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Dynamic spectral characteristics of highresolution simulated equatorial plasma bubbles
Progress in Earth and Planetary Science volume 5, Article number: 83 (2018)
Abstract
Manifestations of severe nighttime equatorial ionospheric disturbances have been observed for decades. It is generally accepted that the phenomena are caused by large depletions, referred to as equatorial plasma bubbles (EPBs), which are initiated on the rising unstable bottom side of the nighttime F layer. Physicsbased simulations have enhanced our understanding of the EPB phenomenon. However, until very recently, stochastic structure smaller than ∼ 10 km was not well resolved. Recent highresolution EPB simulations have extended the resolution to hundreds of meters, which provides a unique opportunity to characterize intermediatescale EPB structure.This paper presents a summary analysis of simulated highresolution intermediatescale EPB structure. Estimation of altitudedependent power law spectral density function parameters provides an altitude versus time history of the intermediatescale structure development. Local structure onset is associated with successive bifurcation of rising EPBs. Developed structure characterized by a twocomponent power law spectral density function ultimately subtends several hundred kilometers in altitude.Twocomponent inverse powerlaw structure was first observed in early in situ rocket measurements. It has been observed in diagnostic measurements of beaconsatellite and GPS scintillation data as well as in situ measurements from Atmospheric Explorer and C/NOFS satellites. The EPB simulation data fully support the reported EPB diagnostics as well as a correlation between the turbulent strength and the largescale spectral index parameter estimates. However, recent analyses have shown that the correlation is an intrinsic property of powerlaw parameter estimation.
Introduction
The terminology equatorial spread F (ESF), plumes, and equatorial plasma bubbles (EPBs) evolved, respectively, from ionospheric sounder, coherent radar backscatter, and diagnostic measurements. In situ and remote EPB radiopropagation diagnostics are formally time series generated by the motion of the probe or the interrogating propagation path. Interpreting such diagnostic measurements is challenging because altitude, magnetic field, and temporal structure variations are invariably intermingled. Moreover, timetospace conversion depends on an unknown structure drift. The dependence of propagation diagnostics on pathintegrated structure further complicates the interpretation of diagnostic measurements.
Physicsbased simulations provide an exceptional opportunity to generate definitive structure development measurements. Although the underlying physics has been well established for decades, simulating the generation and dissipation of steep gradients that evolve in unstable regions has, until very recently, limited the resolution that could be achieved to kilometer scales. Recently, simulations that exploit advanced computational capabilities have resolved EPB structure to hundreds of meters.
Methods/Experimental
Highresolution simulations described in a survey paper by Yokoyama (2017) were made available for the EPB structure analysis presented in this paper. Yokoyama (2017) also reviewed the historical development of EPB simulations, which were introduced in the early 1980s. To make the EPB simulations as representative of realworld conditions as possible, multiple EPBs were initiated with an eastward E×B drift. The number of EPBs initiated depends on the initial conditions. However, the simulation analyzed in this paper shows that intermingling of structure from multiple EPBs populates extended structure regions.
The conditions under which EPBs develop and how the largescale structure evolution can be reconciled with diagnostic measurements, particularly the coherentbackscatter radar echoes that delineate the plumes, have been studied extensively (Hysell 2000). Less attention has been given to characterizing intermediatescale stochastic structure from tens of kilometers to hundreds of meters. Figure 1 is a perspective view of the developed EPB structure. The left frame shows the central meridian plane slice, which emphasizes smoothly varying fieldaligned structure. The right frame shows vertical and horizontal slice planes, which emphasize stochastic crossfield structure.
Stochastic structure is definitively reproduced only in slice planes that cut across field lines. For this study, evolving structure from the equatorial slice plane and two offset slice planes, identified by the rays in Fig. 2, were extracted from the threedimensional simulations. The electron density variation in Fig. 2 shows that fieldaligned structure intercepts systematically varying background electron density. The offset slice planes allow exploration of the fieldaligned structure translation.
The left frame in Fig. 3 shows developed equatorialplane structure detail 1 h after initiation. Seeded bottomside perturbations initiated five EPBs that evolved at different rates depending on their zonal location at initiation. Slice planes were sampled uniformly from 300 to 800 km with 1120 zonal samples at 333.56 m and 1821 vertical samples at 700.83 m. The right frame shows the zonal average electron density (blue) with an overlaid smoothed profile (red). Figures 4 and 5 show the offset1 and offset2 structure summaries. The structure flux tubes intercept the offset planes at progressively lower altitudes. The structure in the offset2 slice plane is mapped below the 300 km lower limit.
