Mesoscale Reproducibility in Regional Ocean Modelling with a Three-Dimensional Stratification Estimate Based on Aviso-Argo Data
Bibliographic record
Abstract
For dynamically consistent, high-resolution, yet cost-effective regional oceanic downscaling modelling, an empirical three-dimensional (3D) density estimate based on publicly available datasets is utilized for the Regional Oceanic Modeling System (ROMS) with simple data assimilation (i.e., TS nudging, where TS stands for temperature and salinity). We rely on a method built upon the two-layer model to reconstruct a mesoscale 3D temperature and salinity field, referred to as Tokyo University of Marine Science and Technology (TUMSAT)-TS, using near real-time altimeter-derived dynamic height along with Argo float profiling data. The TUMSAT-TS is first validated using in situ hydrographic data, then is implemented in the Japan Coastal Ocean Predictability Experiment (JCOPE2)-ROMS downscaling system for the Kuroshio region off Japan. We explore the usability of TUMSAT-TS by carrying out three comparative simulations with temperature and salinity nudging towards the (i) TUMSAT-TS and (ii) JCOPE2-TS fields, and (iii) without the nudging. Whereas the unassimilated case fails to properly account for the Kuroshio, both datasets individually are found to help reproduce the mesoscale variability of the Kuroshio, as well as its transient paths, volume transport, associated kinetic energy (KE) and eddy KE, and seasonally varying stratification.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".