Abstract
The complexity of the physics of the atmosphere makes it hard to evaluate the temporal evolution of weather patterns. We are also limited by the available computing power, disk, and memory space. As the technology in hardware and software advances, new tools are being developed to simulate weather conditions to make predictions more accurate. We also need to be able to visualize the data we obtain from climate model runs, to better understand the relationship between the variables driving the evolution of weather systems. Two tools that have been developed to visualize climate data are Vis5D and Cave5D. This paper discusses the process of taking data in the MM5 format, converting it to a format recognized by Vis5D and Cave5D, and then visualizing the data. It also discusses some of the changes we have made in these programs, including making Cave5D more interactive and rewriting Cave5D and Vis5D to use larger data files. Finally, we discuss future research concerning the use of these programs.
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Keywords
- Virtual Environment
- Weather Pattern
- Argonne National Laboratory
- Configuration File
- Virtual Reality Environment
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.
References
Pennsylvania State University / National Center for Atmospheric Research, MM5 Home Page http://www.mmm.ucar.edu/mm5/mm5-home.html, 1999.
Taylor, J. Argonne National Laboratory, Climate Workbench http://wwwclimate.mcs.anl.gov/proj/climate/public-html/climate-workbench.html, 2000.
Space Science and Engineering Center University of Wisconsin-Madison, Vis5D Version 5.1 ftp://www.ssec.wisc.edu/pub/Vis5D/README, 1999.
Old Dominion University, Cave5D Release 1.4 http://www.ccpo.odu.edu/~Cave5D/Cave5DGuide.html, 1998.
Larson, L. W. Hydrological Research Laboratory, The Great USA Flood of 1993 http://www.nwrfc.noaa.gov/floods/papers/oh-2/great.htm, 1996.
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© 2001 Springer-Verlag Berlin Heidelberg
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Voelz, S.A., Taylor, J. (2001). Visualizing High-Resolution Climate Data. In: Alexandrov, V.N., Dongarra, J.J., Juliano, B.A., Renner, R.S., Tan, C.J.K. (eds) Computational Science — ICCS 2001. ICCS 2001. Lecture Notes in Computer Science, vol 2073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45545-0_30
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DOI: https://doi.org/10.1007/3-540-45545-0_30
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