The zonal average electron densities shown in the right frames of Figs. 3, 4, and 5 are proportional to zonal pathintegrated total electron content (TEC), which can be measured with navigation satellite transmissions received by low orbiting satellite occultations (Tsai et al. 2011). Although such measurements do not resolve the intermediatescale structure directly, scintillation of the probing signals can be processed for structure diagnostics.
To summarize the structure evolution, the slice frames were cyclically shifted to compensate for the 120 mps eastward drift. Periodic simulation boundary conditions confined the zonal extent of the realizations. The residual formed by subtracting the smoothed average profiles provides a measure of the heightdependent structure development. The time resolution for the EPB simulations is 0.1 s but reported at 10 s intervals. Figure 6 summarizes the evolution of the structure residuals at 100 s intervals. Structure onset can be identified at a specific time and altitude, which is the point where the highest EPB penetrates the Fregion peak electron density.
To explore the structure onset detail, Fig. 7 shows four consecutive 10 s zoomed images of the most rapidly progressing central EPB. The steepening gradient at the head of the EPB generates local depletions flanked by enhancements, which are referred to as bifurcations. Each bifurcation initiates a secondary bifurcation. The process of successive bifurcation creates a fractallike structure cascade. The progression in Fig. 4 shows that once initiated, successive bifurcation proceeds very rapidly. A more detailed discussion can be found in Yokoyama et al. (2014).
Structure characterization
EPB electron density sliceplane realizations are formally twodimensional scalar fields, N_{e}(y,z), where y and z represent crossfield and altitude, respectively. Assuming that N_{e}(y,z) is statistically homogeneous, the stochastic structure can be characterized by a twodimensional spectral density function (SDF), which is formally the expectation of the intensity of twodimensional Fourier decompositions of N_{e}(y,z) realizations.
Powerlaw models
Published in situ measurements and remote diagnostics imply an underlying twocomponent powerpower law SDF. The following analytic representation is introduced to guide structure characterization:
where
is the magnitude of the spatial frequency vector [q_{y},q_{z}] in radians per meter. The β coefficient accommodates projection of the radial variation of fieldaligned structure. The defining parameters are turbulent strength, C_{s}; the break frequency, q_{0}; and the spectral indices, p_{n} corresponding to subranges of spatial frequencies smaller than (n=1) and larger than (n=2) q_{0}.
In situ measurements are onedimensional scans. If the structure volume were stochastic in all three dimensions, the measured onedimensional SDF would be represented by a twodimensional integration of the threedimensional SDF. For fieldaligned twodimensional stochastic structures a slice plane containing the onedimensional scan must be constructed. Configurationspace realizations populate arbitrarily oriented slice planes for extrapolation (Rino et al. 2018). For the EPB analysis, the crossfield orientation of the slice planes were selected for direct structure measurement. Onedimensional SDFs are related to (1) by the integration
For (3) to be well defined, the powerlaw variation must be specified in more detail. In the EPB realizations, there is a transition from stochastic to trendlike variation at small spatial frequencies. At sufficiently high frequencies, the physics supporting the EPB simulations is incomplete. Furthermore, as already noted, the stochastic structure itself varies with altitude. To capture these details, the following heightdependent onedimensional SDF hypothesized for EPB structure characterization:
The onedimensional model captures a broad range of structure characteristics as defined by the turbulent strength C_{s}, the spectral indices η_{n}, and the break frequency q_{0}. For example, if η_{1}≃0, q_{0} can be interpreted as an outer scale. If η_{1}≃η_{2}, the SDF is a single power law. Generally, η_{1}≤η_{2}. However, enhanced lowfrequency structure might lead to the opposite ordering, η_{1}>η_{2}. In all cases, the variation of C_{s} provides a measure of overall structure intensity.
Establishing the relation between η_{n} and p_{n}, which is nominally η_{n}=p_{n}−1, is beyond the scope of this study. However, ionospheric structure models can be validated by comparing predicted heightdependent onedimensional structure characteristics with the measured EPB structure.
Irregularity parameter estimation
Irregularity parameter estimation (IPE) systematically adjusts the defining parameters to minimize a measure of the disparity between an SDF estimate and the theoretical SDF. An IPE procedure for estimating scintillation intensity SDF parameters was introduced by Carrano and Rino (2016). The original IPE procedure was refined to maximize the likelihood that the periodogram was derived from a realization with the theoretical SDF (Carrano et al. 2017). For characterizing the EPB SDFs, the maximum likelihood estimation (MLE) procedure was adapted for powerlaw SDF estimation as described in Rino and Carrano (2018). Powerlaw parameter estimation is more challenging than intensity scintillation parameter estimation because of the singular behavior of unmodified powerlaw SDFs at zero frequency.
