank HIGRADE, the graduate school of UFZ (Bissinger and Kolditz 2008), and CompeTE+ the school for doctoral students at the HTWK. The authors gratefully acknowledge the data support of the Spatial Information and Planning System (RIPS) of the Regional Office for Environment, Measurement and Nature Protection of Baden-Württemberg. Thanks to Dr. Andreas Marx and the climate data support of CERA (Climate and Environmental Retrieving and Archiving) for providing the observation data. The presented work is part of the WESS project, WESS is supported by a grant from the Ministry of Science, Research and Arts of Baden-Württemberg (AZ Zu 33-721.3-2) and the Helmholtz Center for Environmental Research, Leipzig (UFZ). The satellite images were provided by the NERC Satellite Receiving Station, Dundee University, Scotland from


  1. Ahrens J, Geveci B, Law C (2005) ParaView: an end-user tool for large data visualization. In: Hansen C, Johnson C (eds) In the visualization handbook. Oxford, London, pp 717–732CrossRefGoogle Scholar
  2. American Meteorological Society (1993) Guidelines for using color to depict meteorological information: IIPS subcommittee for color guidelines. Bull Am Meteorol Soc 74(9):64–81Google Scholar
  3. Anderson J, Andres J (2010) Voyager: an interactive software for visualizing large, geospatial data sets. Mar Technol Soc J 44(4):8–19CrossRefGoogle Scholar
  4. Andrienko G, Andrienko N et al (2011) Challenging problems of geospatial visual analytics. J Vis Lang Comput 22(4):251–256. doi: 10.1016/j.jvlc.2011.04.001 CrossRefGoogle Scholar
  5. Autodesk: Autodesk VRED 3D (2013).
  6. Barthlott C, Hauck C, Adler GS, Kalthoff N, Kottmeier C (2011) Soil moisture impacts on convective indices and precipitation over complex terrain. Meteorol Z 20(2):185–197. doi: 10.1127/0941-2948/2011/0216 CrossRefGoogle Scholar
  7. Bauer HS, Weusthoff T, Dorninger M, Wulfmeyer V, Schwitalla T, Gorgas T, Arpagaus M, Warrach-Sagi K (2011) Predictive skill of a subset of models participating in D-PHASE in the COPS region. Q J R Meteorol Soc 137(S1):287–305. doi: 10.1002/qj.715 CrossRefGoogle Scholar
  8. Behrendt A, Pal S, Wulfmeyer V, Valdebenito BAM, Lammel G (2011) A novel approach for the characterization of transport and optical properties of aerosol particles near sources part I: measurement of particle backscatter coefficient maps with a scanning UV lidar. Atmos Environ 45(16):2795–2802. doi: 10.1016/j.atmosenv.2011.02.061 CrossRefGoogle Scholar
  9. Bissinger V, Kolditz O (2008) Helmholtz interdisciplinary graduate school for environmental research (HIGRADE). GAIA-Ecol Perspect Sci 17(1):71–73Google Scholar
  10. Catchments As Organized Systems (CAOS) (2013).
  11. Clyne J, Mininni P, Norton A, Rast M (2007) Interactive desktop analysis of high resolution simulations: application to turbulent plume dynamics and current sheet formation. New J Phys 9(8):301–301. doi: 10.1088/1367-2630/9/8/301 CrossRefGoogle Scholar
  12. Edenhofer O, Seyboth K (2013) Intergovernmental panel on climate change. Encycl Energy Nat Resour Environ Econ 1:48–56Google Scholar
  13. Endlicher W, Gerstengabe FW (2007) Der Klimawandel: Einblicke, Rückblicke und Ausblicke. Tech. rep., Humboldt-Universität zu Berlin, Potsdam Institute for Climate Impact ResearchGoogle Scholar
  14. ESRI (1998) ESRI shapefile technical description. Environmental Systems Research Institute, IncGoogle Scholar
  15. Forlines C, Wittenburg K (2010) Wakame: sense making of multi-dimensional spatial-temporal data. In: Proceedings of the international conference on advanced visual interfaces—AVI ’10, ACM Press, New York, New York, USA, p 33. doi: 10.1145/1842993.1843000
  16. Gerstner T, Meetschen D, Crewell S, Griebel M, Simmer C (2002) A case study on multiresolution visualization of local rainfall from weather radar measurements. In: Proceedings of the conference on visualization. IEEE Computer Society, Washington, DC, USA, pp 533–536Google Scholar
  17. Goldstone W (2011) Unity 3.x game development essentials, 2nd edn. Packt Publishing, BirminghamGoogle Scholar
  18. Grathwohl P, Rügner H, Wöhling T et al (2013) Catchments as reactors: a comprehensive approach for water fluxes and solute turn-over. Environ Earth Sci 69(2). doi: 10.1007/s12665-013-2281-7
