Time‐Dependent Response of the Terrestrial Exosphere to a Geomagnetic Storm

Cucho‐Padin, Gonzalo; Waldrop, L.

doi:10.1029/2019gl084327

Cited by 18 publications

(27 citation statements)

References 30 publications

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“…Based on the static tomographic results, 1‐hr acquisition will provide less LOSs coverage and a reduced high‐fidelity reconstructed area. Then, we limited our study region to r ∈ [3,12] R E , where the H density variations are higher (∼15 % ) (Cucho‐Padin & Waldrop, 2019) and the LOSs coverage is still high.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…The assumption of exospheric time‐invariance during storm‐time (i.e., geomagnetic activity) is likely invalid as evidence of variation in measured Ly‐ α emission has been reported by several authors (Bailey & Gruntman, 2013; Zoennchen et al, 2017). Furthermore, Cucho‐Padin and Waldrop (2019) presented for the first time‐dynamic tomographic reconstructions of H density distributions based on TWINS Ly‐ α data during the moderate storm that occurred on 15 June 2008, which ultimately revealed significant density variations (∼15 % ). In this research work, we make use of the publicly available H density distribution from Cucho‐Padin and Waldrop (2019) with valid region [3,12] R E to calculate ratios between H densities during storm‐time with respect to quiet‐time.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…Figures 2b–2d show the synthetic temporal evolution of the H density during the selected geomagnetic storm event for three different number of hours after the beginning of the reported analysis, that is, 00:00:00, 12 June 2008. Also, in accordance with the valid region in Cucho‐Padin and Waldrop (2019), we limit the analysis to [3,12] R E , resulting in N d = 9 × 36 = 324 polar pixels where the superindex d is associated with dynamic or time‐dependent reconstructions. We termed the ground‐truth sequence of hydrogen densities as

{boldx}_{G}^{d} {false|}_{k}

with k ∈ [1,160].…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…Composition in this region is dominated by atomic hydrogen, H, which forms a mostly gravitationally bound cloud that extends from several hundred to several tens of thousands of kilometers above Earths surface. These altitudes span the magnetically trapped plasma environment surrounding Earth, and infrequent though significant charge exchange reactions between exospheric H atoms and ambient ions impose large‐scale spatial asymmetries and localized gradients in exospheric structure as well as temporal variability on time scales ranging from hours to years (Cucho‐Padin & Waldrop, 2019; Fahr & Shizgal, 1983; Zoennchen et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…More recently, Cucho‐Padin and Waldrop (2018) presented a new technique for 3‐D exospheric H density estimation based on tomographic inversion of optically thin radiance data, using a 6‐hr interval of synthetic TWINS data near a single orbital apoapsis in order to demonstrate proof‐of‐concept technique fidelity. This concept of static exospheric tomography was further developed by the same authors to support a time‐dependent inversion framework, which was applied to a 6‐day interval of actual TWINS data to yield reconstructions at a temporal cadence (∼1 hr) sufficiently high for investigating the exospheres transient response to geomagnetic storms (Cucho‐Padin & Waldrop, 2019).…”

Section: Introductionmentioning

confidence: 99%

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New Sensing Strategies for Estimation of Global, Exospheric Density

Cucho‐Padin

Waldrop

2020

JGR Space Physics

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Reliable quantification of the global and time-dependent structure of the Earth's outermost atmosphere, a vast region known as the exosphere, has long been elusive, owing mainly to the sparse spatial and temporal sampling afforded by exospheric sensing platforms deployed to date. In this paper, we introduce new observing schemes that overcome these historical limitations and enable high-fidelity, high-cadence reconstruction of the exospheric density distribution on a global scale. Our approach leverages several state-of-the-art remote sensing capabilities, including (1) wide-field photometric imaging of exospheric emission, which is created through resonant photon scattering by the exosphere's constituent hydrogen atoms; (2) constellation sensor deployment near lunar orbit, which provides common-volume sensing; and (3) tomographic inversion of a discretized exospheric emission model that explicitly accounts for the Poisson-distributed shot noise inherent in the photometric data constraints. Using ensembles of synthetic emission data acquired at various realistic temporal cadences, we evaluate the fidelity of both static density reconstructions, in which the solution is formulated in the context of maximum a posteriori (MAP) estimation, as well as dynamic density reconstructions, which incorporate temporal evolution via Kalman filtering. The numerical results demonstrate that the new sensing strategies are capable of retrieving reliable estimates of exospheric density distribution at unprecedented spatial scales and temporal cadence, in support of numerous high-priority investigations of Earth's near-space environment.

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