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

Paper:

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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

Received:
December 9, 2008
Accepted:
February 9, 2009
Published:
May 20, 2009
Creative Commons License
Keywords:
huge climate data, spherical self-organizing map
Abstract
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, H. 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.
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