Boundary Layer Parameterizations to Simulate Fog Over Atlantic Canada Waters
Bibliographic record
Abstract
Abstract In this study, a series of fog events that occurred near Halifax, Canada, during 20 June to 31 July 2016 are investigated using the Weather Research and Forecasting Model Version 3.8.1 (WRF), in comparison with in situ and satellite remotely sensed observations from the Moderate Resolution Imaging Spectroradiometer. We evaluate five planetary boundary layer (PBL) schemes available in WRF. Results show that these five PBL schemes lead to overestimates in liquid water content, especially the nonlocal schemes, and that they are biased early, in terms of the predicting the onset of fog, and late, in terms of fog dissipation, although their spatial patterns of fog are in good agreement with those suggested by Moderate Resolution Imaging Spectroradiometer imagery. The Kunkel equation is used to calculate visibility, based on WRF modeling of liquid water content. Comparisons with observed visibility show that this methodology sometimes fails to predict fog dissipation. We present a modification of this formulation for visibility that shows improved agreement with observations and more accurate fog dissipation. Continued improvements in the PBL scheme and visibility parameterization are needed for more accurate fog prediction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".