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Record W2101463224 · doi:10.1002/hyp.6122

Boundary‐layer growth and advection of heat over snow and soil patches: modelling and parameterization

2006· article· en· W2101463224 on OpenAlexaffabout
Richard Essery, R. J. Granger, John W. Pomeroy

Bibliographic record

VenueHydrological Processes · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of SaskatchewanEnvironment and Climate Change Canada
FundersNatural Environment Research Council
KeywordsSnowSnowmeltAdvectionEnvironmental scienceSensible heatAtmospheric sciencesFlux (metallurgy)Latent heatBoundary layerGeologyMeteorologyMechanicsGeomorphologyPhysicsMaterials scienceThermodynamics

Abstract

fetched live from OpenAlex

Abstract Melting snow is generally patchy; upward sensible heat fluxes from patches of snow‐free ground warm the air and contribute energy for snowmelt. A simple model is presented for advection of heat over partial snow covers and compared with measurements of temperature profiles over snow and snow‐free ground. Approximations for flux and temperature profiles in the internal boundary layers over snow patches are used to develop parameterizations for local and average surface fluxes into the snow. In comparison with results from the advection model for regular patterns of alternating snow patches and snow‐free ground, a tile model is found to give a good parameterization for average heat fluxes over the whole surface, but it does not match the local fluxes into snow and snow‐free ground separately. An extended tile model that gives better results is developed from the flux profile parameterization. For complex snow‐cover patterns with a fractal distribution of patch sizes, average fluxes are found to be close to those obtained for a regular pattern with an effective patch size linearly related to the average patch size of the complex pattern. Copyright © 2006 Environment Canada. Published by John Wiley & Sons, Ltd.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.206
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations54
Published2006
Admission routes2
Has abstractyes

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