Meteorological Downscaling with WRF Model, Version 4.0, and Comparative Evaluation of Planetary Boundary Layer Schemes over a Complex Coastal Airshed
Bibliographic record
Abstract
Abstract Evaluation of downscaled meteorological information is crucial to identifying model behaviors that may propagate to end applications such as the simulation of local air quality. This study conducted and assessed yearlong simulations of hourly meteorological conditions over the Terrace–Kitimat Valley of northwestern British Columbia, Canada, at 1-km horizontal gridding for six PBL schemes in the Weather and Forecasting (WRF) Model, version 4.0. In terms of key surface meteorological variables that affect air quality, simulations over land demonstrated better skill for specific humidity and wind direction than for air temperature and wind speed. Spatial differences in modeled atmospheric properties and vertical profiles, especially for moisture content, were used to diagnose the relative capacity of each PBL scheme to represent pollutant dispersion and dilution. Stable conditions at night increased suppression of boundary layer mixing by the nonlocal Yonsei University (YSU) scheme when compared with suppression by the local eddy-diffusion component of the Asymmetric Convective Model, version 2 (ACM2), scheme, resulting in decreased wind speed and ambient temperature but moister air with the YSU scheme. The weakening of mixing by the Mellor–Yamada–Nakanishi–Niino (MYNN3) scheme with inland distance suggested that higher-order, nonlocal transport is sensitive to increasing topographic steepness toward the northern part of the valley. Disparities in mixing strengths among PBL schemes were greater in the summer when conditions were generally less stable with moist, warm air blowing inland than in winter when the valley channels cold, stable air from the interior. Increased convection in daytime led to greater entrainment of air from aloft and a thicker PBL with the YSU scheme than with the ACM2 scheme in summer while increasing countergradient transport in the MYNN3 scheme that reduces dilution.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".