The MLE SDF estimate is the average of M periodograms, formally
where the periodogram is defined as
The index n corresponds to the spatial frequencies
where Δy is the y sample interval, and Δq=2π/(NΔy) is the spatialfrequency resolution. The index l identifies the altitude at which the zonal scan is extracted. One can show that
MLE exploits the fact that the probability distribution function (PDF) of the periodogram is well approximated by a χ distribution with 2 degrees of freedom. The χ distribution with 2M degrees of freedom follows for the summation.
It is well known that periodogram estimates are contaminated by the sidelobes of endpoint discontinuities. Moreover, efficient discrete Fourier transformation (DFT) evaluation requires N to be even with as many factors as possible, ideally a power of 2. The Welch method (Welch 1995) uses windowing and segmentation with averaging. Periodogram variants, such as maximum entropy estimates (Fougere 2009), provide additional variants. However, MLE relies on unbiased spectral estimates with χ distributions, whereby it is desirable to stay as close to (6) as possible. After some exploration, it was found that using the full 373.6 km y extent of the data zero extended to a nice FFT number gave the best results. Following (Rino and Carrano 2018), periodograms from two altitudes (M=2) were averaged.
Multiparameter MLE used a MATLAB implementation of the NelderMead simplex algorithm (Olsen and Nelsen 1975). The procedure is surprisingly robust in that fits were made to quasideterministic SDFs with no stochastic structure as well SDFs from realizations with fully developed stochastic structure. The two classes are readily distinguished by the reported IPE parameters.
Results
MLEIPE with M=2 was performed over each set of sliceplane scans. Figures 8, 9, and 10 summarize the parameter estimates. The periodogram sample interval is twice the height sampling (1.4 km). The C_{s}>200 estimates shown in the upper frames of Figs. 8, 9, and 10 capture the unstructured regions identified in Fig. 6. From the second and third frames in Figs. 8, 9, and 10, we see that η_{1}<η_{2} within the unstructured regions. Outside the structured regions, the pattern is reversed. For ease of interpretation, σ_{b}=2π/q_{0} is reported rather than the break frequency.
Figure 11 compares representative equatorial sliceplane SDFs extracted from the unstructured (upper frame) and structured (lower frame) altitude ranges. The smoothly varying structure generates enhanced structure at the lower spatial frequencies with sidelobes populating the higher frequencies. The MLEIPE parameters capture the SDF envelope with η_{1}>η_{2} and a break frequency scale near 5 km. The developed structure populates the lower frequencies more uniformly with a more rapid decrease at higher frequencies. MLEIPE captures the structure with η_{1}<η_{2} and larger C_{s} values. The transition from unstructured to structured SDFs necessarily includes SDFs with η_{1}≃η_{2}.
The break scale estimates within the structured region as summarized in the lower frames of Figs. 8, 9, and 10 show more variability. Movie presentations of slice plane realizations and the associated SDFs highlight the intermingling of the largescale EPB structure with the background. The several kilometer size of the bifurcations shown in Fig. 7 appears to be a lower bound on the break frequency. However, the intermingling of the EPBs with the background evidently modulates the structure.
Discussion
The simulations reveal abrupt structure onset as a transition from quasideterministic structure with a steep lowfrequency powerlaw index to the more representative twocomponent SDF structure with η_{1}<η_{2}. To the extent that field lines mapped from the equatorial plane are captured in the offset planes, the structure characteristics are nearly identical.
To summarize the stochastic structure characteristics, Figs. 12, 13, and 14 show probability distributions of the structure with CsdB>200 dB. The developed structure is fairly uniform. The η_{1} and η_{2} distributions show peaks just below 1.5 and just above 2.5. These values are consistent with the C/NOFS results reported by Rino et. al (2016). They are also consistent with the parameters Retterer (2010) used in the PBMOD ionospheric scintillation model, with allowance for the relation p_{n}=η_{n}+1. Moreover, EPB C_{s} range is comparable to the reported C/NOFS values when the unscaled C/NOFS values are translated to common electron density units.
The C/NOFS break scales reported by Rino et. al (2016) are smaller than the break scales from the EPB analysis. One possibility is the resolution of wavelet scale spectra used for the C/NOFS analysis. Alternatively, as noted in the previous section, the EPB break scale is evolving and sensitive to the background structure. Precise measurement of the initiation and evolution of the break scale is a topic for targeted special study.
