RADARSAT-1 SAR for Hurricane Watch
Bibliographic record
Abstract
RADARSAT-1's ScanSAR wide (SCW) modes, with swath widths of 450 or 500 km and a spatial resolution of 100 m, can provide both near-synoptic scale and small-scale views of the imprint of mesoscale meteorological processes and features on the ocean surface's roughness. In the case of hurricanes, the images show wind speed and direction effects around the relatively calm eye, as well as regions of intense convection, rainfall, organized boundary layer phenomena such as boundary layer rolls, and storm-generated swell. Although initial observations were largely serendipitous, Hurricane Watchhas routinely acquired SCW imagery of hurricanes during the 1999 and 2000 Atlantic Basin hurricane seasons (nominally August through October). The Canadian Space Agency's Disaster Watch Program and Background Mission Program submitted SCW imagery requests in support of this project. Disaster Watch made manual requests as late as 29 hours in advance of the pass time that were fewer in number but more accurate than those of the Background Mission. In this paper, we discuss some of the scheduling and swath coverage constraints, and show and discuss some of the striking images that were acquired. New insights to storm morphology, storm dynamics, and SAR ocean imaging have followed from these observations. Hurricane Watch will be repeated in 2001, with a particular emphasis on the study of organized boundary layer structures between rain bands. The wide spread extent of these structures was first revealed by RADARSAT-1 images.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.016 |
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".