Using independent component analysis to separate signals in climate data | Zendy

Imola K. Fodor | Zendy; Chandrika Kamath | Zendy

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Using independent component analysis to separate signals in climate data

Author(s) -

Imola K. Fodor,

Chandrika Kamath

Publication year - 2003

Publication title -

proceedings of spie, the international society for optical engineering/proceedings of spie

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.192

H-Index - 176

eISSN - 1996-756X

pISSN - 0277-786X

DOI - 10.1117/12.487277

Subject(s) - robustness (evolution) , independent component analysis , parametric statistics , computer science , parametric model , context (archaeology) , climate model , volcano , synthetic data , climatology , environmental science , data mining , econometrics , climate change , machine learning , algorithm , statistics , mathematics , geology , paleontology , biochemistry , chemistry , gene , seismology , oceanography

Global temperature series have contributions from difierent sources, such as volcanic eruptions and El Ni~no Southern Oscillation variations. We investigate independent component analysis as a technique to separate unrelated sources present in such series. We flrst use artiflcial data, with known independent components, to study the conditions under which ICA can separate the individual sources. We then illustrate the method with climate data from the National Centers for Environmental Prediction.

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