Modeled MABL Responses to the Winter Kuroshio SST Front in the East China Sea and Yellow Sea
Bibliographic record
Abstract
Abstract Based on the Weather Research Forecasting (WRF) model, we designed a long‐term sea surface temperature (SST)‐forcing simulation to simulate the influences of SST front on the marine atmospheric boundary layer (MABL) over the East China Sea and the Yellow Sea during wintertime. The results revealed that SST front have significant relationship with MABL adjustment. The atmospheric modulations and the comparisons of the vector wind and the scalar wind indicate that the MABL responses to the SST front are quite different under different wind directions. When the prevailing wind blows parallel to the SST front (NE wind directions), the sea level pressure adjustment mechanism dominates the atmospheric adjustment. The variances of scalar wind and vector wind are similar. However, when prevailing wind crossing SST front from cold to warm (NW wind directions), both the sea level pressure adjustment mechanism and vertical mixing mechanism play an important role. The maximum variations of the scalar wind are just over the core of SST front, whereas maximum vector wind variations are over warm flank. When wind blowing from warm to cold (SE wind directions), the vertical mixing mechanism contributes more to the MABL responses. The maximum variations of the scalar wind are beyond the top of MABL, which occur over the core SST front. The further diagnosis analysis showed that the meridional transient eddies and mean flow interaction contribute to the scalar wind variations under cross‐front conditions.
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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) | 0.001 | 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 | 0.000 |
| 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".