Hurricane Sally (2020) Shifts the Ocean Thermal Structure across the Inner Core during Rapid Intensification over the Shelf
Bibliographic record
Abstract
Abstract Prediction of rapid intensification in tropical cyclones prior to landfall is a major societal issue. While air–sea interactions are clearly linked to storm intensity, the connections between the underlying thermal conditions over continental shelves and rapid intensification are limited. Here, an exceptional set of in situ and satellite data are used to identify spatial heterogeneity in sea surface temperatures across the inner core of Hurricane Sally (2020), a storm that rapidly intensified over the shelf. A leftward shift in the region of maximum cooling was observed as the hurricane transited from the open gulf to the shelf. This shift was generated, in part, by the surface heat flux in conjunction with the along- and across-shelf transport of heat from storm-generated coastal circulation. The spatial differences in the sea surface temperatures were large enough to potentially influence rapid intensification processes suggesting that coastal thermal features need to be accounted for to improve storm forecasting as well as to better understand how climate change will modify interactions between tropical cyclones and the coastal ocean. Significance Statement The connections between the underlying thermal energy in the ocean that powers tropical cyclones and rapid intensification of storms over continental shelves are limited. An exceptional set of data collected in the field as well as from space with satellites was used to identify spatial variations in sea surface temperatures across the inner core of Hurricane Sally (2020), a storm that rapidly intensified over the shelf. The spatial differences were due to the heat loss from the surface of the ocean as well as heat transport by shelf currents. The spatial differences were large enough to potentially influence how quickly storms can intensify, suggesting that coastal thermal features need to be accounted for to improve storm forecasting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".