Cold Fog Amongst Complex Terrain
Bibliographic record
Abstract
Abstract Cold fog forms via various thermodynamic, dynamic, and microphysical processes when the air temperature is less than 0°C. It occurs frequently during the cold season in the western United States yet is challenging to detect using standard observations and is very difficult to predict. The Cold Fog Amongst Complex Terrain (CFACT) project was conceived to investigate the life cycle of cold fog in mountain valleys. The overarching goals of the CFACT project are to 1) investigate the life cycle of cold-fog events over complex terrain with the latest observation technology, 2) improve microphysical parameterizations and visibility algorithms used in numerical weather prediction (NWP) models, and 3) develop data assimilation and analysis methods for current and next-generation (e.g., subkilometer scale) NWP models. The CFACT field campaign took place in Heber Valley, Utah, during January and February 2022, with support from NSF’s Lower Atmospheric Observing Facilities (managed by NCAR’s Earth Observing Laboratory), the University of Utah, and Ontario Technical University. A network of ground-based and aerial in situ instruments and remote sensing platforms were used to obtain comprehensive measurements of thermodynamic profiles, cloud microphysics, aerosol properties, and environmental dynamics. Nine intensive observation periods (IOPs) explored various mountainous weather and cold-fog conditions. Field observations, NWP forecasts, and large-eddy simulations provided unprecedented data sources to help understand the mechanisms associated with cold-fog weather and to identify and mitigate numerical model deficiencies in simulating winter weather over mountainous terrain. This article summarizes the CFACT field campaign, its observations, and challenges during the field campaign, including real-time fog prediction issues and future analysis.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 teacher head, 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".