  19. Gregory J (2003) The CF metadata standard.
  20. Griebel M, Preusser T, Rumpf M, Schweitzer M, Telea A (2004) Flow field clustering via algebraic multigrid. In: IEEE visualization 2004. IEEE Computer Society, pp 35–42. doi: 10.1109/VISUAL.2004.32
  21. Grützun V, Knoth O, Simmel M (2008) Simulation of the influence of aerosol particle characteristics on clouds and precipitation with LM-SPECS: model description and first results. Atmos Res 90(2–4):233–242. doi: 10.1016/j.atmosres.2008.03.002 CrossRefGoogle Scholar
  22. Haase H, Bock M, Hergenröther E (2000) Meteorology meets computer graphics—a look at a wide range of weather visualisations for diverse audiences. Comput Gr 24:391–397CrossRefGoogle Scholar
  23. Helbig C, Rink K, Marx A, Priess J, Frank M, Kolditz O (2012) Visual integration of diverse environmental data : a case study in Central Germany. In: Proceedings of iEMSs Conference 2012, Leipzig, Germany, pp 1–8Google Scholar
  24. Hibbard WL, Anderson J, Foster I et al (1996) Exploring coupled atmosphere--ocean models using Vis5D. Int J High Perform Comput Appl 10(2–3):211–222. doi: 10.1177/109434209601000208 CrossRefGoogle Scholar
  25. Illert A (2009) Infrastructure for spatial Information in Europe (INSPIRE) —status report on the development of implementing rules for geographical names data. United Nations group of experts on geographical names, pp 1–7Google Scholar
  26. Jacob D, Petersen J, Eggert B, Alias A, Christensen OB, Bouwer LM, Braun A et al (2013) EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg Environ Change. doi: 10.1007/s10113-013-0499-2
  27. Johnson C (2004) Top scientific visualization research problems. IEEE Comput Gr Appl 24(4):13–17. doi: 10.1109/MCG.2004.20 CrossRefGoogle Scholar
  28. Johnson C, Sanderson A (2003) A next step: visualizing errors and uncertainty. IEEE Comput Graphics Appl 23(5):6–10. doi: 10.1109/MCG.2003.1231171 CrossRefGoogle Scholar
  29. José RS, Pérez J, González R (2012) Advances in 3D visualization of air quality data. In: Usage, usability, and utility of 3D city models—European COST Action. EDP Sciences, Les Ulis, France, pp 1–9. doi: 10.1051/3u3d/201202002
  30. Keeling SJ (2010) Visualization of the weather-past and present. Meteorol Appl 17(2):126–133. doi: 10.1002/met.208 CrossRefGoogle Scholar
  31. Kolditz O, Bauer S, Bilke L, Böttcher N, Delfs JO, Fischer T, Görke UJ, Kalbacher T, Kosakowski G, McDermott CI, Park CH, Radu F, Rink K, Shao H, Shao HB, Sun F, Sun YY, Singh AK, Taron J, Walther M, Wang W, Watanabe N, Wu Y, Xie M, Xu W, Zehner B (2012) OpenGeoSys: an open-source initiative for numerical simulation of thermo-hydro-mechanical/chemical (THM/C) processes in porous media. Environ Earth Sci 67(2):589–599. doi: 10.1007/s12665-012-1546-x CrossRefGoogle Scholar
  32. Kolditz O, Rink K, Shao H, Kalbacher T, Zacharias S, Dietrich P (2012) International viewpoint and news: data and modelling platforms in environmental earth sciences. Environ Earth Sci 66(4):1279–1284. doi: 10.1007/s12665-012-1661-8 CrossRefGoogle Scholar
  33. Kosara R, Healey C, Interrante V (2003) Thoughts on user studies: why, how, and when. IEEE Comput Gr Appl 23(4):20–25CrossRefGoogle Scholar
  34. Koutek M, van der Neut I, Lemcke K et al (2011) Exploration of severe weather events in virtual reality environments. In: European conference on applications of meteorology EMS Annual Meeting, BerlinGoogle Scholar
  35. Laha B, Bowman D (2012) Identifying the benefits of immersion in virtual reality for volume data visualization. In: Immersive visualization revisited workshop of the IEEE VR conference, pp 1–2Google Scholar
