Weather constraints on global drone flyability
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
Small aerial drones are used in a growing number of commercial applications. However, drones cannot fly in all weather, which impacts their reliability for time-sensitive operations. The magnitude and global variability of weather impact is poorly understood. We explore weather-limited drone flyability (the proportion of time drones can fly safely) by comparing historical wind speed, temperature, and precipitation data to manufacturer-reported thresholds of common commercial and weather-resistant drones with a computer simulation. We show that global flyability is highest in warm and dry continental regions and lowest over oceans and at high latitudes. Median global flyability for common drones is low: 5.7 h/day or 2.0 h/day if restricted to daylight hours. Weather-resistant drones have higher flyability (20.4 and 12.3 h/day, respectively). While these estimates do not consider all weather conditions, results suggest that improvements to weather resistance can increase flyability. An inverse analysis for major population centres shows the largest flyability gains for common drones can be achieved by increasing maximum wind speed and precipitation thresholds from 10 to 15 m/s and 0-1 mm/h, respectively.
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 | 0.000 | 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 it