A methodology for estimating spectral indices to fluctuation measurements of ionospheric parameters

Abstract

Spectral analysis is a technique largely used to study scale size regime of ionospheric plasma irregularities based on in situ measurements, notwithstanding the visual representation of power spectral density (PSD) of a signal is often a source of ambiguity during fitting routines and identification of breakpoints. In this work, a method is proposed in order to mitigate the uncertainties inherent to this process. Here, the spectral behavior of time series fluctuations is alternatively investigated using Detrended Fluctuation Analysis (DFA). The DFA algorithm is a scaling analysis procedure widely applied to estimate the detection of long-range correlation without considering apparent short-range ones. Furthermore, the DFA technique is able to remove trends implicit to the signal and to be applied to non-stationary time series. Using in situ measurements of both ionospheric electron density and electric field fluctuations, it was able to analyze plasma bubbles with scales ranging from 1.66 km to 12.4 m. The results show that DFA and PSD routines provide quite similar spectra, but different spectral indices. On the other hand, the spectra revealed steep slopes wrapping the medium scales, a characteristic also detected in other studies. Besides that, the DFA is less noisy than Fourier spectra, which allows a more precise identification of spectral breakpoints.