In-flight measurements of lightning locations using an aircraft-mounted lightning mapper
Bibliographic record
Abstract
Real-time measurements of lightning locations can improve flight safety by providing aircraft operators with valuable information about nearby weather conditions. Lightning warnings can be especially valuable when piloting aircraft that are more susceptible to a direct strike such as electric aircraft, hydrogen-powered aircraft, and even UAVs with composite skins. At best, weather updates are broadcast from weather services every 2.5 to 5 mins, but it's not uncommon for an intermittent connection to cause service stability issues. Therefore, an aircraft-mounted lightning mapper might be the most practical source of real-time lightning information for pilots. This work investigates the in-flight performance of the aircraft-mounted Stormscope Weather Mapping System (WX-500 Series 2) through comparisons to the Houston Lightning Mapping Array, National Lightning Detection Network, and the GOES - Geostationary Lightning Mapper. Measurements from two thunderstorms near Houston, TX, yielded WX-500 detection efficiencies of 33 % and 42 % for intracloud flashes, 75 % and 64 % for cloud to ground flashes, and 53 % and 79 % for total flashes. The WX-500 bearing measurement was accurate to within ±14° (σ), which improved to ±4° when integration time was increased from 2 to 30 s and clear outliers were ignored. The WX-500 range measurement was overestimated by an average of +74 km (±50 km) when the average true flash distance was 94 km. The WX-500 accurately depicted the boundary of lightning activity at an integration time of 1 min which is sufficient for the circumnavigation of thunderstorms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".