Employing the Hilbert–Huang Transform to analyze observed natural complex signals: Calm wind meandering cases

• Published in: Physica A Vol. 462; pp. 1189 - 1196
• Main Authors: Martins, Luis Gustavo Nogueira, Stefanello, Michel Baptistella, Degrazia, Gervásio Annes, Acevedo, Otávio Costa, Puhales, Franciano Scremin, Demarco, Giuliano, Mortarini, Luca, Anfossi, Domenico, Roberti, Débora Regina, Costa, Felipe Denardin, Maldaner, Silvana
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.11.2016
• Subjects: Hilbert–Huang Transform; Low-wind; Natural complex signals; Stable boundary layer; Wind meandering; Wind meandering; Hilbert–Huang Transform; Natural complex signals; Stable boundary layer; Low-wind
• ISSN: 0378-4371; 1873-2119
• DOI: 10.1016/j.physa.2016.06.147

In this study we analyze natural complex signals employing the Hilbert–Huang spectral analysis. Specifically, low wind meandering meteorological data are decomposed into turbulent and non turbulent components. These non turbulent movements, responsible for the absence of a preferential direction of the horizontal wind, provoke negative lobes in the meandering autocorrelation functions. The meandering characteristic time scales (meandering periods) are determined from the spectral peak provided by the Hilbert–Huang marginal spectrum. The magnitudes of the temperature and horizontal wind meandering period obtained agree with the results found from the best fit of the heuristic meandering autocorrelation functions. Therefore, the new method represents a new procedure to evaluate meandering periods that does not employ mathematical expressions to represent observed meandering autocorrelation functions. •We propose a new methodology to investigate the occurrences of turbulence and meandering movements.•We use the Hilbert–Huang Transform to find the characteristic meandering time scale.•We employ wind data measured in a PBL to obtain meandering marginal spectra.