  36. Law M, Collins A (2013) Getting to know ArcGIS for desktop, 3rd edn. Esri Press, Redland, CaliorniaGoogle Scholar
  37. Mahammad S, Ramakrishnan R (2003) GeoTIFF-A standard image file format for GIS applications.
  38. Michalakes J, Dudhia J, Gill D (2004) The weather research and forecast model: software architecture and performance. In: 11th ECMWF workshop on the use of high performance computing in meteorologyGoogle Scholar
  39. Neset T, Johansson J, Linnér B (2009) State of climate visualization, CSPR Report No 09:04. Tech. rep., Centre for Climate Science and Policy Research, Norrköping, SwedenGoogle Scholar
  40. Nocke T, Sterzel T, Böttinger M, Wrobel M (2008) Visualization of climate and climate change data: an overview. In: Digital earth summit on geoinformatics 2008: tools for global change research, Wichmann, pp 226–232Google Scholar
  41. Peng Z, Laramee R (2009) Higher dimensional vector field visualization: a survey. In: Eurographic conference on theory and practice of computer graphics, pp 149–163. doi: 10.2312/LocalChapterEvents/TPCG/TPCG09/149-163
  42. Rew R, Davis G (1990) NetCDF: an interface for scientific data access. IEEE Comput Gr Appl 10(4):76–82CrossRefGoogle Scholar
  43. Rink K, Bilke L, Kolditz O (2013a) Visualisation strategies for environmental modelling data. Environ Earth Sci. doi: 10.1007/s12665-013-2970-2
  44. Rink K, Bilke L, Kolditz O (2013b) Visualisation strategies for modelling and simulation using geoscientific data. In: Workshop on visualisation in environmental sciences (EnvirVis). Eurographics Association, Leipzig, pp 47–51. doi: 10.2312/PE.EnvirVis.EnvirVis13.047-051
  45. Rink K, Fischer T, Selle B, Kolditz O (2012) A data exploration framework for validation and setup of hydrological models. Environ Earth Sci 69(2):469–477. doi: 10.1007/s12665-012-2030-3 CrossRefGoogle Scholar
  46. Schlegel S, Böttinger M, Hlawitschka M, Scheuermann G (2013) Determining and visualizing potential sources of floods. In: Workshop on visualisation in environmental sciences (EnvirVis). Eurographics Association, Leipzig, pp 65–69. doi: 10.2312/PE.EnvirVis.EnvirVis13.065-069
  47. Schroeder W, Martin K, Lorensen B (2006) Visualization toolkit: an object-oriented approach to 3D graphics. Kitware, New YorkGoogle Scholar
  48. Schulzweida U, Kornblueh L (2006) CDO user’s guide. Max-Planck-Institute for Meteorology, HamburgGoogle Scholar
  49. Schwitalla T, Bauer HS, Wulfmeyer V, Aoshima F (2011) High-resolution simulation over central Europe: assimilation experiments during COPS IOP 9c. Q J R Meteorol Soc 137(S1):156–175. doi: 10.1002/qj.721 CrossRefGoogle Scholar
  50. Schwitalla T, Bauer HS, Wulfmeyer V, Zängl G (2008) Systematic errors of QPF in low-mountain regions as revealed by MM5 simulations. Meteorol Z 17(6):903–919. doi: 10.1127/0941-2948/2008/0338 CrossRefGoogle Scholar
  51. Sehili AM, Wolke R, Knoth O, Simmel M, Tilgner A, Herrmann H (2005) Comparison of different model approaches for the simulation of multiphase processes. Atmos Environ 39(23–24):4403–4417. doi: 10.1016/j.atmosenv.2005.02.039 CrossRefGoogle Scholar
  52. Simpson R, LaViola J, Laidlaw D, Forsberg A, van Dam A (2000) Immersive VR for scientific visualization: a progress report. IEEE Comput Gr Appl 20(6):26–52. doi: 10.1109/38.888006 CrossRefGoogle Scholar
  53. Treinish LA (1999) Task-specific visualization design. IEEE Comput Gr Appl 19(5):72–77CrossRefGoogle Scholar
  54. Trembilski A (2001) Two methods for cloud visualisation from weather simulation data. Vis Comput 17(3):179–184. doi: 10.1007/PL00013405 CrossRefGoogle Scholar
