Bibliographic record
Abstract
Modelling the energy exchange between the snowpack and the atmosphere is critical for many hydrological applications.Of the terms present in the snow energy balance, the turbulent fluxes of sensible and latent heat are the most challenging to estimate, particularly within mountain environments where the hydrological importance is great.Many of the flux estimation techniques, such as the bulk transfer method, are poorly adapted for use in complex terrain.In order to characterize the turbulence and to assess the suitability of flux estimation techniques, eddy covariance flux measurements and supporting meteorological data were collected from two mountain valley forest openings in Kananaskis Country, AB.These sites were generally calm, however wind gusts were frequently observed which markedly affected the turbulence characteristics and increased the rates of momentum and heat transfer.In order to successfully apply the bulk transfer technique at these sites, it was necessary to use environment-specific transfer coefficients to account for the effect of the surrounding complex terrain.These observations were compared with data collected on a treeless alpine ridge near Whitehorse, YT, where it was found that many of the turbulence characteristics were similar to flat sites.However, the boundary layer formed over the alpine ridge was very thin and the site was poorly suited for estimating surface fluxes.The mountain results were further contrasted with data collected over a homogeneous and flat prairie site located near Saskatoon, SK.This site included measurement of all of the snow energy terms, permitting an estimate of the energy balance closure obtainable over snow surfaces.The observed energy balance residual was very large, indicating that the eddy covariance technique was unable to capture all of the turbulent energy.It was concluded that an unmeasured transfer of sensible heat was occurring which was strongly correlated with the long-wave radiation balance.Mechanisms for this relationship were hypothesized.Two snow energy balance models were used to investigate the energy imbalance, where it was observed that the flux terms could be suitably simulated if effective parameters were used to augment the sensible heat transfer rate.The results from this thesis contribute to the understanding of heat transfer processes over snow surfaces during mid-winter conditions and improve the ability to model turbulent heat and mass fluxes from snow surfaces in complex environments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".