Characterization of the Sq and Cross-Wavelet analysis of the geomagnetic activity at three stations in the Brazilian Sector

2011 ◽  
Author(s):  
David Oliva ◽  
Andres R. R. Papa
Keyword(s):  
Wavelet Analysis ◽  
Geomagnetic Activity ◽  
Cross Wavelet Analysis ◽  
Cross Wavelet

scholarly journals Nitrous oxide fluxes over establishing biofuel crops: Characterization of temporal variability using the cross‐wavelet analysis

GCB Bioenergy ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 756-770
Author(s):  
Marcelo Zeri ◽  
Wendy H. Yang ◽  
Gisleine Cunha‐Zeri ◽  
Christy D. Gibson ◽  
Carl J. Bernacchi
Keyword(s):  
Nitrous Oxide ◽  
Wavelet Analysis ◽  
Temporal Variability ◽  
Biofuel Crops ◽  
The Cross ◽  
Cross Wavelet Analysis ◽  
Cross Wavelet

scholarly journals Wavelet analysis of the magnetotail response to solar wind fluctuations during HILDCAA events

2019 ◽  
Vol 37 (5) ◽  
pp. 919-929
Author(s):  
Adriane Marques de Souza Franco ◽  
Ezequiel Echer ◽  
Mauricio José Alves Bolzan
Keyword(s):  
Solar Wind ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Geomagnetic Field ◽  
Field Component ◽  
Long Duration ◽  
Analysis Technique ◽  
Spacecraft Mission ◽  
Cross Wavelet Analysis ◽  
Cross Wavelet

Abstract. In this work a study of the effects of the high-intensity long-duration continuous AE activity (HILDCAAs) events in the magnetotail was conducted. The aim of this study was to search the main frequencies during HILDCAAs in the Bx component of the geomagnetic field in the magnetotail, as well as the main frequencies, at which the magnetotail responds to the solar wind during these events. In order to conduct this analysis the wavelet transform was employed during nine HILDCAA events that coincided with Cluster spacecraft mission crossing through the tail of the magnetosphere from 2003 to 2007. The most energetic periods for each event were identified. It was found that 76 % of them have periods ≤4 h. With the aim to search the periods that have the highest correlation between the IMF Bz (OMNI) component and the Cluster Bx geomagnetic field component, the cross wavelet analysis technique was also used in this study. The majority of correlation periods between the Bz (IMF) and Bx component of the geomagnetic field observed also were ≤4 h, with 62.9 % of the periods. Thus the magnetotail responds stronger to IMF fluctuations during HILDCCAS at 2–4 h scales, which are typical substorm periods. The results obtained in this work show that these scales are the ones on which the coupling of energy is stronger, as well as the modulation of the magnetotail by the solar wind during HILDCAA events.

scholarly journals MULTIFRACTAL CROSS WAVELET ANALYSIS

Fractals ◽  
2017 ◽  
Vol 25 (06) ◽  
pp. 1750054 ◽  
Author(s):  
ZHI-QIANG JIANG ◽  
XING-LU GAO ◽  
WEI-XING ZHOU ◽  
H. EUGENE STANLEY
Keyword(s):  
Wavelet Analysis ◽  
Long Range ◽  
Cross Correlation ◽  
Multifractal Spectrum ◽  
Theoretical Formula ◽  
Cross Correlations ◽  
The Cross ◽  
Cross Wavelet Analysis ◽  
Multifractal Behavior ◽  
Cross Wavelet

Complex systems are composed of mutually interacting components and the output values of these components usually exhibit long-range cross-correlations. Using wavelet analysis, we propose a method of characterizing the joint multifractal nature of these long-range cross correlations, a method we call multifractal cross wavelet analysis (MFXWT). We assess the performance of the MFXWT method by performing extensive numerical experiments on the dual binomial measures with multifractal cross correlations and the bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. For binomial multifractal measures, we find the empirical joint multifractality of MFXWT to be in approximate agreement with the theoretical formula. For bFBMs, MFXWT may provide spurious multifractality because of the wide spanning range of the multifractal spectrum. We also apply the MFXWT method to stock market indices, and in pairs of index returns and volatilities we find an intriguing joint multifractal behavior. The tests on surrogate series also reveal that the cross correlation behavior, particularly the cross correlation with zero lag, is the main origin of cross multifractality.

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