  55. University Corporation for Atmospheric Research: NCL (2013).
  56. van Dam A, Laidlaw DH, Simpson RM (2002) Experiments in immersive virtual reality for scientific visualization. Comput Gr 26(4):535–555. doi: 10.1016/S0097-8493(02)00113-9 CrossRefGoogle Scholar
  57. Vautard R, Gobiet A, Jacob D, Belda M, Colette A, Déqué M, Fernández J et al (2013) The simulation of European heat waves from an ensemble of regional climate models within the EURO-CORDEX project. Climate Dynamics 41(9–10):2555–2575. doi: 10.1007/s00382-013-1714-z
  58. Verbree E, Maren GV, Germs R, Jansen F, Kraak MJ (1999) Interaction in virtual world views-linking 3D GIS with VR. Int J Geogr Inf Sci 13(4):385–396. doi: 10.1080/136588199241265 CrossRefGoogle Scholar
  59. Walther M, Bilke L, Delfs JO, Graf T, Grundmann J, Kolditz O, Liedl R (2014) Assessing the saltwater remediation potential of a three-dimensional, heterogeneous, coastal aquifer system. Model verification, application and visualization for transient density-driven seawater intrusion. Environ Earth SciGoogle Scholar
  60. Wang W, Kolditz O (2010) Sparse matrix and solver objects for parallel finite element simulation of multi-field problems. High performance computing and applications, pp 418–425.
  61. Warrach-Sagi K, Schwitalla T, Wulfmeyer V, Bauer HS (2013) Evaluation of a climate simulation in Europe based on the WRFNOAH model system: precipitation in Germany. Clim Dyn 41(3–4):755–774. doi: 10.1007/s00382-013-1727-7 CrossRefGoogle Scholar
  62. Wier S, Meertens C (2008) The GEON integrated data viewer (IDV) for exploration of geoscience data with visualizations. In: American Geophysical Union, Fall Meeting, pp 1–4Google Scholar
  63. Wulfmeyer V, Behrendt A, Kottmeier C et al (2011) The convective and orographically-induced precipitation study (COPS): the scientific strategy, the field phase, and research highlights. Q J R Meteorol Soc 137(S1):3–30. doi: 10.1002/qj.752 CrossRefGoogle Scholar
  64. Zehner B (2008) Landscape visualization in high resolution stereoscopic visualization environments multichannel rendering. In: Digital design in landscape architecture 2008. Proceedings at Anhalt University of Applied Sciences. Wichmann, Heidelberg, pp 224–231Google Scholar
  65. Zehner B, Watanabe N, Kolditz O (2010) Visualization of gridded scalar data with uncertainty in geosciences. Comput Geosci 36(10):1268–1275. doi: 10.1016/j.cageo.2010.02.010 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Carolin Helbig
    • 1
    • 3
    • 4
  • Hans-Stefan Bauer
    • 2
  • Karsten Rink
    • 1
  • Volker Wulfmeyer
    • 2
  • Michael Frank
    • 4
  • Olaf Kolditz
    • 1
    • 3
  1. 1.Department of Environmental InformaticsHelmholtz Centre for Environmental Research, UFZLeipzigGermany
  2. 2.Institute of Physics and MeteorologyUniversity of HohenheimStuttgartGermany
  3. 3.Faculty of Environmental SciencesTechnical University DresdenDresdenGermany
  4. 4.Faculty of Computer Science, Mathematics and Natural SciencesUniversity of Applied Sciences LeipzigLeipzigGermany

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