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VR Visualisation as an Interdisciplinary Collaborative Data Exploration Tool for Large Eddy Simulations of Biosphere-Atmosphere Interactions

  • Gil Bohrer
  • Marcos Longo
  • David J. Zielinski
  • Rachael Brady
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5358)

Abstract

Scientific research has become increasingly interdisciplinary, and clear communication is fundamental when bringing together specialists from different areas of knowledge. This work aims at discussing the role of fully immersive virtual reality experience to facilitate interdisciplinary communication by utilising the Duke Immersive Virtual Environment (DiVE), a CAVE-like system, to explore the complex and high-resolution results from the Regional Atmospheric Modelling System-based Forest Large-Eddy Simulation (RAFLES) model coupled with the Ecosystem Demography model (ED2). VR exploration provided an intuitive environment to simultaneously analyse canopy structure and atmospheric turbulence and fluxes, attracting and engaging specialists from various backgrounds during the early stages of the data analysis. The VR environment facilitated exploration of large multivariate data with complex and not fully understood non-linear interactions in an intuitive and interactive way. This proved fundamental to formulate hypotheses about tree-scale atmosphere-canopy-structure interactions and define the most meaningful ways to display the results.

Keywords

Virtual Reality Large Eddy Simulation Atmospheric Boundary Layer Forest Canopy Canopy Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    Metzger, N., Zare, R.N.: Science policy - Interdisciplinary research: From belief to reality. Science 283(5402), 642–643 (1999)CrossRefGoogle Scholar
  2. 2.
    Riva, G.: Applications of virtual environments in medicine. Methods Inf. Med. 42(5), 524–534 (2003)Google Scholar
  3. 3.
    Desmeulles, G., et al.: The virtual reality applied to biology understanding: The in virtuo experimentation. Expert Syst. Appl. 30(1), 82–92 (2006)CrossRefGoogle Scholar
  4. 4.
    Ausburn, L.J., Ausburn, F.B.: Desktop virtual reality: A powerful new technology for teaching and research in industrial teacher education. Journal of Industrial Teacher Education 41(4), 33–58 (2004)Google Scholar
  5. 5.
    Smith, S.S., et al.: Experiences in using virtual reality in design and graphics classrooms. Int. J. Eng. Educ. 23(6), 1192–1198 (2007)Google Scholar
  6. 6.
    Bohrer, G.: Large eddy simulations of forest canopies for determination of biological dispersal by wind. In: Department of Civil and Environmental Engineering, p. 150, Duke University, Durham, NC (2007)Google Scholar
  7. 7.
    Bohrer, G., et al.: Effects of canopy heterogeneity, seed abscission, and inertia on wind-driven dispersal kernels of tree seeds. J. Ecol. 96, 569–580 (2008)CrossRefGoogle Scholar
  8. 8.
    Dupont, S., Brunet, Y.: Simulation of turbulent flow in an urban forested park damaged by a windstorm. Bound. Layer. Meteor. 120(1), 133–161 (2006)CrossRefGoogle Scholar
  9. 9.
    Dupont, S., Brunet, Y.: Edge flow and canopy structure: A large-eddy simulation study. Bound Layer Meteor. 126(1), 51–71 (2008)CrossRefGoogle Scholar
  10. 10.
    Yue, W., et al.: Large-eddy simulations of plant canopy flows using plant-scale representation. Bound Layer Meteor. 124(2), 183–203 (2007)CrossRefGoogle Scholar
  11. 11.
    Yue, W.S., et al.: A comparative quadrant analysis of turbulence in a plant canopy. Water Resour. Res. 43(5) (2007)Google Scholar
  12. 12.
    Yue, W., et al.: Turbulent kinetic energy budgets in a model canopy: comparisons between LES and wind-tunnel experiments. Environ. Fluid Mech. 8, 73–95 (2008)CrossRefGoogle Scholar
  13. 13.
    Cruz-Neira, C., et al.: The CAVE - audio-visual experience automatic virtual environment. Commun. ACM 35(6), 64–72 (1992)CrossRefGoogle Scholar
  14. 14.
    Visage Imaging. AMIRA (2008) (cited July 7, 2008), http://www.amiravis.com
  15. 15.
    Bergeron, T.: Synoptic meteorology - an historical review. Pure Appl. Geophys. 119(3), 443–473 (1981)CrossRefGoogle Scholar
  16. 16.
    Hibbard, W.L., Santek, D.A.: The VIS-5D system for easy interactive visualization. In: Proceedings of Visualization 1990. IEEE CS Press, Los Alamitos (1990)Google Scholar
