Measurements and modelling of turbulent fluxes under different flow regimes at a glacier surface
Bibliographic record
Abstract
<!--!introduction!--> Parameterization of turbulent heat fluxes with bulk aerodynamic methods has been shown to perform relatively poorly in the presence of shallow katabatic winds. Identifying when and how the bulk methods under-perform is essential to reduce uncertainty in surface energy balance modelling of glacier melt. Here, we evaluate the most commonly used bulk method in simulating 30-min sensible heat fluxes over two summer months at the Kaskawulsh glacier in the Yukon, Canada. As our reference data, we use eddy-covariance (EC) measurements from one on-glacier site at three different heights (1 m, 2 m, and 3 m). To adequately process the EC-data, we propose two new methods: a statistical method that ensures the fluxes are derived from time windows with (near-)stationary turbulence, and a filtering method that ensures the fluxes are representative of surface conditions, in particular during shallow katabatic winds. We find that the agreement between EC-derived fluxes and those modelled with the bulk method substantially improves with the implementation of the two data-processing methods. The least data-processing is required for the measurements at 1 m above the surface, the height that also yields the best performance of the bulk method. Contrary to the previous findings, the bulk method does perform well during shallow katabatic flows if the measurements of temperature and wind speed are taken close to the surface (<= 1 m) and well below the wind speed maxima. We conclude that adequate data processing and filtering are critical in obtaining accurate sensible heat fluxes at glacier surfaces.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".