FTA: A Feature Tracking Empirical Model of Auroral Precipitation

Wu, Chen; Ridley, A. J.; DeJong, A. D.; Paxton, L. J.

doi:10.1029/2020sw002629

Cited by 7 publications

(23 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, improvements regarding the implicit solver will be thoroughly studied in order to admit larger time steps, even in presence of chemical reactions. Ionospheric electrodynamic model Weimer 58 Heelis 59 Auroral precipitation model FTA 60 Default Magnetic field/activity B = (0, 0, −5) nT, v x = 400 km/s (default) K p = 1…”

Section: Discussionmentioning

confidence: 99%

A high‐order discontinuous Galerkin approach for physics‐based thermospheric modeling

Vila‐Pérez,

Nguyen,

Peraire

2023

Numerical Meth Engineering

View full text Add to dashboard Cite

The accurate prediction of aerodynamic drag on satellites orbiting in the upper atmosphere is critical to the operational success of modern space technologies, such as satellite‐based communication or navigation systems, which have become increasingly popular in the last few years due to the deployment of constellations of satellites in low‐Earth orbit. As a result, physics‐based models of the ionosphere and thermosphere have emerged as a necessary tool for the prediction of atmospheric outputs under highly variable space weather conditions. This paper proposes a high‐fidelity approach for physics‐based space weather modeling based on the solution of the Navier–Stokes equations using a high‐order discontinuous Galerkin method, combined with a matrix‐free strategy suitable for high‐performance computing on GPU architectures. The approach consists of a thermospheric model that describes a chemically frozen neutral atmosphere in nonhydrostatic equilibrium driven by the external excitation of the Sun. A novel set of variables is considered to treat the low densities present in the upper atmosphere and to accommodate the wide range of scales present in the problem. At the same time, and unlike most existing approaches, radial and angular directions are treated in a nonsegregated approach. The study presents a set of numerical examples that demonstrate the accuracy of the approximation and validate the current approach against observational data along a satellite orbit, including estimates of established empirical and physics‐based models of the ionosphere‐thermosphere system. Finally, a one‐dimensional radial derivation of the physics‐based model is presented and utilized for conducting a parametric study of the main thermal quantities under various solar conditions.

show abstract

Section: Discussionmentioning

confidence: 99%

A high‐order discontinuous Galerkin approach for physics‐based thermospheric modeling

Vila‐Pérez,

Nguyen,

Peraire

2023

Numerical Meth Engineering

View full text Add to dashboard Cite

show abstract

“…In this study, the processed LBHl and LBHs emission images (Wu et al, 2023) from Polar UVI from Jan 1997 to Jun 1998 are utilized. As described by Wu et al (2021), individual images have been processed to identify the auroral oval and track a cumulative energy grid in 96 magnetic local time (MLT) sectors. Summarizing the processing.…”

Section: Methodsmentioning

confidence: 99%

Auroral Characteristics Related to AU&AL Indices

Wu,

Ridley

2024

JGR Space Physics

Self Cite

View full text Add to dashboard Cite

Auroral images of the N2 Lyman‐Birge‐Hopfield emissions from Polar Ultraviolet Imager (UVI) for 1.5 years are used to investigate the auroral characteristics related to the AU and AL indices that represent the directly driven and unloading processes in the solar wind‐magnetosphere‐ionosphere coupling, respectively. Findings include: (a) Growing AL mainly relates to the nightside aurora and tends to keep the general auroral morphology. (b) As AU increases, the aurora brightens and expands more globally, especially in the midnight to postmidnight sector. (c) The regional auroral power (AP), equatorward boundary, poleward boundary, and peak intensity change quasi‐linearly with intensifying AU and AL. (d) The rates of change depend on the MLT and AU/AL levels. The same dataset has been used to construct the empirical Feature Tracking of Auroral Precipitation (FTA) model which specifies the global energy flux and mean energy determined by the AE index (FTA‐AE). As an extension of FTA‐AE, the relationships of the auroral emission with the AU&AL indices were derived to construct the FTA‐AU&AL model which specifies a more consistent aurora during different magnetospheric driving modes compared to FTA‐AE. Comparisons of AP from the Defense Meteorological Satellite Program Special Sensor Ultraviolet Spectrographic Imagers (SSUSI) measurements and empirical auroral models show that the FTA‐AE and ‐AU&AL models predicted larger AP than the Fuller‐Rowell and Evans (1987) model and the OVATION‐prime model did. As the activity level increased, all four models tended to underestimate the AP but the FTA APs increased relatively faster and were therefore more consistent with the data.

show abstract

“…The observations are hourly averaged and span the past ∼30 years (1978–2013 for electrons; 1983–2013 for ions). The study uses the empirical models, OVATION Prime (Newell et al., 2009; shortened to OV Prime) and the AE‐driven Feature Tracking of Aurora (FTA; Wu et al., 2021) for comparison of energy flux and average energies in the auroral region. OV Prime is developed from multiple observations from DMSP during the years 1988–1998, while FTA is based on 1.5 years of Polar Ultraviolet Imager data.…”

Section: Numerical Methodologymentioning

confidence: 99%

Global Driving of Auroral Precipitation: 1. Balance of Sources

et al. 2022

Self Cite

View full text Add to dashboard Cite

High-latitude precipitation of charged particles is a crucial driver of ionospheric electrodynamics (e.g., Kivelson & Russell, 1995). These particles precipitate from the near-Earth plasma environment to form the aurora, and enhance the electrical conductance in the polar regions (e.g., Schunk & Nagy, 2009). Auroral precipitation is broadly defined into two types: diffuse and discrete aurora. Particles scattered into the loss cone by plasma waves create the diffuse aurora (Nishimura et al., 2020a and references therein). Diffuse particles precipitate into the upper atmosphere without the need of acceleration, and can consist of both electrons (e.g., Evans & Moore, 1979) and ions (e.g., Sergeev et al., 1983). Conversely, the discrete aurora is generated by particles that are accelerated into the ionosphere (e.g., Korth et al., 2014). These particles can be accelerated by geomagnetic field-aligned electric fields (monoenergetic; e.g., Evans, 1974;Knight, 1973) or by dispersive Alfvén waves (broadband; e.g., Chaston et al., 2003;Ergun et al., 1998). The conductance enhancements caused by auroral precipitation are important to investigative studies of magnetosphere-ionosphere coupling (e.g., Öztürk et al., 2020), since it regulates the closure of field-aligned currents (FACs;Iijima & Potemra, 1976) and maintain the nonlinear feedback

show abstract

FTA: A Feature Tracking Empirical Model of Auroral Precipitation

Cited by 7 publications

References 65 publications

A high‐order discontinuous Galerkin approach for physics‐based thermospheric modeling

A high‐order discontinuous Galerkin approach for physics‐based thermospheric modeling

Auroral Characteristics Related to AU&AL Indices

Global Driving of Auroral Precipitation: 1. Balance of Sources

Contact Info

Product

Resources

About