Microphysical Observations and Mesoscale Model Simulation of a Warm Fog Case during FRAM Project
Why this work is in the frame
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Bibliographic record
Abstract
The objective of this work is to apply a new microphysical parameterization for fog visibility for potential use in numerical weather forecast simulations, and to compare the results with ground-based observations. The observations from the Fog Remote Sensing And Modeling (FRAM) field which took place during the winter of 2005–2006 over southern Ontario, Canada (Phase I) were used in the analysis. The liquid water content (LWC), droplet number concentration (Nd), and temperature (T) were obtained from the fog measuring device (FMD) spectra and Rosemount probe, correspondingly. The visibility (Vis) from a visibility meter, liquid water path from microwave radiometers (MWR), and inferred fog properties such as mean volume diameter, LWC, and Nd were also used in the analysis. The results showed that Vis is nonlinearly related to both LWC and Nd. Comparisons between newly derived parameterizations and the ones already in use as a function of LWC suggested that if models can predict the total Nd and LWC at each time step using a detailed microphysics parameterization, Vis can then be calculated for warm fog conditions. Using outputs from the Canadian Mesoscale Compressible Community (MC2) model, being tested with a new multi-moment bulk microphysical scheme, the new Vis parameterization resulted in more accurate Vis values where the correction reached up to 20–50%.
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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 | 0.000 | 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) | 0.000 | 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 it