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Record W4220801235 · doi:10.5194/egusphere-egu22-4262

Multi-instrument observations of polar cap patches and traveling ionospheric disturbances during geomagnetic storms

2022· preprint· en· W4220801235 on OpenAlexaffabout
Paul Prikryl, R. G. Gillies, Shibaji Chakraborty, David R. Themens, E. G. Thomas, J. M. Weygand

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of CalgaryUniversity of New Brunswick
Fundersnot available
KeywordsIonosphereThermosphereGeophysicsElectrojetSubstormTECIncoherent scatterEarth's magnetic fieldAtmospheric sciencesGeomagnetic stormF regionPhysicsSolar windGeologyGeodesyMagnetospherePlasmaMagnetic field

Abstract

fetched live from OpenAlex

Solar wind Alfvén waves [1] coupling to the magnetosphere-ionosphere-thermosphere (MIT) have been associated with high-intensity long-duration continuous auroral electrojet activity [2] and shown to modulate ionospheric convection in the cusp generating polar cap patches and atmospheric gravity waves [3,4]. The Resolute Bay Incoherent Scatter Radars (RISR-C and RISR-N) [5] are well suited for observing the ionospheric signatures of flux transfer events and subsequent polar patch formation in the cusp. During minor to moderate geomagnetic storms caused by corotating interaction regions at the leading edge of solar wind high speed streams polar patches were observed as they convected over the RISR, and the Canadian High-Arctic Ionospheric Network (CHAIN) ionosondes and GPS receivers [6]. The patches were generated by the MIT coupling of Alfvén waves in the upstream solar wind. The coupling process modulated the ionospheric convection and the intensity of ionospheric currents, including auroral electrojets. The horizontal equivalent ionospheric currents and vertical current amplitudes are estimated from the ground-based magnetometer data using an inversion technique [7]. Pulses of ionospheric currents that are a source of Joule heating in the lower thermosphere launched atmospheric gravity waves causing traveling ionospheric disturbances (TIDs) propagating equatorward. TIDs were observed in the SuperDARN HF radar ground scatter [8], in the detrended GPS TEC maps, and in one case, in the altitude profiles of ionospheric electron densities observed by the Poker Flat ISR [9]. [1] Belcher, JW, Davis, L, Jr. 1971. J. Geophys. Res. 76, 3534–3563. [2] Tsurutani, BT, Gonzalez, WD. 1987. Planet. Space Sci. 35(4), 405–412. [3] Prikryl, P, et al., 1999. Ann. Geophys. 17, 463–489. [4] Prikryl, P, et al., 2005. Ann. Geophys. 23, 401–417. [5] Gillies RG, et al., 2016. Radio Sci., 51(10):1645-1659. [6] Jayachandran, PT, et al., 2009. Radio Sci., 44, RS0A03. [7] Weygand, JM, et al., 2011. J. Geophys. Res. 116, A03305. [8] Chisham, G., et al., 2007. Surv. Geophys. 28, 33–109. [9] Heinselman, CJ, Nicolls, MJ, 2008. Radio Sci., 43, RS5013.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.222
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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