Upper Ocean Response to Typhoon Kalmaegi and Sarika in the South China Sea from Multiple-Satellite Observations and Numerical Simulations
Bibliographic record
Abstract
We investigated ocean surface and subsurface physical responses to Typhoons Kalmaegi and Sarika in the South China Sea, utilizing synergistic multiple-satellite observations, in situ measurements, and numerical simulations. We found significant typhoon-induced sea surface cooling using satellite sea surface temperature (SST) observations and numerical model simulations. This cooling was mainly caused by vertical mixing and upwelling. The maximum amplitudes were 6 °C and 4.2 °C for Typhoons Kalmaegi and Sarika, respectively. For Typhoon Sarika, Argo temperature profile measurements showed that temperature response beneath the surface showed a three-layer vertical structure (decreasing-increasing-decreasing). Satellite salinity observations showed that the maximum increase of sea surface salinity (SSS) was 2.2 psu on the right side of Typhoon Sarika’s track, and the maximum decrease of SSS was 1.4 psu on the left. This SSS seesaw response phenomenon is related to the asymmetrical rainfall on both sides of the typhoon track. Acoustic Doppler Current Profilers measurements and numerical simulations both showed that subsurface current velocities rapidly increased as the typhoon passed, with peak increases of up to 1.19 m/s and 1.49 m/s. Typhoon-generated SST cooling and current velocity increases both exhibited a rightward bias associated with a coupling between typhoon wind-stress and mixed layer velocity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".