Tropical Cyclone Boundary Layer Rolls in Synthetic Aperture Radar Imagery
Bibliographic record
Abstract
Abstract Marine atmospheric boundary layer (MABL) roll plays an important role in the turbulent exchange of momentum, sensible heat, and moisture throughout MABL of tropical cyclone (TC). Hence, rolls are believed to be closely related to TC's development, intensification, and decay processes. Spaceborne synthetic aperture radar (SAR) provides a unique capability to image the sea surface imprints of quasi‐linear streaks induced by the MABL rolls within a TC. In this study, sixteen SAR images, including three images acquired during three major hurricanes: Irma, Jose, and Maria in the 2017 Atlantic hurricane season, were utilized to systematically map the distribution and wavelength of MABL rolls under the wide range of TC intensities. The images were acquired by SAR onboard RADARSAT‐1/2, ENVISAT, and SENTINEL‐1 satellites. Our findings are in agreement with the previous one case study of Hurricane Katrina (2005), showing the roll wavelengths are between 600 and 1,600 m. We also find that there exist roll imprints in eyewall and rainbands, although the boundary layer heights are shallower there. Besides, the spatial distribution of roll wavelengths is asymmetrical. The roll wavelengths are found to be the shortest around the storm center, increase and then decrease with distance from storm center, reaching the peak values in the range of , where is defined as the physical location to TC centers normalized by the radius of maximum wind. These MABL roll characteristics cannot be derived using conventional aircraft and land‐based Doppler radar observations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".