Transition populations with η_{1}≃η_{2} are very small. However, the simulated background structure is idealized and might not represent realworld background structure. Bhattacharyya et al. (2003) showed that the latitudinal dependence of EPB structure can be explained by invoking a single powerlaw structure in the lower Fregion, with attendant smaller scintillation levels.
The offset1 and offset2 PDFs shown in the lower frames of Figs. 13 and 14 have peaks between 4 and 5 km, which is consistent with the dimensions of the initial bifurcations shown in Fig. 7. The distribution of larger break scales is associated with the aforementioned intermingling of background structure. The offset2 structure is fully contained in the enhanced background, which would explain the enhanced distribution of larger break scales.
Figure 15 shows a scatter diagram of the measured EPB parameters CsdB versus η_{1}. The correlation is identical to the correlation reported in Rino et al. (2016) from an analysis of 4 years of C/NOFS data. The overlaid loglinear dependence has the reported slope of 0.02 nepers per dB of C_{s} change. The tendency for the correlation to appear in narrow bands was also noted in the C/NOFS data. However, we now know from the analysis reported by Rino and Carrano (2018) that the correlation can be completely explained as a intrinsic property of powerlaw irregularity parameter estimation. The correlation occurs because the χ distribution with small degrees of freedom generates a significant population of errors larger than the mean. This is reflected in the C_{s} distributions shown in the upper frames of Figs. 12, 13, and 14, which favor larger C_{s} values.
As a consequence of the correlation, the true values of C_{s} and η_{1} are likely to be closer to the central values in Fig. 15. As a test of this effect, the equatorial MLEIPE was recomputed with M=10, which reduces the altitude resolution to 7 km. Figure 16 shows the probability distributions. The M=10 equatorial Cs distribution is more concentrated between 210 and 215 dB. The η_{1} and η_{2} peaks are sharpened somewhat, while larger scales dominate the break frequency scale. Because resolution requires larger segments, which reduces the number of segments that can be averaged, trades between resolution and statistical uncertainty are unavoidable. However, the variability of the break scale appears to convey information about the underlying structure. These are clearly topics for further study.
Conclusions
The analysis of highresolution EPB simulations presented in this paper supports that generally accepted hypothesis that developed EPB structure can be characterized by a onedimensional twocomponent powerlaw SDF with η_{1} somewhat smaller than 1.5 and η_{2} somewhat larger than 2.5. The scale associated with the break frequency varies from the 4 to 5 km bifurcation scale to much larger values reflecting intermingling of the EPB structure with the Fregion background. In units of electrons per cubic centimeter, the decibel turbulent strength parameter falls between 210 and 215 dB. A persistent correlation between the measured turbulent strength and the largescale spectral index is an intrinsic property of powerlaw parameter estimation that researchers need to be aware of. Structure evolving in the equatorial plane maps along field lines with no significant structure variation. However, structure mapped below Flayer was not investigated.
The transition from smoothly varying background structure to stochastic structure is manifest by an SDF transition initially reflecting largescale structure with η_{1}>η_{2} to developed structure with η_{1}<η_{2}. The transition through a single power law with η_{2}≃η_{2} is not a prominent feature, but we have argued that a smooth background is idealized and not representative of real ionospheric structure. Within the structured region, the structure is uniform over the 30 min interval with developed structure and over an altitude range of several hundred kilometers. The result favors the standard interpretation that towdimensional SDF characterizing the crossfield structure has the twodimensional form (1) with p_{n}=η_{n}+1.
We conclude by recalling that the 1971 PLUMEX campaign successfully launched a powerful rocket into an EPB being tracked by coherentscatter radar. The rocket carried a radio beacon and a Langmuir probe. Analysis of the PLUMEX radiobeacon and Langmuir probe data summarized by Rino et. al (1981) showed the first evidence of a twocomponent powerlaw structure. Numerical simulations being developed by the U. S. Naval Research Laboratory and reviewed by Yokoyama (2017) were being used to interpret the PLUMEX results. To quote the final sentence in the PLUMEX paper: Rapid progress is being made in such [numerical simulations], and we believe that the simulations have the potential to verify the results presented in [the PLUMEX] paper.
Abbreviations
 EPB:

Equatorial plasma bubble
 PSD:

Power spectral density
 SDF:

Spectral density function
References
Bhattacharyya, A, Groves KM, Basu S, Kuenzler H, Valladares CE, Sheehan R (2003) Lband scintillation activity and spacetime structure of lowlatitude UHF scintillations. Radio Sci 38(1):1004. https://doi.org/10.1029/2002RS002711.
