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 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.

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

2019 ◽  
Author(s):  
Adriane Marques de Souza Franco ◽  
Ezequiel Echer ◽  
Mauricio José Alves Bolzan
Keyword(s):  
Solar Wind ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
High Intensity ◽  
Geomagnetic Field ◽  
Long Duration ◽  
Analysis Technique ◽  
The Cross ◽  
Cross Wavelet Analysis ◽  
Cross Wavelet

Abstract. In this work a study of the effects of the High-Intensity Long-Duration Continuous AE activity events (HILDCAAs) 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, as well as at the main frequencies which the magnetotail responds to the solar wind during these events. In order to conduct this analysis the wavelet transform was employed in 9 HILDCAA events that occurred after the Cluster mission (2000) and coincided with the Cluster crossing through the tail of the magnetosphere from 2003 to 2007. Altogether, 25 most energetic periods was observed, which 76 % are ≤ 4 hours. The cross wavelet analysis technique was also used for the development of this study. It was applied to data of the Bz-IMF component and the Bx geomagnetic component, searching to obtain the periods in that had the highest correlation between these two series. To obtain these periods is important to identify frequencies on which the coupling of energy is stronger, as well the modulation of the magnetotail by the solar wind during HILDCAA events. The majority of correlation periods between the Bz (IMF) and Bx component of the geomagnetic field observed also were ≤ 4 hours, with 62.9 % of the periods. Thus the magnetotail responds stronger to IMF fluctuations during HILDCCAS at 2–4 hours scales, which are typical substorm periods.

ON THE CROSS WAVELET ANALYSIS OF DUFFING OSCILLATOR

1999 ◽  
Vol 228 (1) ◽  
pp. 199-210 ◽  
Author(s):  
A. KYPRIANOU ◽  
W.J. STASZEWSKI
Keyword(s):  
Wavelet Analysis ◽  
Duffing Oscillator ◽  
The Cross ◽  
Cross Wavelet Analysis ◽  
Cross Wavelet

scholarly journals Spatial and Temporal Variability of Canadian Seasonal Streamflows

2005 ◽  
Vol 18 (1) ◽  
pp. 191-210 ◽  
Author(s):  
Paulin Coulibaly ◽  
Donald H. Burn
Keyword(s):  
Correlation Analysis ◽  
Wavelet Analysis ◽  
Temporal Variability ◽  
Spatial And Temporal Variability ◽  
Western Canada ◽  
Climatic Indices ◽  
Cross Wavelet Analysis ◽  
Intense Activity ◽  
Seasonal Streamflow ◽  
Cross Wavelet

Abstract Wavelet and cross-wavelet analysis are used to identify and describe spatial and temporal variability in Canadian seasonal streamflows, and to gain insights into the dynamical relationship between the seasonal streamflows and the dominant modes of climate variability in the Northern Hemisphere. Results from applying continuous wavelet transform to mean seasonal streamflows from 79 rivers selected from the Canadian Reference Hydrometric Basin Network (RHBN) reveal striking climate-related features before and after the 1950s. The span of available observations, 1911–99, allows for depicting variance and covariance for periods up to 12 yr. Scale-averaged wavelet power spectra are used to simultaneously assess the temporal and spatial variability in each set of 79 seasonal streamflow time series. The most striking feature, in the 2–3-yr period and in the 3–6-yr period—the 6–12-yr period is dominated by white noise and is not considered further—is a net distinction between the timing and intensity of the temporal variability in autumn, winter, and spring–summer streamflows. It is found that the autumn season exhibits the most intense activity (or variance) in both the 2–3- and the 3–6-yr periods. The spring–summer season corresponds to the least intense activity for the 2–3-yr period, but it exhibits more activity than winter for the 3–6-yr period. Cross-wavelet analysis is provided between the seasonal streamflows and three selected climatic indices: the Pacific–North America (PNA), the North Atlantic Oscillation (NAO), and the sea surface temperature series over the Niño-3 region (ENSO3). The wavelet cross-spectra reveal strong climate–streamflow activity (or covariance) in the 2–6-yr period starting after 1950 whatever the climatic index and the season. Prior to 1950, local and weaker 2–6-yr activity is revealed in central and western Canada essentially in winter and autumn, but overall a non-significant streamflow–climate relationship is observed prior to 1950. Correlation analysis in the 2–6-yr band between the seasonal streamflow and the selected climatic indices revealed strong positive correlations with the ENSO in the spring–summer and winter seasons for the post-1950 period for both eastern and western Canada. A similar correlation pattern is revealed in the west with the NAO, while in the east moderate negative NAO correlations are observed only in the autumn season prior to 1950. After 1950 strong NAO correlations emerge for all the seasons. The cross-wavelet spectra and the correlation analysis in the 2–6-yr band suggest the presence of a change point around 1950 in the east and west seasonal streamflows.

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