Assessment of a NEMO-based hydrodynamic modelling system for the Great Lakes
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Bibliographic record
Abstract
Environment Canada recently developed a coupled lake–atmosphere–hydrological modelling system for the Laurentian Great Lakes. This modelling system consists of the Canadian Regional Deterministic Prediction System (RDPS), which is based on the Global Environmental Multiscale model (GEM), the MESH (Modélisation Environnementale Surface et Hydrologie) surface and river routing model, and a hydrodynamic model based on the three-dimensional global ocean model Nucleus for European Modelling of the Ocean (NEMO). This paper describes the performance of the NEMO model in the Great Lakes. The model was run from 2004 to 2009 with atmospheric forcing from GEM and river forcing from the MESH modelling system for the Great Lakes region and compared with available observations in selected lakes. The NEMO model is able to produce observed variations of lake levels, ice concentrations, lake surface temperatures, surface currents and vertical thermal structure reasonably well in most of the Great Lakes. However, the model produced a diffused thermocline in the central basin of Lake Erie. The model predicted evaporation is relatively strong in the upper lakes. Preliminary results of the modelling system indicate that the model needs further improvements in atmospheric–lake exchange bulk formulae and surface mixed layer physics.
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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 | 0.010 | 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 | 0.001 | 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) | 0.000 | 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 it