Development and Qualification of Instrumented Unmanned Planes for Turbulence Observations in the Atmospheric Surface Layer
Bibliographic record
Abstract
The development of new observation systems like drones, present an opportunity to measure differently the turbulence in the atmospheric boundary layer. One of the main advantage of the unmanned plane lies in its capacity to fly at very low heights which is not possible with piloted airplanes, and thus to in situ investigate the turbulence in a way complementary to instrumented towers/masts. In the recent years, we have developed in Toulouse (France) two platforms of different size. The first one, called OVLI-TA, is a small unmanned aerial system (UAS) (3kg, payload included). It is instrumented with a 5-hole probe on the nose of the airplane, a Pitot probe, a fast inertial measurement unit (IMU), a GPS receiver, as well as temperature and moisture sensors in specific housings. After wind tunnel calibrations, the drone’s flight tests were conducted in Lannemezan (France), where there is an equipped 60m tower, which constitutes a reference to our measurements. The drone then participated to the international project DACCIWA (Dynamics-Aerosol-Chemistry-Clouds Interactions In West Africa), in Benin. Moreover, another project is carried out about the instrumentation of a so-called “Boreal” drone, which weights 25 kg and can embark 5 kg of sensors and IMU with data fusion. The scientific payload relates to atmospheric turbulence, GNSS reflectometry and gravimetry. In addition, this UAS has a long endurance (up to 10 h) and is more robust to fly in turbulent conditions. We will present the instrumental packages of the two UASs, the results of qualification flights as well as the first scientific results obtained in the DACCIWA campaign. We will also give some examples of envisaged deployment and observation strategy in future campaigns.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".