Bibliographic record
Abstract
Abstract An improved treatment of advection is essential for atmospheric transport and chemistry models. Eulerian treatments are generally plagued with instabilities, unrealistic negative constituent values, diffusion, and dispersion errors. A higher-order Eulerian model improves one error at significant cost but magnifies another error. The cost of semi-Lagrangian models is too high for many applications. Furthermore, traditional trajectory “Lagrangian” models do not solve both the dynamical and tracer equations simultaneously in the Lagrangian frame. A fully Lagrangian numerical model is, therefore, presented for calculating atmospheric flows. The model employs a Lagrangian mesh of particles to approximate the nonlinear advection processes for all dependent variables simultaneously. Verification results for simulating sea-breeze circulations in a dry atmosphere are presented. Comparison with Defant’s analytical solution for the sea-breeze system enabled quantitative assessment of the model’s convergence and stability. An average of 20 particles in each cell of an 11 × 20 staggered grid system are required to predict the two-dimensional sea-breeze circulation, which accounts for a total of about 4400 particles in the Lagrangian mesh. Comparison with Eulerian and semi-Lagrangian models shows that the proposed fully Lagrangian model is more accurate for the sea-breeze circulation problem. Furthermore, the Lagrangian model is about 20 times as fast as the semi-Lagrangian model and about 2 times as fast as the Eulerian model. These results point toward the value of constructing an atmospheric model based on the fully Lagrangian approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".