Drone Measurements of Surface-Based Winter TemperatureInversions in the High Arctic at Eureka
Bibliographic record
Abstract
Abstract. The absence of sunlight during the winter in the High Arctic results in a strong surface-based atmospheric temperature inversion especially during clear skies and light surface wind conditions. The inversion suppresses turbulent heat transfer between the ground and the boundary layer. As a result the difference between the surface air temperature, measured at a height of 2 m, and the ground skin temperature can exceed several degrees Celsius. Such inversions occur very frequently in polar regions and are of interest to understand the mechanisms responsible for surface-atmosphere heat, mass and momentum exchanges and are critical for satellite validation studies. In this paper we present the results of operations of two commercial remotely piloted aircraft systems, or drones, at the Polar Environment Atmospheric Research Laboratory (PEARL), Eureka, Nunavut, Canada, at 80° N latitude. The drones are the Matrice 100 and M210-RTK quad-copters manufactured by DJI and were flown over Eureka during the February–March field campaigns in 2017 and 2020. They were equipped with a temperature measurement system built on a Raspberry Pi single-board computer, three platinum wire temperature sensors, GNSS receiver, and a pressure sensor. We demonstrate that the drones can be effectively used in the High Arctic to measure vertical temperature profiles up to 60 m of the ground and sea ice surface. Our results indicate that the inversion lapse rates within 0–10 m altitude range above the ground can reach the values of ~0.1–0.3 °C/m (~100–300 °C/km). The results are in a good agreement with the coincident temperatures measured at 2, 6 and 10 m levels at the National Oceanic and Atmospheric Administration flux tower at PEARL. Above 10 m a weaker inversion with an order of magnitude smaller lapse rates is recorded by the drone. The inversion strength agrees well with one obtained from the radiosonde temperature measurements. Above the sea ice, drone temperature profiles are found to have an isothermal layer above a surface based layer of instability which is attributed to the sensible heat flux through the sea ice. With the drones we were able to evaluate the influence of local topography on the surface-based inversion structure above the ground and to measure extremely cold temperatures of air that can pool in topographic depressions. The unique technical challenges of conducting drone campaigns in the winter High Arctic are highlighted in the paper.
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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".