Deterministic and ensemble storm surge prediction for Atlantic Canada with lead times of hours to ten days
Bibliographic record
Abstract
Regional deterministic and ensemble surge prediction systems (RDSPS and RESPS respectively) are used to forecast sea levels off the east of Canada and northeast US. The surge models for the RDSPS and RESPS have grid spacings of 1/30° and 1/12° respectively. The models are driven by surface air pressure and 10 m winds generated by operational global deterministic and ensemble prediction systems that are run operationally by the Canadian Meteorological Centre. Surge forecasts are evaluated for the period 1 March, 2013 to 31 March 2014. Based on traditional statistics (e.g., standard deviation of the difference between observations and predictions) both systems are shown to have skill in forecasting surges six days into the future. It is shown however that skill exists beyond six days if allowance is made for errors in the timing of large surges. The usefulness of the RESPS is demonstrated for two positive surges (important for coastal flooding and erosion) and a negative surge (important for safe navigation in shallow water). It is shown that the RESPS can identify events not forecast by the RDSPS, and can also add useful additional information on the timing of the surge, an important consideration in tidally dominated waters. Several new types of display are used to illustrate the sort of information that can be generated by the RESPS to support the issuers of warnings of unusually high and low total water levels.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".