Review of an Airborne Lightning Detection System and Atmospheric Conditions During Flights in Coastal Thunderstorm Conditions
Bibliographic record
Abstract
Lightning poses a significant risk to aircraft safety, especially as the aviation industry transitions from conventional to hybrid and electric aircraft. It is becoming more common to rely on remotely piloted aircraft systems (RPAS), unmanned aerial vehicles (UAVs), and vertical take-off and landing aircraft (VTOLs) for all-weather aerial activities like transportation and the delivery of goods. Important flight operations decisions of postponing or diverting flights due to severe weather are reliant on accurate information about the presence of lightning and its type, location, flash rate, and information about the ambient conditions inducive of lightning. At present, numerous well-established ground and satellite-based methods exist for monitoring lightning activity. At best, aircraft can receive weather updates from ground sources every 2.5 to 5 minutes, but it is not uncommon for updates to be intermittent due to connection and service stability issues. Therefore, an aircraft-mounted lightning locator may be the most practical source of real-time lightning information for pilots. Detailed performance metrics with uncertainties for commercial airborne lightning locating systems are typically not published and the literature investigating such systems is limited. Here, we present airborne lightning measurements obtained using the commercially available Stormscope Weather Mapping System (WX-500 Series 2). This single-station direction-finding sensor was installed on the Convair-580 research aircraft owned and operated by the National Research Council of Canada (NRC) during the 2022 Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE) campaign in Houston, Texas, which targeted convective updrafts (up to 30 m/s). Stormscope performance is assessed through comparisons to high-quality datasets of total lightning activity provided by the Houston Lightning Mapping Array (HLMA; 60 to 66 MHz) and the GOES - Geostationary Lightning Mapper (GLM; 777 nm). Preliminary results show the Stormscope registered lightning activity in less than 60% of detection windows containing at least one HLMA flash. When considering only single and clustered flashes, Stormscope bearing accuracy was ±12° while the range was often overpredicted and with a large spread. Also presented are in-flight microphysics data including high-resolution images of single particles within in a lightning producing cell.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".