Sensitivity Analysis on Sea Surface Drag Parameterization in Storm Surge Modeling
Bibliographic record
Abstract
Storm surges can be described as nonperiodic fluctuations in sea levels associated with variations in wind stress and atmospheric pressure, caused by the approach of cyclones or extratropical storms. They can have catastrophic results on coastal communities, particularly in combination with high tides and large waves. Therefore, for the efficiency of storm surge modeling, the accuracy and resolution of meteorological data as well as hydrodynamic processes, which are essentially governed by the atmospheric flow under such strong wind conditions, are particularly crucial. In addition to the accurate forecasts of wind speed, there is uncertainty in translating wind speed to the wind shear stress, which is an essential contributor to storm surges that quantify the driving force of wind to ocean flow using a sea-surface drag in the numerical models.The main purpose of this study is to improve our understanding of the role of sea surface drag parametrization in storm surge dynamics and reproduction within the Global Tide and Surge Model (GTSM). The depth-averaged hydrodynamic model GTSM, which has global coverage and spatially varying resolution increasing towards the coast, can be used to simulate changes in water levels and currents caused by tides and storm surges. In this study, the GTSM is used to compute tide and surge combined to provide accurate tide-surge interactions, and we conduct a sensitivity study of the storm surges to evaluate the performance of various sea surface drag parameterization in predicting storm surge behavior to determine the most suitable one. The model's performance is assessed based on a comparison of modeled and observed storm surges, both being estimated based on tidal analysis of total water levels. We investigate the performance from 2006 to 2022 and analyze 20 specific extreme weather events, such as extra-tropical storm Xaver that occurred over the North Sea and post-tropical cyclone Fiona hit Canada. The results of this study will provide valuable insights into the most suitable sea surface drag parameterization for the prediction of tide-surge interactions, surge signal's mean behavior, and storm surge dynamics within the GTSM under storm conditions.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".