NUMERICAL MODELLING THE IMPACTS OF HURRICANE FLORENCE (2018) ON COASTAL NORTH CAROLINA, USA
Bibliographic record
Abstract
Hurricanes have high winds that can generate strong ocean surface circulation, large surface waves and storm surges. In a typical year, several hurricanes form over the Atlantic Ocean and interact with the continental shelf making landfall on the east coast of North America. In 2018, Hurricane Florence crossed the narrow continental shelf then made landfall in Onslow Bay, North Carolina, USA. Onslow Bay is a 160 km curved embayment rimmed by barrier islands and narrow back-barrier lagoons. Two large-scale investigations of Hurricane Florence are completed by using a hydrodynamic model and two different model grids: Delft3D with a structured grid and Delft3D-Flexible Mesh with an unstructured grid. The large-scale models were used to analyze the waves, currents and storm surges generated by a major storm that moved across the continental shelf and onto the coastline. This study aims to gain insight into the affects a landfalling hurricane has offshore on the continental shelf and nearshore on beaches and in back-barrier lagoons. Model results are validated at twelve sites, and the model is used to spatially explore the waves and hydrodynamics. The deep and shallow water source terms that control the generation and dissipation of the surface wave field are examined over the continental shelf by using the structured model, Delft3D. The unstructured model, Delft3D-Flexible Mesh, is used to investigate the hydrodynamics of four barrier island systems located throughout Onslow Bay. The results conclude that the impacts of a hurricane at landfall vary based on the shape of the coastline and the intensity and track of the storm. Overall, this research contributes to a better understanding of hurricane wave processes on the continental shelf and the storm surge response of different barriers island systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".