Bibliographic record
Abstract
Abstract. There have been many studies of marine fog, some using Weather Research and Forecasting (WRF) and other models. Several model studies report overpredictions of near-surface liquid water content (Qc), leading to visibility estimates that are too low. This study has found the same. One possible cause of this overestimation could be the treatment of a surface deposition rate of fog droplets at the underlying water surface. Most models, including the Advanced Research Weather Research and Forecasting (WRF-ARW) Model, available from the National Center for Atmospheric Research (NCAR), take account of gravitational settling of cloud droplets throughout the domain and at the surface. However, there should be an additional deposition as turbulence causes fog droplets to collide and coalesce with the water surface. A water surface, or any wet surface, can then be an effective sink for fog water droplets. This process can be parameterized as an additional deposition velocity with a model that could be based on a roughness length for water droplets, z0c, that may be significantly larger than the roughness length for water vapour, z0q. This can be implemented in WRF either as a variant of the Katata scheme for deposition to vegetation or via direct modifications in boundary-layer modules.
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 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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.034 | 0.028 |
| Insufficient payload (model declined to judge) | 0.151 | 0.120 |
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".