Influence of hurricane wind field variability on real-time forecast simulations of the coastal environment
Bibliographic record
Abstract
Dynamic conditions occur in the coastal ocean during severe storms. Forecasting these conditions is challenging, and large-scale numerical models require significant computing power. In this paper, we describe a real-time modelling system (DUNEX-RT), developed in support of the DUring Nearshore Event eXperiment (DUNEX) in North Carolina, USA. The model is run with wave, current, and water level boundary conditions from larger-scale models, and provides 36-hour forecasts of significant wave height, depth-averaged velocity, and water levels every 6-hours using Delft3D-SWAN. Observations and forecasts run at different times are compared and communicated via an interactive website to verify model performance in real-time and to visualize uncertainty from changing inputs. Here, we evaluate model sensitivity to inputs from different atmospheric hindcasts and forecasts for Hurricane Dorian (2019). The real-time model had relatively low errors across the system, indicating that this novel approach can be applied to forecast other areas of the coastal ocean.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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) | 0.005 | 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 teacher head, 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".