Variability at low frequencies with wavelet transform and empirical mode decomposition: Aplication to climatological series

The aim of this study is to detect variability at low frequencies and trend of time series connected with climate using two different processing techniques. In previous work the wavelet transform and models of pure oscillations with statistical parameter setting were applied to the series of surface...

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Main Authors: Zitto, M.E., Piotrkowski, R., Barrucand, M., Canziani, P.
Format: Conference Object
Language:unknown
Subjects:
Online Access:https://hdl.handle.net/20.500.12110/paper_97814673_v_n_p_Zitto
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spelling ftunibueairesbd:todo:paper_97814673_v_n_p_Zitto 2023-10-29T02:32:12+01:00 Variability at low frequencies with wavelet transform and empirical mode decomposition: Aplication to climatological series Zitto, M.E. Piotrkowski, R. Barrucand, M. Canziani, P. https://hdl.handle.net/20.500.12110/paper_97814673_v_n_p_Zitto unknown http://hdl.handle.net/20.500.12110/paper_97814673_v_n_p_Zitto info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar empirical mode decomposition Orcadas temperature time series wavelet transform Atmospheric temperature Information science Wavelet decomposition Complementary characteristics Empirical mode decomposition method Long-term behavior Processing technique Stationary components Statistical parameters Surface temperatures Wavelet transforms CONF ftunibueairesbd https://doi.org/20.500.12110/paper_97814673_v_n_p_Zitto 2023-10-05T01:35:11Z The aim of this study is to detect variability at low frequencies and trend of time series connected with climate using two different processing techniques. In previous work the wavelet transform and models of pure oscillations with statistical parameter setting were applied to the series of surface temperatures of the Orcadas Antarctic Station (Argentina) over 110 years. Periods of about 20 and 50 years were detected. The analysis highlighted the limitations of the usual calculations of trend involving a few decades if there is present a significant variability. Periods of the order of 150-200 years or more were also obtained, although they do not represent scales with physical meaning but the best simple oscillation which fits the nonlinear tendency. To improve the understanding of the long term behavior of the temperature series, the empirical mode decomposition method was applied in the present work to the same data and the trend or stationary component was obtained with more precision. The result of the comparison of trends was promising. It is advantageous to apply different methods to the same series in order to reveal complementary characteristics. © 2015 IEEE. Fil:Zitto, M.E. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Piotrkowski, R. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Barrucand, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Canziani, P. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Conference Object Antarc* Antarctic Biblioteca Digital FCEN-UBA (Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires)
institution Open Polar
collection Biblioteca Digital FCEN-UBA (Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires)
op_collection_id ftunibueairesbd
language unknown
topic empirical mode decomposition
Orcadas temperature
time series
wavelet transform
Atmospheric temperature
Information science
Wavelet decomposition
Complementary characteristics
Empirical mode decomposition method
Long-term behavior
Processing technique
Stationary components
Statistical parameters
Surface temperatures
Wavelet transforms
spellingShingle empirical mode decomposition
Orcadas temperature
time series
wavelet transform
Atmospheric temperature
Information science
Wavelet decomposition
Complementary characteristics
Empirical mode decomposition method
Long-term behavior
Processing technique
Stationary components
Statistical parameters
Surface temperatures
Wavelet transforms
Zitto, M.E.
Piotrkowski, R.
Barrucand, M.
Canziani, P.
Variability at low frequencies with wavelet transform and empirical mode decomposition: Aplication to climatological series
topic_facet empirical mode decomposition
Orcadas temperature
time series
wavelet transform
Atmospheric temperature
Information science
Wavelet decomposition
Complementary characteristics
Empirical mode decomposition method
Long-term behavior
Processing technique
Stationary components
Statistical parameters
Surface temperatures
Wavelet transforms
description The aim of this study is to detect variability at low frequencies and trend of time series connected with climate using two different processing techniques. In previous work the wavelet transform and models of pure oscillations with statistical parameter setting were applied to the series of surface temperatures of the Orcadas Antarctic Station (Argentina) over 110 years. Periods of about 20 and 50 years were detected. The analysis highlighted the limitations of the usual calculations of trend involving a few decades if there is present a significant variability. Periods of the order of 150-200 years or more were also obtained, although they do not represent scales with physical meaning but the best simple oscillation which fits the nonlinear tendency. To improve the understanding of the long term behavior of the temperature series, the empirical mode decomposition method was applied in the present work to the same data and the trend or stationary component was obtained with more precision. The result of the comparison of trends was promising. It is advantageous to apply different methods to the same series in order to reveal complementary characteristics. © 2015 IEEE. Fil:Zitto, M.E. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Piotrkowski, R. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Barrucand, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Canziani, P. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
format Conference Object
author Zitto, M.E.
Piotrkowski, R.
Barrucand, M.
Canziani, P.
author_facet Zitto, M.E.
Piotrkowski, R.
Barrucand, M.
Canziani, P.
author_sort Zitto, M.E.
title Variability at low frequencies with wavelet transform and empirical mode decomposition: Aplication to climatological series
title_short Variability at low frequencies with wavelet transform and empirical mode decomposition: Aplication to climatological series
title_full Variability at low frequencies with wavelet transform and empirical mode decomposition: Aplication to climatological series
title_fullStr Variability at low frequencies with wavelet transform and empirical mode decomposition: Aplication to climatological series
title_full_unstemmed Variability at low frequencies with wavelet transform and empirical mode decomposition: Aplication to climatological series
title_sort variability at low frequencies with wavelet transform and empirical mode decomposition: aplication to climatological series
url https://hdl.handle.net/20.500.12110/paper_97814673_v_n_p_Zitto
genre Antarc*
Antarctic
genre_facet Antarc*
Antarctic
op_relation http://hdl.handle.net/20.500.12110/paper_97814673_v_n_p_Zitto
op_rights info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/2.5/ar
op_doi https://doi.org/20.500.12110/paper_97814673_v_n_p_Zitto
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