Application of a new wind gust parameterization: Multiscale case studies performed with the Canadian regional climate model
Bibliographic record
Abstract
The implementation of a physically based parameterization scheme for computation of wind gusts in a numerical regional climate model (RCM) is described in this paper. The method is based on an innovative approach proposed by Brasseur [2001] that assumes that gusts occurring at the surface result from the deflection of air parcels flowing higher in the boundary layer. Our parameterization scheme is developed so as to use quantities available at each model time step: consequently, the gusts are also computed for each of these time steps. To illustrate the performances of this novel method, gusts simulated for two severe midlatitude windstorms with the Canadian RCM at various resolutions are compared with observed gust speeds. The study is carried out concurrently for the complex terrain of Switzerland and for the smoother topography of Belgium. A preliminary analysis indicates that this parameterization performs equally well over flat and over mountainous regions; it also responds properly in the strengthening as well as the weakening phases of wind storms. The storm‐dependent results rely on the model configuration associated with the downscaling procedure, as well as on the accuracy of the simulated flow fields. The model response is dependent on the resolved topography distribution and height and on the types of lower boundary conditions that affect the stability of the boundary layer. The simulated gusts are generally more realistic at higher resolution over the complex topography of Switzerland but are less sensitive to resolution over the flat terrain as in Belgium. On the basis of these two storms, this study also shows that simple scaling coefficients relating gust speeds and resolution are not an appropriate method for addressing such issues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".