Is a more physical representation of aerosol chemistry needed for fog forecasting?
Bibliographic record
Abstract
Abstract With the changing climate, the study of fog formation is essential due to the impact of the complexity of natural and anthropogenic aerosols. The evolution of the droplet size distribution in the presence of different aerosol species remains poorly understood. To make progress towards reducing the uncertainty of fog forecasts, the Eulerian–Lagrangian particle‐based small‐scale model for the diffusional growth of droplets is used to better understand the droplet activation and growth. The small‐scale model simulations are performed using observed data from the Winter Fog Experiment study over Indira Gandhi International Airport, New Delhi. The microphysical properties, such as droplet number concentrations (Nd) and liquid water content (LWC), important for fog simulation, are evaluated to gain more insights. The small‐scale simulations have shown the droplet microphysical properties at different evolutionary stages. The Nd and effective radius change with variations in LWC for different aerosol chemistries (i.e., organics, mix, and inorganic). The calculated visibility at small scale is also shown with the variation of Nd and LWC. This study compared visibility from an existing parametrization with parcel–direct numerical simulation calculation. The hygroscopicity , which is highly related to the activation of aerosols to condensation nuclei, is taken into account to demonstrate the contribution of aerosol chemistry to fog droplet formation. The results highlight that hygroscopicity is essential in the numerical model for fog and visibility prediction as the microphysical properties of fog are regulated by aerosol species.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".