Multiscale Dynamics in the High‐Latitude Ionosphere

Nishimura, Y.; Deng, Yue; Lyons, L. R.; McGranaghan, Ryan; Zettergren, M. D.

doi:10.1002/9781119815617.ch3

Cited by 22 publications

(25 citation statements)

References 160 publications

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“…We rely upon and slightly update the methodologies developed by Strickland et al [22] and Hecht et al [23][24][25], which are described in Determining Energy Flux and Average Energy From Aurora Color Ratios. These methodologies, which utilize a model atmosphere to incorporate the appropriate physics and chemistry, improve calculations of the emissions and conductance as functions of the incident energy flux and average energy as compared to Rees and Luckey [21], which Nishimura et al [20] used. We note that our techniques additionally include more physics and chemistry than the techniques employed by Kosch et al (1998) and Lam et al [17], who did not calculate emissions, energy flux, or average energy, but deduced the Pedersen conductance assuming that it is proportional to the square root of the white light intensity.…”

Section: Methodology Background and Overviewmentioning

confidence: 99%

“…This allows us to expand upon previous demonstrations of ASI capabilities to estimate precipitated energy flux and average energy in 2D, but on a continental scale. Our recent work has shown that the THEMIS ASI array can observe evolution of precipitating energy flux over the whole size of a substorm and that it is possible to estimate contributions of mesoscale precipitation over large-scale precipitation [20].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we describe the technique that utilizes the whitelight ASI array that was not detailed in Nishimura et al [20], including important upgrades made through the present work, supplemented by MSPs and auroral transport code calculations, to estimate precipitating energy flux, average energy, and Hall conductance, as well as what percent of the energy flux is contributed by mesoscale auroral forms during two back-toback substorms.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Precipitating Energy Flux, Average Energy, and Hall Auroral Conductance From THEMIS All-Sky-Imagers With Focus on Mesoscales

et al. 2021

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Recent attention has been given to mesoscale phenomena across geospace (∼10 s km to 500 km in the ionosphere or ∼0.5 RE to several RE in the magnetosphere), as their contributions to the system global response are important yet remain uncharacterized mostly due to limitations in data resolution and coverage as well as in computational power. As data and models improve, it becomes increasingly valuable to advance understanding of the role of mesoscale phenomena contributions—specifically, in magnetosphere-ionosphere coupling. This paper describes a new method that utilizes the 2D array of Time History of Events and Macroscale Interactions during Substorms (THEMIS) white-light all-sky-imagers (ASI), in conjunction with meridian scanning photometers, to estimate the auroral scale sizes of intense precipitating energy fluxes and the associated Hall conductances. As an example of the technique, we investigated the role of precipitated energy flux and average energy on mesoscales as contrasted to large-scales for two back-to-back substorms, finding that mesoscale aurora contributes up to ∼80% (∼60%) of the total energy flux immediately after onset during the early expansion phase of the first (second) substorm, and continues to contribute ∼30–55% throughout the remainder of the substorm. The average energy estimated from the ASI mosaic field of view also peaked during the initial expansion phase. Using the measured energy flux and tables produced from the Boltzmann Three Constituent (B3C) auroral transport code (Strickland et al., 1976; 1993), we also estimated the 2D Hall conductance and compared it to Poker Flat Incoherent Scatter Radar conductance values, finding good agreement for both discrete and diffuse aurora.

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Section: Methodology Background and Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating Precipitating Energy Flux, Average Energy, and Hall Auroral Conductance From THEMIS All-Sky-Imagers With Focus on Mesoscales

et al. 2021

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“…• Multiresolution Gaussian process model (Lattice Kriging) is used to assimilate Special Sensor Ultraviolet Spectrographic Imagers and Time History of Events and Macroscale Interactions during Substorms for auroral energy flux and mean energy • Auroral assimilation combines various datasets coherently, alleviates boundary issue, and captures real-time mesoscale structure as observed • Thermosphere Ionosphere Electrodynamics General Circulation Model driven by the assimilated auroral maps reproduces strong and structured total electron contents, comparing more favorably with the global navigation satellite system observations field-aligned currents (FACs) in response to the different phases of day and night-side reconnection (Nishimura et al, 2021). These ionospheric processes further affect the neutral atmosphere through the exchange and transport of momentum, energy, and composition in the coupled magnetosphere-ionosphere-thermosphere (MIT) system (Thayer & Semeter, 2004).…”

mentioning

confidence: 94%

Multiresolution Data Assimilation for Auroral Energy Flux and Mean Energy Using DMSP SSUSI, THEMIS ASI, and An Empirical Model

