Insight on the Coastal Response to Combined Tides, Storm Surges, and Surface Waves in a Macrotidal Bay From Real‐Time Predictions
Bibliographic record
Abstract
Abstract Hazardous sea surface conditions can develop during storm events, when wind‐generated waves and storm surges coincide with high astronomical tides. For better understanding of these conditions, a novel and computationally efficient real‐time forecast model (COASTLINES‐BoF) was developed for a macrotidal bay that is exposed to wind waves, the Bay of Fundy in Atlantic Canada. This forecasting system simulates the combined effects of tides, storm surge, and waves. Spatiotemporally varying meteorological forecasts drive the model, with water levels and ocean waves applied at the open boundary in the Atlantic Ocean, implemented with input from large‐scale ocean forecast models. Analysis of the real‐time performance indicates that the model accurately predicts total water levels compared to observations. Modeled significant wave heights agree with buoy observations and altimeter data. During Post‐tropical Hurricane Fiona in 2022, a peak water level residual (combined storm surge and water level change driven by wave‐current interactions) of over 0.9 m was forecast in the upper Bay of Fundy. Sensitivity analysis indicates that 0.6 m of the water level increase resulted from wind and pressure effects, and an additional 0.3 m water level contribution is a result of wave‐current interaction. The high accuracy, use of open data to drive and validate the model, and relatively low computational demand make this approach a useful way to gain insight into the coastal response to a wide range of conditions. This method can be applied to predict marine environmental conditions in other coastal regions.
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 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".