ADVANCING HINDCASTING AND FORECASTING OF SURFACE WAVES AND HYDRODYNAMICS IN LARGE LAKES AND COASTAL BASINS
Bibliographic record
Abstract
Strong winds blowing over long distances across open water drive elevated water levels and generate large waves. Predicting the effects of extreme events in coastal regions is a challenge. In this work, circulation and surface waves are simulated with high resolution Deflt3D and SWAN models in large coastal regions to advance the understanding of the processes driving extreme hydrodynamic conditions for improved storm hazard prediction. Extreme events in Lake Ontario were simulated to evaluate the processes driving storm surge and waves. Wind and pressure field inputs from different atmospheric models were used to drive the coupled hydrodynamic-wave models and yielded different results. Including baroclinic processes also impacted how surface behaviour was represented in the stratified lake. To forecast hydrodynamics in the lake, an automated system was developed that simulates water levels and surface waves in real-time. The modelling system achieved accurate predictions compared to observations during strong storm events and required a relatively low computational demand compared to other operational forecast models. This forecast system was modified for application to a tidally driven region, the Bay of Fundy, by adding ocean wave and water level boundary conditions. Predictions agreed well with observations, and data from satellite altimeters provided additional validation in regions with limited in-situ data. Results were validated during the passing of Hurricane Fiona, where the dynamically coupled wave and circulation model accounted for contributions to total water levels from tidal inputs, wind set-up, and wave-current interactions. The wave environment during this event was also simulated in a region that experienced stronger hurricane impacts, the Gulf of St. Lawrence. Strong agreement was achieved with available in-situ and altimeter data. The size of the gulf restricted interactions between locally generated waves and swell, limiting wave growth. Smaller, fetch limited waves in a back-barrier bay connected to the gulf were simulated in higher resolution. This work emphasizes the need for detailed model validation against available observations, and highlights that accurate atmospheric forcing, high resolution grids, and factors that may influence surface behaviour, such as stratification or wave-current interactions, are essential to achieving accurate predictions of surface dynamics in coastal regions.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".