Sensitivity of Numerical Simulations of Near-Surface Atmospheric Conditions to Snow Depth and Surface Albedo during an Ice Fog Event over Heber Valley
Bibliographic record
Abstract
Abstract This study examines the sensitivity of numerical simulations of near-surface atmospheric conditions to the initial surface albedo and snow depth during an observed ice fog event in the Heber Valley of northern Utah. Numerical simulation results from the mesoscale community Weather Research and Forecasting (WRF) Model are compared with observations from the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program fog field program. It is found that near-surface cooling during the nighttime is significantly underestimated by the WRF Model, resulting in the failure of the model to reproduce the observed fog episode. Meanwhile, the model also overestimates the temperature during the daytime. Nevertheless, these errors could be reduced by increasing the initial surface albedo and snow depth, which act to cool the near-surface atmosphere by increasing the reflection of downward shortwave radiation and decreasing the heating effects from the soil layer. Overall results indicate the important effects of snow representation on the simulation of near-surface atmospheric conditions and highlight the need for snow measurements in the cold season for improved model physics parameterizations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".