JACIII Vol.13 No.3 pp. 210-216
doi: 10.20965/jaciii.2009.p0210


Visualization of Huge Climate Data with High-Speed Spherical Self-Organizing Map

Kanta Tachibana*, Norihiko Sugimoto**,
Hideo Shiogama***, and Toru Nozawa***

*Nagoya University

**Keio University

***National Institute for Environmental Studies

December 9, 2008
February 9, 2009
May 20, 2009
huge climate data, spherical self-organizing map

We propose the use of a high-speed spherical self-organizing map (HSS-SOM) to visualize climate variability as a complementary alternative to empirical orthogonal function (EOF) analysis. EOF analysis, which is the same as principal component analysis, is often used in the fields of meteorology and climatology to extract leading climate variability patterns, its production of linear mapping with only a low contribution rate may preclude producing any meaningful results. Due to computational limitations, however, conventional self-organizing maps are difficult to apply to huge climate datasets. The development of HSS-SOMs with dynamically growing neurons has helped reduce computational time. After demonstrating validity of our HSS-SOM using observational climate data and HSS-SOM effectiveness as a complementary alternative to the EOF, we extract dominant atmospheric circulation patterns from huge amounts of climate data in the general circulation model, in which both present climatology and future climate are simulated. These patterns correspond to those obtained in previous studies, indicating the HSS-SOM’s usefulness in climate research.

Cite this article as:
K. Tachibana, N. Sugimoto, <. Shiogama, and T. Nozawa, “Visualization of Huge Climate Data with High-Speed Spherical Self-Organizing Map,” J. Adv. Comput. Intell. Intell. Inform., Vol.13, No.3, pp. 210-216, 2009.
Data files:
  1. [1] D. A. Randell and R. A. Wood, “Climate Models and Their Evaluation,” In S. D. Solomon et al. (Eds.), Climate Change 2007: The Physical Science Basis, IPCC AR4, pp. 589-662, Cambridge University Press, Cambrigde, 2008.
  2. [2] S. Corti, F. Molteni, and T. N. Palmer, “Signature of recent climate change in frequencies of natural atmospheric circulation regimes,” Nature, 398, pp. 799-802, 1999.
  3. [3] H. Itoh, “True versus apparent arctic oscillation,” Geophys. Res. Lett., 29, pp. 1268-1271, 2002.
  4. [4] T. Kohonen, “Self-organizing maps,” Springer, 3rd edition, 2001.
  5. [5] K. Fujimura, “Self-organizing maps: application to climate,” Res. Note of Meteor., 203, pp. 109-145, 2002 (in Japanese).
  6. [6] D. B. Reusch, R. B. Alley, and B. C. Hewitson, “Relative performance of self-organizing maps and principal component analysis in pattern extraction from synthetic climatological data,” Polar Geogr., 29, pp. 188-212, 2005.
  7. [7] T. Cavazos, “Using self-organizing maps to investigate extreme climate events: An application to wintertime precipitation in the Bankans,” J. Climate, 13, pp. 1718-1732, 2000.
  8. [8] W. J. Gutowski et al., “Diagnosis and attribution of a seasonal precipitation deficit in a U. S. regional climate simulation,” J. Hydrometeor., 5, pp. 230-242, 2004.
  9. [9] B. C. Hewitson and R. G. Crane, ”Self-organizing maps: applications to synoptic climatology,” Climate Res., 22, pp. 13-26, 2002.
  10. [10] J. J. Cassano, P. Uotila, and A. Lynch, “Changes in synoptic weather patterns in the polar regions in the twentieth and twenty-first centuries, part 1: Arctic,” Int. J. Climatol., 26, pp. 1027-1049, 2006.
  11. [11] A. Lynch, P. Uotila, and J. J. Cassano, “Changes in synoptic weather patterns in the polar regions in the twentieth and twenty-first centuries, part 2: Antarctic,” Int. J. Climatol., 26, pp. 1181-1199, 2006.
  12. [12] D. B. Reusch, R. B. Alley, and B. C. Hewitson, “North Atlantic climate variability from a self-organizing map perspective,” J. Geophys. Res., 112, pp. 1-20, 2007.
  13. [13] K-1 Model Developers, “K-1 coupled GCM (MIROC) description,” Technical report, Cent. For Clim. Syst. Res., Univ. of Tokyo, Tokyo, 2001.
  14. [14] IPCC, “Special Report on Emissions Scenarios,” Cambridge Univ. Press, Cambrigde, 2000.
  15. [15] K. Tachibana and T. Furuhashi, “Self-organizing map with generating and moving neurons in visible space,” Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.11, No.6 pp. 626-632, 2007.
  16. [16] N. Sugimoto and K. Tachibana, “A first attempt to apply high speed self-organizing map to huge climate datasets,” SOLA, 4, pp. 41-44, 2008.

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Last updated on Jun. 18, 2019