Carrano, CS, Rino CL (2016) A theory of scintillation for twocomponent power law irregularity spectra: overview and numerical results. Radio Sci 51:789–813. https://doi.org/10.1002/2015RS005903.
Carrano, CS, Rino CL, Groves (2017) Maximum likelihood estimation of phase screen parameters from ionospheric scintillation spectra In: 15th International Ionospheric Effects Symposium, 1–11.. Radio Science Publications, Alexandria. May 911.
Fougere, PF (2009) On the accuracy of spectrum analysis of red noise processes using maximum entropy and periodogram methods simulation studies and application to geophysical data. J Geophys Res 90(A5):4355–4366.
Hysell, DL (2000) An overview and synthesis of plasma irregularities in equatorial spread f. J Atmos SolarTerr Phys 62:1037–1056.
Livingston, RC, Rino CL, McClure JP, Hanson WB (1981) Spectral characteristics of mediumscale equatorial Fregion irregularities. J Geophys Res 86:7421–7428.
Olsen, DM, Nelsen LS (1975) The NelderMead simplex procedure for function minimization. Technomectrics 17(1):46–51.
Retterer, JM (2010) Forecasting low latitude radio scintillation with 3D ionospheric plume models: 2. scintillation calculation. J Geophys Res 115. https://doi.org/10.1029/2008JA013840.
Rino, CL, Tsunoda RT, Petriceks, Livingston RC, Kelley MC, Baker DK (1981) Simultaneous rocketborne beacon and in situ measurements of equatorial spread F–intermediate wavelength results. J Geophys Res 84(A4):2411–2420.
Rino, CL, Groves KM, Carrano CS, Roddy PA (2016) A characterization of intermediatescale spread F structure from four years of highresolution C/NOFS satellite data. Radio Sci 51. https://doi.org/10.1002/2015RS005841.
Rino, C, Carrano C (2018) On the characterization of intermediate scale ionospheric structure. Radio Sci.Accepted for publication.
Rino, C, Carrano C, Groves K, Yokoyama T (2018) A configuration space model for intermediate scale ionospheric structure. Radio Science. https://doi.org/10.1029/2018RS006678.
Tsai, LC, Cheng KC, Liu CH (2011) GPS radio occultation measurements on ionospheric electron density from low earth orbit. J Geodesy 85:7421–7428.
Welch, P (1995) The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoustics 15(2):70–73.
Yokoyama, T. H, Shinagawa H, Jin H (2014) Nonlinear growth, bifurcation, and pinching of equatorial plasma bubble simulated by threedimensional highresolution bubble model. J Geophys Res Space 119:10474–10482. https://doi.org/10.1002/2014JA020708.
Yokoyama, T (2017) A review on the numerical simulation of equatorial plasma bubbles toward scintillation evaluation and forecasting. Prog Earth Planet Sci 4:37. https://doi.org/10.1186/s4064501701536.
Acknowledgements
The computation was performed on the FX100 supercomputer system at the Information Technology Center, Nagoya University, and Hitachi SR16000/M1 system at NICT, Japan.
Funding
This work was supported by JSPS KAKENHI Grant Number JP16K17814. This work was also supported by the computational joint research program of the Institute for SpaceEarth Environmental Research (ISEE), Nagoya University, Japan.
Support for CR and CC was provided under Advanced Data Driven Specification and Forecast Models for the IonosphereThermosphere System, Air Force Contract FA945312C0205
Availability of data and materials
The simulation data are stored on the FX100 supercomputer system at the Information Technology Center, Nagoya University, and Hitachi SR16000/M1 system at NICT, Japan. TY (tyoko@nict.go.jp) can provide data upon request.
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All of the simulations analyzed in this paper were performed by TY and generously reformatted and made available to CR who performed the analysis. The analysis was conceived by CR and TY at the December 2016 AGU meeting following a presentation by TY. CC has worked extensively to improve scintillation diagnostics, particularly definitive SDF parameter estimation, which was central to this study. All authors read and approved the final manuscript.
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Correspondence to Charles Rino.
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Rino, C., Yokoyama, T. & Carrano, C. Dynamic spectral characteristics of highresolution simulated equatorial plasma bubbles. Prog Earth Planet Sci 5, 83 (2018) doi:10.1186/s4064501802430
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Keywords
 Equatorial spread F
 Powerlaw ionospheric structure
 Convective plasma instability
 Structure characterization