Open Access
Subscription Access
Open Access
Subscription Access
Four-Layer Spherical Self-Organized Maps Neural Networks Trained by Recirculation to Follow the Phase Evolution of a Nearly Four-Year Rainfall Signal
Subscribe/Renew Journal
This work is intended to organize a big set of time series of rainfall reanalysis built on the Fourier harmonic that corresponds to the 4.8-year cycle of variability. To do that a self-organized map is implemented in four spherical layers trained by recirculation. The methodology is shortly described. It is used to organize time series on grid point around the Earth to follow the phase evolution of the signal. The phase and amplitude are the main criterion for organization. It is shown how the successive layers contain more general abstractions, their representativeness around the Globe and in regional scale. The main objective is to show how to use the neural network tool to follow the phase evolution of the signal around the Globe. It is described as an anomaly with highest amplitude in the central Pacific Ocean, this evolution and return after 4.8 years.
Keywords
Neural Network, Spherical Self-Organized Maps, Recirculation, Signal Analysis. Phase Evolution, Rainfall Reanalysis, Climate Variability.
Subscription
Login to verify subscription
User
Font Size
Information
- N. Jiang, J. David Neelin and M. Ghil, “Quasi-Quadriennial and Quasi-Biennial Variability in the Equatorial Pacific”, Climate Dynamics, Vol. 12, No. 2, pp. 101-112, 1995.
- A. Garcia, F.J. Lorente, P.M. Lorente and M.J. Ortiz Bevia, “Quasi Quadrennial Variations in European Precipitation”, International Journal of Climatology, Vol. 32, No. 9, pp. 1295-1309, 2012
- Y.M. Tourre et al., “Western European Climate, and Pinot Noir Grape Harvest Dates in Burgundy, France, since the 17th Century”, Climate Research, Vol. 46, No. 3, pp. 243-253, 2011.
- M. Kimoto, “Studies of Climate Variability using General Circulation Models”, Available at: https://www.terrapub.co.jp/e-library/toyota/pdf/049.pdf, Accessed on 2001.
- D.A. Huggenberger, “Four-Layer Spherical Self-Organized Maps Neural Networks Trained by Recirculation to Simulate Perception and Abstraction Activity: Application to Patterns of Rainfall Global Reanalysis”, ICTACT Journal on Soft Computing, Vol. 8, No. 4, pp. 1733-1749, 2018.
- P.A.A. Alessandro, “Identificación Caracterización de Situaciones de Bloqueo a través de un Mapa Auto Organizado de Redes Neuronales”, Available at: http://www.sbmet.org.br/cbmet2010/artigos/396_64437.pdf Accessed on 2010.
- G.E. Hinton and J.L. Mcclelland, “Learning Representation by Recirculation”, Proceedings of IEEE Conference on Neural Information Processing Systems, pp. 358-366, 1988.
- E. Kalnay et al, “The NCEP/NCAR 40-Year Reanalysis Project”, Advancing Science, Serving Society, Vol. 77, pp. 437-470, 1996.
Abstract Views: 197
PDF Views: 0