Improved processing methods for eddy covariance measurements in calculating sensible heat fluxes at glacier surfaces
Bibliographic record
Abstract
Abstract Bulk aerodynamic methods have been shown to perform poorly in computing turbulent heat fluxes at glacier surfaces during shallow katabatic winds. Katabatic surface layers have different wind shear and flux profiles to the surface layers for which the bulk methods were developed, potentially invalidating their use in these conditions. In addition, eddy covariance-derived turbulent heat fluxes are unlikely to be representative of surface conditions when eddy covariance data are collected close to the wind speed maximum (WSM). Here we utilize two months of eddy covariance and meteorological data measured at three different heights (1 m, 2 m, and 3 m) at Kaskawulsh Glacier in the Yukon, Canada, to re-examine the performance of bulk methods relative to eddy covariance-derived fluxes under different near-surface flow regimes. We propose a new set of processing methods for one-level eddy covariance data to ensure the validity of calculated fluxes during highly variable flows and low-level wind speed maxima, which leads to improved agreement between eddy covariance-derived and modelled fluxes across all flow regimes, with the best agreement (correlation >0.9) 1 m above the surface. Contrary to previous studies, these results show that adequately processed eddy covariance data collected at or above the WSM can provide valid estimates of surface heat fluxes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".