Ionospheric Scintillation Nowcasting and Forecasting for Civil Aviation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".