Wavelet_analysis

Wavelet_analysis is a repository of python scripts to carry out spectral decomposition of tiemseries using a set of wavelet basis functions. The methodology is discussed extensively in Terrence and Compo et al.

Mathematical Approach

The wavelet transform of a uniform 1-dimensional time series, x, of length N and timestep δt is given by the convolution between the series and a scaled and translated version of a wavelet function ψ₀

$W_n(s) = \sum^{N - 1}_{n' = 0} x_{n'} \psi^* \bigg[(n' - n) \frac{\delta t}{s}\bigg],$

where s is the wavelet scale indicating the frequency of the wavelet. Varying s and translating along the time scale (the index n), W_n indicates the amplitude of signals at different scales and their variation in time. The scale s is increased in powers of 2 according to

$s_j = s_0 2^{j \deltaj}, j = 0, 1, ..., J$

$J = \delta j^{-1} log_2\bigg(\frac{N \delta t}{s_0}\bigg),$

where s₀ is the shortest resolvable scale of a signal, J corresponds to the longest and δj is the scale resolution. The translated and scaled wavelet has the form

$\psi^* \bigg[(n' - n) \frac{\delta t}{s}\bigg] = \bigg(\frac{\delta t}{s}\bigg)^{1/2} \psi_0\bigg[(n' - n) \frac{\delta t}{s}\bigg]$

a common form of &psi₀ is as a Morlet wavelet, an oscillatory function enveloped by a Gaussian which is expressed as

$\psi_0(p) = \pi^{-1/4} e^{i\omega_0 p} e^{\frac{p^2}{2}}.$

It is computationally quicker to compute the wavelet transform in discrete Fourier space. By the convolution theorem, the transform reduces to multiplication

$W_n(s) = \sum^{N - 1}_{k = 0} \hat{x}_{k} \hat{\psi}^* (s\omega_k) e^{i \omega_k n \delta t},$

where \hat{x}_{k} and \hat{&psi} are the discrete Fourier transforms of the time series x and the wavelet function respectively,

$\hat{x}_k = \frac{1}{N} \sum^{N-1}_{n = 0} x_n e^{\frac{-2\pi i k n}{N}}$

$\hat{\psi}(s\omega_k) = \bigg(\frac{2 \pi s}{\delta t}\bigg) \pi^{-1/4}H(\omega_k) e^{-(s\omega_k - \omega_0)^2/2}.$

H(&omega_k;) is the Heavyside function. The square modulus of the wavelet transform gives the wavelet power spectrum which indicates relative strength of signals in the time series as a function of signal period and discretised time. We also define a confidence interval for wavelet power observed at a given period and time for a series by assuming a mean background spectrum corresponding to that of a first order autoregressive (AR1, red noise) process modelled by

$x_n = \alpha x_{n - 1} z_n,$

where α is the lag-1 autocorrelation of the time series and z_n is Gaussian white noise. such a process's wavelet power spectrum is χ ² distributed and therefore can be used to define a 95% confidence interval for any observed power. Additionally, the cross wavelet spectrum of two time series x and y with associated wavelet spectra Wx_n and Wy_n gives a measure of coincident power (the same period at the same timepoints) between the series. It is given by

$\vert W^{xy}_n(s)\vert = \vert W^{x*}_n(s) W^{y}_n(s)\vert,$

where Wx_n is the complex conjugate of the wavelet power spectrum of x. The complex argument of W^xy_n gives the local phase difference between signals in x and y in frequency-time space. The phase relationship between the two time-series can be represented by a vector that subtends an angle representing the phase difference: On all plots of cross spectra, arrows to the right (left) denoted signals which are in-phase and correlated (anti-correlated). Vertical arrows indicate a phase relationship of π/2 between the time-series, so that the evolution of one is correlated with the rate-of-change of the other. As for individual power spectra, we define a confidence interval for which cross power of a larger amplitude is deemed significant (>95% confidence interval) by comparing power exhibited by actual series with a theoretical red noise process. The cross power of two such AR1 processes is theoretically distributed such that the probability of obtaining cross power greater than a set of red-noise processes is

$D\bigg(\frac{\vert W^{xy}_n(s)\vert}{\sigma_x \sigma_y} < p\bigg) = \frac{Z_\nu(p)}{\nu} \sqrt{P^x_k P^y_k},$

where σ denotes the standard deviation of the time series, Z is the confidence interval defined by p (Z = 3.999 for 95% confidence), ν is the degrees of freedom for a real wavelet spectrum (ν = 2) and P_k is the theoretical Fourier spectrum of the AR1 process. For a given wavenumber k, this can be expressed as

$P_k = \frac{1 - \alpha^2}{\vert 1 - \alpha e^{2i\pi k} \vert^2}.$

Files

wavelet_lib.py - script containing functions to compute the wavelet transform, power spectra and significance tests on a timeseries. Functions are based on source code of the waipy package developed by Mabel Calim Costa (mabelcalim).

wavelet_plot.py - functions to plot wavelet spectra and cross spectra

wavelet_analysis.py - script to load data, call wavelet functions and output spectra

PDO_wavelet_UKESM_SON_DJF_5_yr_window.png - sample output of wavelet power spectra from climate model data

cross_coherence_SSWs_PDO_SON_UKESM.png - sample output of wavelet cross power spectra from climate model data.

Usage

This repository has been used for analysis in Dimdore-Miles et al. 2020 which analyses long term variability in stratospheric circulation indices.

oscardm20994 / Wavelet_analysis

Wavelet_analysis

Mathematical Approach

Files

Usage

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