1998
DOI: 10.1175/1520-0493(1998)126<2466:aowttm>2.0.co;2
|View full text |Cite
|
Sign up to set email alerts
|

Application of Wavelet Transform to Meteosat-Derived Cold Cloud Index Data over South America

Abstract: Cold cloud index (CCI) data derived from Meteosat infrared imagery are used to detect periodicities in convective activity in South America. The generally used Fourier transform (FT) cannot provide time-localized information but gives information on the average periodicity of oscillations over the entire time domain. As many events in the atmosphere are intermittent, wavelet transform (WT) is used to identify periodic events in CCI data. First, the Morlet WT is applied to different combinations of time series … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
5
0

Year Published

2002
2002
2020
2020

Publication Types

Select...
8

Relationship

0
8

Authors

Journals

citations
Cited by 9 publications
(5 citation statements)
references
References 27 publications
0
5
0
Order By: Relevance
“…The correct choice of the mother wavelet is essential, as this choice influences the time and frequency resolution of the results. In this study, we use the Morlet wavelet as the mother wavelet, which consists on a plane wave modulated by a Gaussian (Chapa et al, 1998;Huang et al, 1998), and it is given by…”
Section: Wavelet Transform Analysismentioning
confidence: 99%
“…The correct choice of the mother wavelet is essential, as this choice influences the time and frequency resolution of the results. In this study, we use the Morlet wavelet as the mother wavelet, which consists on a plane wave modulated by a Gaussian (Chapa et al, 1998;Huang et al, 1998), and it is given by…”
Section: Wavelet Transform Analysismentioning
confidence: 99%
“…The algorithm used is described by Torrence and Compo (1998) and based on the most commonly used Morlet wavelet (Chapa et al 1998;Huang et al 1998). The continuous wavelet transform of a discrete time series x n is defined as the convolution of x n with a scaled and translated mother wavelet c to give…”
Section: B Wavelet Analysismentioning
confidence: 99%
“…Unlike the Fourier transform, the wavelet transform localizes a signal in both the frequency and time domains and is well suited to analysis of multiscale, nonstationary time series that result from nonlinear interactions between several physical processes occurring on a range of temporal and spatial scales (Lau and Weng 1995;Webster and Hoyos 2004). The wavelet transform uses generalized base functions (wavelets) that can be stretched and translated with a flexible resolution (e.g., Weng and Lau 1994;Torrence and Compo 1998) and is increasingly employed to decompose geophysical time series into time-frequency space to detect periodicities and trends (e.g., Wang and Wang 1996;Baliunas et al 1997;Chapa et al 1998, Barrett and Leslie 2009) and to develop statistical prediction models (e.g., Webster and Hoyos 2004;Mwale and Gan 2005).…”
Section: Introductionmentioning
confidence: 99%
“…Torrence and Compo (1998) name several choices that should be taken into account when choosing the wavelet: i) orthogonal or non-orthogonal wavelets; ii) complex or real wavelets (complex wavelets are also largely used in geophysics, e.g. Spedding et al (1993), Chapa et al (1998), Rao and Murthy (2001); iii) the width of the wavelet (or compact support); and iv) the shape of the wavelet; in fact, the shape of the wavelet should be chosen accordingly to the characteristics of the data to analyse, because the results change when using different wavelet forms. There are other methods to choose the more adequate waveform.…”
Section: Discrete Wavelet Transformsmentioning
confidence: 99%