New calibration procedures for airborne turbulence measurements and accuracy of the methane fluxes during the AirMeth campaigns
Bibliographic record
Abstract
Abstract. Low-level flights over tundra wetlands in Alaska and Canada have been conducted during the Airborne Measurements of Methane Emissions (AirMeth) campaigns to measure turbulent methane fluxes in the atmosphere. In this paper we describe the instrumentation and new calibration procedures for the essential pressure parameters required for turbulence sensing by aircraft that exploit suitable regular measurement flight legs without the need for dedicated calibration patterns. We estimate the accuracy of the mean wind and the turbulence measurements. We show that airborne measurements of turbulent fluxes of methane and carbon dioxide using cavity ring-down spectroscopy trace gas analysers together with established turbulence equipment achieve a relative accuracy similar to that of measurements of sensible heat flux if applied during low-level flights over natural area sources. The inertial subrange of the trace gas fluctuations cannot be resolved due to insufficient high-frequency precision of the analyser, but, since this scatter is uncorrelated with the vertical wind velocity, the covariance and thus the flux are reproduced correctly. In the covariance spectra the -7/3 drop-off in the inertial subrange can be reproduced if sufficient data are available for averaging. For convective conditions and flight legs of several tens of kilometres we estimate the flux detection limit to be about 4 mg m−2 d−1 for w′CH4′‾, 1.4 g m−2 d−1 for w′CO2′‾ and 4.2 W m−2 for the sensible heat flux.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".