On the relevance of mesoscale transport for in-situ energy balance measurements
Bibliographic record
Abstract
Mesoscale transport of energy and matter between the \nsurface and the atmosphere often occurs in the form of \nnon-propagating turbulent organised structures or thermally- \ninduced circulations. Spatially resolving measurements \nare required to capture such fluxes and, thus far, airborne \nmeasurements are the only means to accomplish this. In \ncontrast, tower-based eddy-covariance measurements are \nconducted at one point and therefore inherently cannot \ncapture the total atmospheric exchange, which is recognised \nas a major contributor to the energy balance closure \nproblem. As long as there are mean vertical thermal and \nhumidity gradients in the atmospheric boundary layer, with \na higher potential temperature and specific humidity in the \nsurface layer than in the outer layer, such organised structures \nwill lead to a systematic underestimation of turbulent \nenergy fluxes from eddy-towers. Firstly, we address the \nquestion of how deep such meso-γ scale motions penetrate \ninto the surface layer. We present indications from Doppler- \nLiDAR, airborne and tower-based measurements, which \nshow that mesoscale motions can indeed be found quite \nclose to the surface, but the mesoscale effect vanishes \nwhen measurements are actually conducted within the \nroughness sublayer and when shear stress is sufficiently \nlarge to break up mesoscale contributions into smaller \neddies. This is illustrated by observations from Germany \nand Israel. Secondly, we investigate whether the common \npractice of adjusting the measured eddy tower fluxes for \nenergy balance closure by conserving the Bowen ratio is \nsupported by experimental evidence. Mesoscale and smallscale \nturbulent fluxes from four different flight campaigns \nare presented, which were carried out on board of the \nCanadian Twin Otter (National Research Council of Canada) \nand the German Polar 5 (Alfred-Wegener Institute) research \naircraft over different landscapes in Canada and Alaska.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".