An assessment of corrections for eddy covariance measured turbulent fluxes over snow in mountain environments
Bibliographic record
Abstract
Snow‐covered complex terrain is an extremely important runoff‐generating landscape in high‐altitude and high‐latitude environments, yet it is often considered nonviable for eddy covariance measurements of turbulent fluxes. Turbulent flux data are useful for evaluating the coupled snow cover mass and energy balance that control snow ablation and melt. In particular, detailed, multiseason analyses of eddy covariance data postprocessing requirements and resulting data quality for hydrological analyses in open and sheltered mountain sites have not been conducted. These analyses are needed since these landscapes typify those that generate snowmelt runoff in the mountain west of North America. Eddy covariance measurements taken from exposed hilltop and sheltered subcanopy snow research sites during three snow seasons underwent rigorous postprocessing and data quality assessments. Procedures included data filtering, air density corrections, sensor heating, axis rotation, and exclusion of nonstationary data. Data quality analysis showed that 77% of the sensible heat flux data and 95% of the latent heat flux data were of high quality. There was little interannual variability over three seasons in quality or improvements due to postprocessing results. A comparison of summary data based on a 30‐min averaging period to postprocessed high‐resolution flux data found that the postprocessed sensible heat fluxes were up to 14% less than the summary fluxes for the snow season. The results indicated that application of unattended eddy covariance techniques at these sites was viable, but the full suite of corrections and postprocessing are advisable to obtain flux observations of sufficient reliability for snow hydrology calculations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".