U.S. IOOS Coastal and Ocean Modeling Testbed: Hurricane‐Induced Winds, Waves, and Surge for Deep Ocean, Reef‐Fringed Islands in the Caribbean
Bibliographic record
Abstract
Abstract As part of a U.S. Integrated Ocean Observing System (IOOS) funded Coastal and Ocean Modeling Testbed (COMT), hindcasts of waves and storm surge for 2017 Hurricanes Irma and Maria are examined and compared to wave and water level gauge data in the vicinity of Puerto Rico and the U.S. Virgin Islands. The region is characterized by adjacent deep ocean water, narrow shelves, and coral reef systems providing coastal protection. The storm physics are analyzed using an unstructured grid third‐generation wave circulation coupled modeling system (ADCIRC+SWAN) with respect to tides, winds, atmospheric pressure, waves, and wave radiation stress‐induced setup. The water level response is generally dominated by the pressure deficit of the hurricanes. Wind‐driven surge is important over the shallow shelf to the east of Puerto Rico and wave‐induced setup becomes significant at locations in close proximity to the coastline. Contrary to conditions along the Gulf of Mexico shelf, geostrophically induced setup is negligible. Characteristics from a range of meteorological forcing models are assessed, and the associated errors in the hydrodynamic response are quantified. A data‐assimilated tropical planetary boundary model leads to the smallest atmospheric pressure, water level and wave property errors across both storms. Through comparisons between ADCIRC+SWAN and SLOSH‐FW (a structured grid first‐generation wave circulation coupled model), it is shown that the response to atmospheric forcing is similar; however, nearshore wave setup is smaller in SLOSH‐FW due to its coarser resolution here. Further, in addition to erroneous wind‐driven surge through depth limiting over the open ocean, numerical oscillations in the water level time series develop in SLOSH‐FW likely due to its small domain size.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".