MétaCan
Menu
Back to cohort
Record W4392648003 · doi:10.5194/egusphere-egu24-17954

Ionospheric Scintillation Nowcasting and Forecasting for Civil Aviation

2024· preprint· en· W4392648003 on OpenAlexaboutno aff
Philippe Yaya, Roïya Souissi, Marie Cherrier, Ali Naouri

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAir Traffic Management and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsNowcastingScintillationIonosphereAeronauticsCivil aviationInterplanetary scintillationAviationMeteorologyEnvironmental scienceGeographyComputer scienceGeologyEngineeringAerospace engineeringTelecommunicationsGeophysicsPhysics

Abstract

fetched live from OpenAlex

The International Civil Aviation Organization (ICAO) has set up a space weather service for monitoring and raising alerts in case of moderate or severe potential impact on aviation, and covering three domains: GNSS, Radiation and HF Communications. This service is operational since November 2019 and is working in a bi-weekly rotation of four global centers: SWPC (US Space Weather Prediction Center), PECASUS (consortium of 9 European States), CRC (China-Russia Consortium) and ACFJ (Australia-Canada-France-Japan). CLS (Collecte Localisation Satellites), a member of ACFJ, is a subsidiary of the French Space Agency and responsible for delivering near real-time ionospheric scintillation maps. The input data is based on a worldwide network of GNSS receivers, composed of various regional and global networks. The work presented here summarizes the adopted algorithms and pre-processing tasks leading to generate the nowcast maps. The results of a validation work are shown (comparison of indices from geodetic receivers and scintillation monitors) as well as a focus on severe events and their effect on aviation. Finally, taking advantage of a 4-years long data base, the status of a forecasting scintillation model is presented, taking care of separating the EPBs (Equatorial Plasma Bubbles) and geomagnetic storms origins.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.210
Teacher spread0.189 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicAir Traffic Management and OptimizationFrench-language works237,207