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| Titel |
Conjugate fluctuation analysis for a set of 41 magnetic clouds measured by the ACE spacecraft |
| VerfasserIn |
A. Ojeda González, W. D. Gonzalez, O. Mendes, M. O. Domingues, R. R. Rosa |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
2198-5634
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics Discussions ; 1, no. 1 ; Nr. 1, no. 1 (2014-04-11), S.583-613 |
| Datensatznummer |
250115086
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| Publikation (Nr.) |
 copernicus.org/npgd-1-583-2014.pdf |
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| Zusammenfassung |
| The statistical distribution of values in the signal and the autocorrelations
(interpreted as the memory or persistence) between values are attributes of
a time series. The autocorrelation function values are positive in a~time
series with persistence, while it are negative in a time series with anti
persistence. The persistence of values with respect to each other can be
strong, weak, or nonexistent. A strong correlation implies a "memory" of
previous values in the time series. The long-range persistence in time series
could be studied using semivariograms, rescaled-range, detrended fluctuation
analysis and Fourier spectral analysis, respectively. In this work the
persistence analysis has been used to study IMF time series. We use data from
the IMF GSM-components with time resolution of 16 s. Time intervals
corresponding to distinct processes around 41 MCs in the period between
March 1998 and December 2003 were selected. In this exploratory study the
purpose with this selection is to deal with the cases presenting the three
periods: plasma sheath, MC and post-MC. We calculated one exponent of
persistence (e.g., α, β, Hu, Ha) over the
previous three time intervals. The persistence exponent values increased
inside cloud regions, and it was possible select the following threshold
values: 〈α(j)〉 =1.392; 〈Ha(j)〉 =
0.327; 〈Hu(j)〉 =0.875. These values are
useful as another test to evaluate the quality of the identification. If the
cloud is well-structured, then the persistence exponents values exceed
thresholds. In 80.5% of the cases studied, these tools were able to
separate the region of the cloud from neighboring regions. The Hausdorff
exponent (Ha) provides the best results. |
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