Four‐dimensional GPS imaging of space weather storms

Bust, G. S.; Crowley, G.; Garner, T. W.; Gaussiran, T.; Meggs, Robert W.; Mitchell, Cathryn N.; Spencer, P.S.; Yin, Ping; Zapfe, B.

doi:10.1029/2006sw000237

Cited by 56 publications

(70 citation statements)

References 34 publications

(37 reference statements)

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“…In fact, total electron content (TEC) maps created from GPS signal processing has been extensively used to quantify the timing and intensity of SEDs (e.g. Bust et al 2007;Wen et al 2007;Yizengaw et al 2007;Basu et al 2008;Coster and Skone 2009). SEDs have been shown to extend right through the dayside auroral zone into the polar cap, becoming tongues of ionization and, if breaking off, polar cap patches (e.g.…”

Section: Ring Current and Ionosphere And Thermospherementioning

confidence: 99%

Space Weather Effects Produced by the Ring Current Particles

2017

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One of the definitions of space weather describes it as the time-varying space environment that may be hazardous to technological systems in space and/or on the ground and/or endanger human health or life. The ring current has its contributions to space weather effects, both in terms of particles, ions and electrons, which constitute it, and magnetic and electric fields produced and modified by it at the ground and in space. We address the main aspects of the space weather effects from the ring current starting with brief review of ring current discovery and physical processes and the Dst-index and predictions of the ring current and storm occurrence based on it. Special attention is paid to the effects on satellites produced by the ring current electrons. The ring current is responsible for several processes in the other inner magnetosphere populations, such as the plasmasphere and radiation belts which is also described. Finally, we discuss the ring current influence on the ionosphere and the generation of geomagnetically induced currents (GIC).

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Section: Ring Current and Ionosphere And Thermospherementioning

confidence: 99%

Space Weather Effects Produced by the Ring Current Particles

2017

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“…In general, different methods may be used in different dimensions to construct the matrix because the mapping matrix can be very large [ Bust et al , 2007]. …”

Section: Multipass Inversion Algorithmmentioning

confidence: 99%

A multiresolution inversion for imaging the ionosphere

et al. 2017

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Ionospheric tomography has been widely employed in imaging the large‐scale ionospheric structures at both quiet and storm times. However, the tomographic algorithms to date have not been very effective in imaging of medium‐ and small‐scale ionospheric structures due to limitations of uneven ground‐based data distributions and the algorithm itself. Further, the effect of the density and quantity of Global Navigation Satellite Systems data that could help improve the tomographic results for the certain algorithm remains unclear in much of the literature. In this paper, a new multipass tomographic algorithm is proposed to conduct the inversion using intensive ground GPS observation data and is demonstrated over the U.S. West Coast during the period of 16–18 March 2015 which includes an ionospheric storm period. The characteristics of the multipass inversion algorithm are analyzed by comparing tomographic results with independent ionosonde data and Center for Orbit Determination in Europe total electron content estimates. Then, several ground data sets with different data distributions are grouped from the same data source in order to investigate the impact of the density of ground stations on ionospheric tomography results. Finally, it is concluded that the multipass inversion approach offers an improvement. The ground data density can affect tomographic results but only offers improvements up to a density of around one receiver every 150 to 200 km. When only GPS satellites are tracked there is no clear advantage in increasing the density of receivers beyond this level, although this may change if multiple constellations are monitored from each receiving station in the future.

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“…[11] Ionospheric Data Assimilation Four-Dimensional (IDA4D) [Bust et al, 2000] is a continuous-time, threedimensional imaging algorithm [Bust et al, 2004] routinely used to produce 4-D (time and space) ionospheric electron density specifications for various science investigations Bust et al, 2007;Yin et al, 2006;Crowley et al, 2006]. In particular, IDA4D has been used to investigate the ionospheric response to major geomagnetic storms [Bust and Mitchell, 2008;Bust and Crowley, 2008], including the formation and evolution of SED.…”

Section: Ionospheric Data Assimilation Four-dimensionalmentioning

confidence: 99%

First storm‐time plasma velocity estimates from high‐resolution ionospheric data assimilation

2013

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This paper uses data assimilation to estimate ionospheric state during storm time at subdegree resolution. We use Ionospheric Data Assimilation Four‐Dimensional (IDA4D) to resolve the three‐dimensional time‐varying electron density gradients of the storm‐enhanced density poleward plume. By Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE), we infer the three‐dimensional plasma velocity from the densities. EMPIRE estimates of ExB drift are made by correcting the Weimer 2000 electric potential model. This is the first time electron densities derived from GPS total electron content (TEC) data are being used to estimate field‐aligned and field‐perpendicular drifts at such high resolution, without reference to direct drift measurements. The time‐varying estimated electron densities are used to construct the ionospheric spatial decorrelation in vertical total electron content (TEC) on horizontal scales of less than 100 km. We compare slant TEC (STEC) estimates to actual STEC GPS observations, including independent unassimilated data. The IDA4D density model of the extreme ionospheric storm on 20 November 2003 shows STEC delays of up to 210 TEC units, comparable to the STEC of the GPS ground stations. Horizontal drifts from EMPIRE are predicted to be northwestward within the storm‐enhanced density plume and its boundary, turning northeast at high latitudes. These estimates compare favorably to independent Assimilative Mapping of Ionospheric Electrodynamics‐assimilated high‐latitude ExB drift estimates. Estimated and measured Defense Meteorological Satellite Program in situ drifts differ by a factor of 2–3 and in some cases have incorrect direction. This indicates that significant density rates of change and more accurate accounting for production and loss may be needed when other processes are not dominant.

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Four‐dimensional GPS imaging of space weather storms

Cited by 56 publications

References 34 publications

Space Weather Effects Produced by the Ring Current Particles

Space Weather Effects Produced by the Ring Current Particles

A multiresolution inversion for imaging the ionosphere

First storm‐time plasma velocity estimates from high‐resolution ionospheric data assimilation

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