Modeling the circulation of Western Bank on the Scotian Shelf through sequential application of a variational algorithm and a nonlinear model
Bibliographic record
Abstract
A new approach is presented for the modeling of the quasi‐stationary circulation through the sequential application of a variational algorithm and a nonlinear diagnostic model. The model loop begins with data assimilation of temperature, salinity, surface elevation, and velocity data in a simplified geostrophic model in which transport and continuity equations are treated as weak constraints. The temperature/salinity fields and balanced open boundary conditions points are then used as input to a nonlinear primitive equation model, which employs a turbulent closure scheme. The nonlinear model is then run to produce a diagnostic flow field. A radiation open boundary condition is applied at the outflow points of the open boundaries. These two steps are organized into an iteration cycle by using output from the nonlinear model as input to the variational model. The proposed approach combines the advantages of variational data assimilation in simplified models with a complicated fully nonlinear primitive equation model. We apply the approach to Western Bank on the Scotian Shelf. Comparisons with observed current from Western Bank, in September and October 1998, show that the sequential application of the variational approach and the fully nonlinear model allow determination of the quasi‐stationary circulation whose agreement with the observations is ≈10–30% better than circulation determined from the variational or the nonlinear model alone. Our calculations of the cross‐shelf transport across Western Bank show that it varies from 0.20 Sv to 0.35 Sv over a 2‐week period. The combined models also allow us to determine the character of the circulation over the Bank, the role of wind forcing and the implications for resident biological populations.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".