In-flight measurements of lightning locations using an aircraft-mounted lightning mapper
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it