Open AccessNonlinear noise reduction for the observation data of climatology based on the searching average method
Author(s) -
Hou Wei,
Yi Liu,
Guolin Feng
Publication year - 2007
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.56.589
Subject(s) - noise reduction , noise (video) , gaussian noise , reduction (mathematics) , nonlinear system , computer science , white noise , value noise , gradient noise , algorithm , additive white gaussian noise , mathematics , noise measurement , physics , artificial intelligence , noise floor , telecommunications , geometry , quantum mechanics , image (mathematics)
The searching average nonlinear noise reduction method, which is based on local linear fit to the nonlinear dynamics, is introduced to reduce the noise in the observation data of climatology. Recurrence plots are used to estimate the size of local neighbors. The noise reduction is improved markedly. In order to show the validity of the program in noise reduction, it is first applied to a noise time series of Henon map contaminated by Gaussian white noise. And then, this noise reduction scheme is applied separately to the observation data of meteorology. The analyses of a nonlinear prediction demonstrate the efficiency of the method for noise reduction.