Characterizing and Constraining Uncertainty Associated with Surface and Boundary Layer Turbulent Fluxes in Simulations of Lake-Effect Snowfall
Bibliographic record
Abstract
Abstract Lake-effect snow (LeS) storms are driven by strong turbulent surface layer (SL) and planetary boundary layer (PBL) fluxes of heat and moisture caused by the flow of cold air over relatively warm water. To investigate the sensitivity of simulated LeS to the parameterization of SL and PBL turbulence, high-resolution simulations of two major storms, downwind of Lakes Superior and Ontario, are conducted using the Weather Research and Forecasting Model. Multischeme and parameter sensitivity experiments are conducted. Measurements of overlake fluxes and downwind snowfall are used to evaluate the simulations. Consistent with previous studies, LeS is found to be strongly sensitive to SL and PBL parameterization choices. Simulated precipitation accumulations differ by up to a factor of 2 depending on the schemes used. Differences between SL schemes are the dominant source of this sensitivity. Parameterized surface fluxes of sensible and latent heat can each vary by over 100 W m−2 between SL schemes. The magnitude of these fluxes is correlated with the amount of downwind precipitation. Differences between PBL schemes play a secondary role, but have notable impacts on storm morphology. Many schemes produce credible simulations of overlake fluxes and downwind snowfall. However, the schemes that produce the largest surface fluxes produce fluxes and precipitation accumulations that are biased high relative to observations. For two SL schemes studied in detail, unrealistically large fluxes can be attributed to parameter choices: the neutral stability turbulent Prandtl number and the threshold friction velocity used for defining regimes in the overwater surface roughness calculation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".