Bibliographic record
Abstract
Abstract. In this study, we utilized a high-resolution (4 km) convection-permitting Weather Research Forecasting (WRF) simulation spanning a 13-year period (2000–2013) to investigate the climatological features of Low-level Jets (LLJs) over North America. The 4-km simulation enabled us to represent the effects of orography and the underlying surface on the boundary layer winds better. Focusing on the continental US and the adjacent border regions of Canada and Mexico, this study characterizes the spatial distribution, seasonal patterns, and diurnal fluctuations of northerly/southerly LLJ occurrence frequencies. This paper not only identified several well-known large-scale LLJs in North America, such as the southerly Great Plains LLJ and the summer northerly California coastal LLJ, but also the Quebec northerly LLJ, which gets less focus before. Moreover, the high-resolution simulation revealed climatic characteristics of weaker and smaller-scale LLJs or low-level wind maxima in regions with complex terrains, such as the northerly LLJs in the foothill regions of the Rocky Mountains and the Appalachian during the winter. Additionally, the different thermal and dynamic mechanisms forming significant LLJs near the Great Plains, California, and Quebec are investigated. This study provides valuable insights into the climatological features of LLJs in North America and the high-resolution simulation offers a more detailed understanding of LLJ behavior near complex terrains and other smaller-scale features.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.248 | 0.181 |
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".