Effect of data gaps: comparison of different spectral analysis methods

Munteanu, Costel; Negrea, Cătălin; Echim, Marius; Мурсула, К.

doi:10.5194/angeo-34-437-2016

Cited by 37 publications

(32 citation statements)

References 26 publications

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“…The fast Fourier transform requires regularly sampled data without gaps (e.g., Munteanu et al 2016). Therefore, we fill gaps within each thread using linear interpolation.…”

Section: Methodology Of the Nuwt Codementioning

confidence: 99%

An Automated Algorithm for Identifying and Tracking Transverse Waves in Solar Images

Weberg

Morton

McLaughlin

2018

ApJ

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Recent instrumentation has demonstrated that the solar atmosphere supports omnipresent transverse waves, which could play a key role in energizing the solar corona. Large-scale studies are required in order to build up an understanding of the general properties of these transverse waves. To help facilitate this, we present an automated algorithm for identifying and tracking features in solar images and extracting the wave properties of any observed transverse oscillations. We test and calibrate our algorithm using a set of synthetic data, which includes noise and rotational effects. The results indicate an accuracy of 1%-2% for displacement amplitudes and 4%-10% for wave periods and velocity amplitudes. We also apply the algorithm to data from the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory and find good agreement with previous studies. Of note, we find that 35%-41% of the observed plumes exhibit multiple wave signatures, which indicates either the superposition of waves or multiple independent wave packets observed at different times within a single structure. The automated methods described in this paper represent a significant improvement on the speed and quality of direct measurements of transverse waves within the solar atmosphere. This algorithm unlocks a wide range of statistical studies that were previously impractical.

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“…The fast Fourier transform requires regularly sampled data without gaps (e.g., Munteanu et al 2016). Therefore, we fill gaps within each thread using linear interpolation.…”

Section: Methodology Of the Nuwt Codementioning

confidence: 99%

An Automated Algorithm for Identifying and Tracking Transverse Waves in Solar Images

Weberg

Morton

McLaughlin

2018

ApJ

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“…Data gaps can influence correlation studies (George et al, ). For many applications such as spectral analysis, it is desirable or necessary to fill the data gaps and a number of methods for doing this are available, but one has to remain aware of the implications of the method used for the application in question (Henn et al, ; Munteanu et al, ; Sturges, ; Wynn & Wickwar, ). Such gap filling techniques have been applied to solar wind data by, for example, Kondrashov et al (, ), but many require a proxy data set for either interpolation or testing purposes.…”

Section: Introductionmentioning

confidence: 99%

The Development of a Space Climatology: 1. Solar Wind Magnetosphere Coupling as a Function of Timescale and the Effect of Data Gaps

et al. 2019

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Different terrestrial space weather indicators (such as geomagnetic indices, transpolar voltage, and ring current particle content) depend on different coupling functions (combinations of near-Earth solar wind parameters), and previous studies also reported a dependence on the averaging timescale, τ. We study the relationships of the am and SME geomagnetic indices to the power input into the magnetosphere P α , estimated using the optimum coupling exponent α, for a range of τ between 1 min and 1 year. The effect of missing data is investigated by introducing synthetic gaps into near-continuous data, and the best method for dealing with them when deriving the coupling function is formally defined. Using P α , we show that gaps in data recorded before 1995 have introduced considerable errors into coupling functions. From the near-continuous solar wind data for 1996-2016, we find that α = 0.44 ± 0.02 and no significant evidence that α depends on τ, yielding P α ∝B 0.88 V sw 1.90 (m sw N sw ) 0.23 sin 4 (θ/2), where B is the interplanetary magnetic field, N sw the solar wind number density, m sw its mean ion mass, V sw its velocity, and θ the interplanetary magnetic field clock angle in the geocentric solar magnetospheric reference frame. Values of P α that are accurate to within ±5% for 1996-2016 have an availability of 83.8%, and the correlation between P α and am for these data is shown to be 0.990 (between 0.972 and 0.997 at the 2σ uncertainty level), 0.897 ± 0.004, and 0.790 ± 0.03, for τ of 1 year, 1 day, and 3 hr, respectively, and that between P α and SME at τ of 1 min is 0.7046 ± 0.0004.Plain Language Summary This is the first step of three toward constructing a climatology describing the statistics of how space weather has varied over the past 400 years. This climatology will be valuable in the design of systems vulnerable to space weather. To do this, we here investigate how best to quantify the power extracted from the solar wind by the magnetosphere. We need to do this over a range of timescales from the annual averages used to describe long-term changes (space climate) down to fluctuations over minutes and hours, which drive space weather events.A great many combinations of near-Earth interplanetary parameters (so-called coupling functions) have been proposed over many years to describe the transfer of energy, and/or mass, and/or momentum, and/or LOCKWOOD ET AL. 133

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“…Occasional small data gaps are linearly interpolated. Munteanu et al (2016) have shown that it is a reasonable approach for estimating the PSD, except for the highest frequencies, which are not a matter of concern here. The main result is a familiar double power law P f f µ g -( ) whose spectral index γ approaches 1 at low frequencies and is closer to 1.7 in the inertial range.…”

Section: Power-law Scaling Of the Psdmentioning

confidence: 90%

Inherentness of Non-stationarity in Solar Wind

Jagarlamudi

Wit

Krasnoselskikh

et al. 2019

ApJ

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Most studies of turbulence in the solar wind invoke stationarity as a working hypothesis. Unfortunately, this concept is difficult to verify in practice. To investigate the validity of the weak stationarity assumption we consider magnetic field measurements made by the WIND satellite and study the properties of the autocorrelation function (ACF), which is a classical gauge for characteristic times or scales. We find that the ACF suffers from a high variance, which precludes the routine interpretation of correlation times and scales. In addition, the ACF fails to converge toward a constant function, even when considering the longest available intervals of either fast or slow solar wind. The reasons behind this lack of convergence are better understood by considering the power spectral density (PSD) of the magnetic field and analyzing synthetic data that exhibit the same PSD. Interestingly, we find evidence for anf −1 scaling at low frequencies in both fast and slow solar winds. These results, together with the theoretical properties of processes withf −γ scaling all point to the non-stationary behavior of the solar wind, in particular for scales that correspond to the inertial range. They also impose strong constraints on the applicability of ACF analysis as a tool for characterizing statistical properties of solar wind turbulence.

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Effect of data gaps: comparison of different spectral analysis methods

Cited by 37 publications

References 26 publications

An Automated Algorithm for Identifying and Tracking Transverse Waves in Solar Images

An Automated Algorithm for Identifying and Tracking Transverse Waves in Solar Images

The Development of a Space Climatology: 1. Solar Wind Magnetosphere Coupling as a Function of Timescale and the Effect of Data Gaps

Inherentness of Non-stationarity in Solar Wind

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