Variational Computation of Sensible and Latent Heat Flux over Lake Superior
Bibliographic record
Abstract
Abstract Sensible and latent heat fluxes over Lake Superior are computed using a variational approach with a Bowen ratio constraint and inputs of 7 years of half-hourly temporal resolution observations of hydrometeorological variables over the lake. In an advancement from previous work focusing on the sensible heat flux, in this work computations of the latent heat flux are required so that a new physical constraint of the Bowen ratio is introduced. Verifications are made possible for fluxes predicted by a Canadian operational coupled atmosphere–ocean model due to recent availabilities of observed and model-predicted fluxes over Lake Superior. The observed flux data with longer time periods and higher temporal resolution than those used in previous studies allows for the examination of detailed performances in computing these fluxes. Evaluations utilizing eddy-covariance measurements over Lake Superior show that the variational method yields higher correlations between computed and measured sensible and latent heat fluxes than a flux-gradient method. The variational method is more accurate than the flux-gradient method in computing these fluxes at annual, monthly, daily, and hourly time scales. Under both unstable and stable conditions, the variational method considerably reduces mean absolute errors produced by the flux-gradient approach in computing the fluxes. It is demonstrated with 2 months of data that the variational method obtains higher correlation coefficients between the observed and the computed sensible and latent heat fluxes than the coupled model predicted, and yields lower mean absolute errors than the coupled model. Furthermore, comparisons are made between the coupled-model-predicted fluxes and the fluxes computed based on three buoy observations over Lake Superior.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".