  17. 17.
    Hibbard, W.L., et al.: Interactive visualization of earth and space science computations. Computer 27(7), 65–72 (1994)CrossRefGoogle Scholar
  18. 18.
    Mathematics and Computer Science Division Argonne National Laboratory. Cave5D Release 2.0 (2007) (cited June 30, 2008), http://www-unix.mcs.anl.gov/~mickelso/CAVE2.0.html
  19. 19.
    Johnson, S.G., Edwards, J.: Vis5d+ (2001) (cited June 30, 2008), http://vis5d.sourceforge.net/
  20. 20.
    Desert Research Institute. CAVCaM (2008) (cited July 9, 2008), http://www.cavcam.dri.edu/
  21. 21.
    Roswintiarti, O., Raman, S.: Three-dimensional simulations of the mean air transport during the 1997 forest fires in Kalimantan, Indonesia using a mesoscale numerical model. Pure Appl. Geophys. 160(1-2), 429–438 (2003)CrossRefGoogle Scholar
  22. 22.
    Magsig, M.A., Snow, J.T.: Long-distance debris transport by tornadic thunderstorms. Part I: The 7 may 1995 supercell thunderstorm. Mon. Weather Rev. 126(6), 1430–1449 (1998)CrossRefGoogle Scholar
  23. 23.
    Bramer, D.J., et al.: Linking interactive concept models into the Visual Geophysical Exploration Environment (VGEE). In: 13th Symposium on Education. American Meteorological Society, Seattle (2004)Google Scholar
  24. 24.
    Semeraro, D., et al.: Collaboration, analysis, and visualization of the future. In: 20th International conference on interactive information and processing system (IIPS) for meteorology, oceanography, and hydrology. American Meteorological Society, Seattle (2004)Google Scholar
  25. 25.
    Loth, E., et al.: A virtual reality technique for multi-phase flows. Int. J. Comput. Fluid Dyn. 18(3), 265–275 (2004)zbMATHCrossRefGoogle Scholar
  26. 26.
    Nichol, J., Wong, M.S.: Modeling urban environmental quality in a tropical city. Landsc. Urban Plan. 73(1), 49–58 (2005)CrossRefGoogle Scholar
  27. 27.
    Sen, S.I., Day, A.M.: Modelling trees and their interaction with the environment: A survey. Comput. Graph.-UK 29(5), 805–817 (2005)CrossRefGoogle Scholar
  28. 28.
    Pretzsch, H., et al.: Models for forest ecosystem management: A European perspective. Ann. Bot. 101(8), 1065–1087 (2008)CrossRefGoogle Scholar
  29. 29.
    Van Haevre, W., Bekaert, P.: A simple but effective algorithm to model the competition of virtual plants for light and space. J. WSGC 11(3), 464–471 (2003)Google Scholar
  30. 30.
    Deussen, O., et al.: Interactive visualization of complex plant ecosystems. In: Proceedings of Visualization. IEEE, Los Alamitos (2002)Google Scholar
  31. 31.
    Seifert, S.: Visualisierung von waldlandschaften. Allgemeine Forstzeitschrift/Der Wald 61, 1170–1171 (2006)Google Scholar
  32. 32.
    Pielke, R.A., et al.: A Comprehensive meteorological modeling system - RAMS. Meteorol. Atmos. Phys. 49(1-4), 69–91 (1992)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Deardorff, J.W.: Stratocumulus-capped mixed layers derived from a 3-dimensional model. Bound Layer Meteor. 18(4), 495–527 (1980)CrossRefGoogle Scholar
  34. 34.
    Bhushan, S., Warsi, Z.U.A.: Large eddy simulation of turbulent channel flow using an algebraic model. Int. J. Numer. Methods Fluids 49(5), 489–519 (2005)zbMATHCrossRefGoogle Scholar
  35. 35.
    Palace, M., et al.: Amazon forest structure from IKONOS satellite data and the automated characterization of forest canopy properties. Biotropica 40(2), 141–150 (2008)CrossRefGoogle Scholar
  36. 36.
    Bohrer, G., et al.: A Virtual Canopy Generator (V-CaGe) for modeling complex heterogeneous forest canopies at high resolution Tellus 59(3), 566–576 (2007)Google Scholar
  37. 37.
    Medvigy, D., et al.: Mass conservation and atmospheric dynamics in the regional atmospheric modeling system (RAMS). Environ. Fluid Mech. 5(1-2), 109–134 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Gil Bohrer
    • 1
  • Marcos Longo
    • 2
  • David J. Zielinski
    • 3
  • Rachael Brady
    • 3
  1. 1.department of Civil and Environmental Engineering and Geodetic ScienceOhio State UniversityColumbus
  2. 2.Department of Earth and Planetary SciencesHarvard UniversityCambridge
  3. 3.Visualization Technology GroupDuke University

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