Tan

Zhang

et al. 2022

Space Weather

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We apply a multiresolution Gaussian process model (Lattice Kriging) to combine satellite observations, ground‐based observations, and an empirical auroral model, to produce the assimilation of auroral energy flux and mean energy over high‐latitude regions. Compared to a simple padding, the assimilation coherently combines various data inputs leading to continuous transitions between different datasets. The multiresolution modeling capability is achieved by allocating multiple layers of basis functions with different resolutions. Higher‐resolution fitting results capture more mesoscale (10–100 s km) structures such as auroral arcs, than the low‐resolution ones and the empirical model. To better reconcile different datasets, two preprocessing steps, temporal interpolation of satellite data and spatial down‐sampling of low‐fidelity data, are implemented. The inherent smoothing effect of the fitting, which causes an unrealistic spreading of the aurora, is mitigated by a post processing step: the K Nearest Neighbor (KNN) algorithm. KNN identifies the probability of a region with significant aurora and thereby eliminates those regions with low values. Thereby, this methodology can be used to maintain realistic and mesoscale auroral structures without boundary issues. We then run the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) driven by the high‐ and low‐resolution auroral assimilations and compare total electron contents (TECs). TIEGCM driven by data assimilation produces enhanced TECs by a factor of ∼2 than the one driven by the empirical aurora, and high‐resolution results show mesoscale structures. Our study shows the value of incorporating realistic auroral inputs via assimilation to drive ionosphere‐thermosphere models for better understanding the consequences of mesoscale phenomena.

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“…Ion motion can enhance, recede, and even reverse in reacting to different interplanetary magnetic field (IMF) and solar wind conditions. During disturbed periods, enhanced ion convection transports midlatitude plasma into the polar cap, leading to tongues of ionization and patches, which can disrupt communication and navigation in the polar region (Buchau et al., 1983; Nishimura et al., 2021; Weber et al., 1984). The radio backscatter technique has been widely used to measure ion motions.…”

Section: Introductionmentioning

confidence: 99%

Data Assimilation of High‐Latitude Electric Fields: Extension of a Multi‐Resolution Gaussian Process Model (Lattice Kriging) to Vector Fields

2022

Space Weather

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Ionospheric plasma convection is largely driven by the electrodynamic processes in the magnetosphere, which is controlled by the interaction between magnetosphere and solar wind. Thus, ion convection is a key indicator of the ionospheric responses to geomagnetic variations. Ion motion can enhance, recede, and even reverse in reacting to different interplanetary magnetic field (IMF) and solar wind conditions. During disturbed periods, enhanced ion convection transports midlatitude plasma into the polar cap, leading to tongues of ionization and patches, which can disrupt communication and navigation in the polar region (Buchau et al., 1983;Nishimura et al., 2021;Weber et al., 1984). The radio backscatter technique has been widely used to measure ion motions. For instance, the Super Dual Auroral Radar Network (SuperDARN) scans over azimuth sectors on a regular basis

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Multiscale Dynamics in the High‐Latitude Ionosphere

Cited by 22 publications

References 160 publications

Estimating Precipitating Energy Flux, Average Energy, and Hall Auroral Conductance From THEMIS All-Sky-Imagers With Focus on Mesoscales

Estimating Precipitating Energy Flux, Average Energy, and Hall Auroral Conductance From THEMIS All-Sky-Imagers With Focus on Mesoscales

Multiresolution Data Assimilation for Auroral Energy Flux and Mean Energy Using DMSP SSUSI, THEMIS ASI, and An Empirical Model

Data Assimilation of High‐Latitude Electric Fields: Extension of a Multi‐Resolution Gaussian Process Model (Lattice Kriging) to Vector Fields

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