Hurricane tangential wind profile from synthetic aperture radar observations
Bibliographic record
Abstract
Over the last several years, considerable efforts have been devoted to investigate hurricane surface winds using SAR observations [1]-[6]. Compared to optical satellite sensors, synthetic aperture radar (SAR) has advantages for observation of ocean surface winds, with high resolution and large spatial coverage, in almost all-weather conditions. Measurements of the inner-core intensities and tangential wind profiles are generally provided by NOAA aircraft flying though the hurricanes [7]. However, aircraft only can provide single point observations along the flight track. Thus, there can be some variance in the aircraft-measured tangential wind profiles, particularly for those hurricanes with asymmetric wind structures. For example, for a non-symmetric hurricane with an elliptic eye, we can assume that the true maximum wind speed exists somewhere on the major axis direction. Therefore, if the aircraft flies along the minor axis of this hurricane, instead of the major axis, the maximum wind and the radius of maximum wind (RMW) observations are probably not accurate. In this study, we determine the hurricane eye center and its extent, and propose a model to estimate hurricane intensity and structure parameters. A specific objective is to describe the tangential wind profile corresponding to the reintensification phase in the hurricane eyewall replacement cycle (ERC).
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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) | 0.000 | 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 | 0.000 |
| 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".