Real-time capability of meteotsunami detection by WERA ocean radar system
Bibliographic record
Abstract
High-frequency (HF) ocean radar systems have demonstrated their capability to capture the signal from tsunami currents after the 2011 Tohoku earthquake and tsunami. However, these systems were configured for mapping surface currents in the coastal zones and not for detecting specific patterns of tsunami waves in real-time. The WERA®ocean radar system was optimized for tsunami alerting and installed for real-time tsunami monitoring at the coast of Tofino, British Columbia, Canada. If the shelf extends tens of kilometers off the coast then the first appearance of tsunami waves can be monitored early enough to issue an alert message. On 14 October 2016, the WERA system detected strong changes in measured radial currents at distances up to 60 km off the coast and triggered an alert immediately. The system tracked the unusual current pattern in real-time following the wave propagation coincided with an atmospheric cold frontal passage. No earthquake occurred at that time but strong storm currents caused most likely by remnants of typhoon Songda were the most likely reason for the detection. This localized event seems to be the combination of the strong currents caused by the typhoon, the storm surge, and a large wave that could be classified as a meteorological tsunami (meteotsunami). This detection showed the capability of the radar to measure unusual surface current velocities induced by tsunami waves. The real-time detection and alerting on this tsunami-like current have shown a good applicability of HF phased-array radar technology for offshore tsunami monitoring and navigation safety.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".