Use of Oceanic Reanalysis to Improve Estimates of Extreme Storm Surge
Bibliographic record
Abstract
Abstract A storm surge hindcast for the west coast of Canada was generated for the period 1980–2016 using a 2D nonlinear barotropic Princeton Ocean Model forced by hourly Climate Forecast System Reanalysis wind and sea level pressure. Validation of the modeled storm surges using tide gauge records has indicated that there are extensive areas of the British Columbia coast where the model does not capture the processes that determine the sea level variability on intraseasonal and interannual time scales. Some of the discrepancies are linked to large-scale fluctuations, such as those arising from major El Niño and La Niña events. By applying an adjustment to the hindcast using an ocean reanalysis product that incorporates large-scale sea level variability and steric effects, the variance of the error of the adjusted surges is significantly reduced (by up to 50%) compared to that of surges from the barotropic model. The importance of baroclinic dynamics and steric effects to accurate storm surge forecasting in this coastal region is demonstrated, as is the need to incorporate decadal-scale, basin-specific oceanic variability into the estimation of extreme coastal sea levels. The results improve long-term extreme water level estimates and allowances for the west coast of Canada in the absence of long-term tide gauge records data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".