Impact of sea spray on the <scp>Y</scp>ellow and <scp>E</scp>ast <scp>C</scp>hina <scp>S</scp>eas thermal structure during the passage of <scp>T</scp>yphoon <scp>R</scp>ammasun (2002)
Bibliographic record
Abstract
Abstract Strong winds lead to large amounts of sea spray in the lowest part of the atmospheric boundary layer. The spray droplets affect the air‐sea heat fluxes due to their evaporation and the momentum due to the change of sea surface, and in turn change the upper ocean thermal structure. In this study, impact of sea spray on upper ocean temperatures in the Yellow and East China Seas (YES) during typhoon Rammasun's passage is investigated using the POMgcs ocean model with a sea spray parameterization scheme, in which the sea spray‐induced heat fluxes are based on an improved Fairall's sea spray heat fluxes algorithm, and the sea spray‐induced momentum fluxes are derived from an improved COARE version 2.6 bulk model. The distribution of the sea spray mediated turbulent fluxes was primarily located at Rammasun eye‐wall region, in accord with the maximal wind speeds regions. When Rammasun enters the Yellow sea, the sea spray mediated latent (sensible) heat flux maximum is enhanced by 26% (13.5%) compared to that of the interfacial latent (sensible) heat flux. The maximum of the total air‐sea momentum fluxes is enhanced by 43% compared to the counterpart of the interfacial momentum flux. Furthermore, the sea spray plays a key role in enhancing the intensity of the typhoon‐induced “cold suction” and “heat pump” processes. When the effect of sea spray is considered, the maximum of the sea surface cooling in the right side of Rammasun's track is increased by 0.5°C, which is closer to the available satellite 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 | 0.000 | 0.000 |
| 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".