A Mesoscale Analysis Method for Surface Potential Temperature in Mountainous and Coastal Terrain
Bibliographic record
Abstract
Abstract A technique is developed to anisotropically spread surface observations in steep valleys. The goal is to create an improved objective analysis for the lowest, terrain-following numerical weather prediction (NWP) model level in mountainous terrain. The method is a mother–daughter (MD) approach, where the amount of information transferred from one grid point (the mother) to all neighboring grid points (the daughters) depends on elevation differences. The daughters become mothers and further share information with their neighboring grid points. This iterative method allows information to follow valleys around ridges, while reducing spread over the ridge top. The method is further refined to account for land–sea anisotropy. This approach is tested in the objective analyses of surface potential temperatures over the steep mountainous and coastal terrain of southwestern British Columbia, Canada. Analysis results are compared with other existing schemes using the Advanced Regional Prediction System Data Assimilation System (ADAS). It is found that the MD approach outperforms the other schemes over mountainous and coastal terrain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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 teacher head, 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".