Effects of solar cycle 24 activity on WAAS navigation
Bibliographic record
Abstract
This paper reviews the effects of geomagnetic activity of solar cycle 24 from 2011 through mid‐2013 on the Federal Aviation Administration's Wide Area Augmentation System (WAAS) navigation service in the U.S., to identify (a) major impacts and their severity compared with the previous cycle and (b) effects in new service regions of North America added since last solar cycle. We examine two cases: a storm that reduced service coverage for several hours and ionospheric scintillation that led to anomalous receiver tracking. Using the 24–25 October 2011 storm as an example, we examine WAAS operational system coverage for the conterminous U.S. (CONUS). The WAAS algorithm upgrade to ionospheric estimation, in effect since late 2011, is able to mitigate the daytime coverage loss but not the nighttime loss. We correlate WAAS availability to maps of the storm plasma generated with the data assimilative model Ionospheric Data Assimilation 4‐D, which show a local nighttime corotating persistent plume of plasma extending from Florida across central CONUS. We study the effect of scintillation on 9 October 2012 on the WAAS reference station at Fairbanks, Alaska. Data from a nearby scintillation monitor in Gakona and all‐sky imaging of aurora at Poker Flat corroborate the event. Anomalous receiver processing triggered by scintillation reduces accuracy at Fairbanks for a few minutes. Users experiencing similar effects would have their confidence bounds inflated, possibly trading off service continuity for safety. The activity to date in solar cycle 24 has had minor effects on WAAS service coverage, mainly occurring in Alaska and Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".