Comparison of Typhoon Locations over Ocean Surface Observed by Various Satellite Sensors
Bibliographic record
Abstract
In this study, typhoon eyes have been delineated using wavelet analysis from the synthetic aperture radar (SAR) images of ocean surface roughness and from the warm area at the cloud top in the infrared (IR) images, respectively. Envisat SAR imagery, and multi-functional transport satellite (MTSAT) and Feng Yun (FY)-2 Chinese meteorological satellite IR imagery were used to examine the typhoons in the western North Pacific from 2005 to 2011. Three cases of various typhoons in different years, locations, and conditions have been used to compare the typhoon eyes derived from SAR (on the ocean surface) with IR (at the cloud-top level) images. Furthermore, the best track data from the Joint Typhoon Warning Center (JTWC), Chinese Meteorological Administration (CMA), and the Japan Meteorological Agency (JMA) are checked for the calibration. Because of the vertical wind shear, which acts as an upright tilt, the location of the typhoon eye on the ocean surface differs from that at the top of the clouds. Consequently, the large horizontal distance between typhoon eyes on the ocean surface and on the cloud top implies that the associated vertical wind shear profile is considerably more complex than generally expected. This result demonstrates that SAR can be a useful tool for typhoon monitoring study over the ocean surface.
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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) | 0.000 | 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".