Análise de Fourier e Wavelets aplicada à temperatura do ar em diferentes tipologias de ocupação

Abstract

RESUMOComumente, séries temporais de temperatura do ar são usadas nos estudos de clima devido à natureza temporal de seus sinais, que requerem investigações baseadas no espaço tempo-frequência a fim de revelar características espectrais em função do tempo. A transformada Wavelet fornece a representação precisa de um sinal em função do tempo e da frequência, simultaneamente, além de permitir analisar qualquer tipo de sinal ao qual a Série de Fourier se limita em determinadas análises. Desta forma, o presente trabalho fez uma análise da série temporal da temperatura do ar utilizando a série de Fourier e a transformada de Wavelet em quatro pontos, três de área urbana e um de área rural, em 2007. A intensidade de energia na alta escala de frequência para a temperatura do ar foi 24 h verificando-se potências significativas no decorrer do ano todo, nas escalas temporais de 2 a 32 dias, para os quatro pontos de estudo cuja variância mais acentuada foi no centro da área urbana. Palavras-chave: microclima, série temporal, Wavelet de MorletFourier analysis and Wavelet applied to air temperature in different types of occupation ABSTRACT Temperature time-series are commonly used to study the climate and due to the temporal nature of their signals, require investigations based on time-frequency space in order to reveal the spectral characteristics as a function of time and the Wavelet transform provides an accurate representation of a signal as a function of time and frequency simultaneously, as well as, to analyse any signal, to which the Fourier series is limited to certain tests. Thus, this study made a descriptive analysis using the monthly series of Fourier and Wavelet transform of the variable air temperature at four points of the study, three urban and one rural area, for the year 2007. For the variable air temperature it was found that the energy intensity in the high frequency was 24 and significant powers over the whole year was verified, in time scales from 2 to 32 days for the four studied points, whose variance is more pronounced at the center of urban area.

Topoclimate in Morlet, Cross and Coherence Wavelet Analyses in the Brazilian Cerrado

“…Wavelets have mobile windows in time and space that expand or contract to capture low and high-frequency signals, which helps in the analysis of the microclimatic variables adopted (Ehlers, 2009). According to Vilani & Sanches (2013), Addison (2017) 0 w = 6 was adopted to satisfy the condition of admissibility, meaning that, for this value, the errors due to measurement not equal to zero, are less than the typical rounding errors (Domingues et al, 2016).…”

Analysis of the wind regime at high spatial-temporal resolution in the itacaiúnas river watershed, eastern amazon

“…Studies such as these are important in supporting environmental diagnoses, in addition to providing knowledge of small-scale mechanisms that influence local circulation and the regional climate (Bitencourt et al, 2016). Thus, the WT has been used in several studies on atmospheric phenomena (Echer et al, 2008;Blain and Kayano 2011;Moura and Vitorino, 2012;Vilani and Sanches, 2013;Rocha et al, 2018;Rocha et al, 2019), hydrological systems (Sassi et al, 2011;Santos et al, 2013;Guo et al, 2015;Sun et al, 2016), and tidal studies (Danial et al, 2019).…”

“…In the WT analysis, the continuous line in the shape of a cone varying in both axes of graphics, which is called the cone of influence, encompasses areas with significant variances at a 95% level; i.e., the periods outside this cone should be disregarded because they do not have adequate statistical confidence (Vilani and Sanches, 2013). The highest frequencies are at the top of the y-axis, and low frequencies are found at the bottom of the graph (Figures 6A-E).…”

Wavepress is a user-friendly toolkit for computational chemistry, drug design and data analysis

“…The decomposition of a function using Wavelets is known as the Wavelet transform and has its variants, continuous and discrete, for the present software (Wavepress) the discrete wavelet transform (DWT) is used. 1,5 It is well-known that as DWT is a mathematical procedure that converts a signal into a distinct form, this conversion is important because it can reveal the hidden characteristics of the signal and can represent the original signal in a more succinct way.…”