Simulation of in‐cloud icing events on Mount Washington with the GEM‐LAM
Bibliographic record
Abstract
In‐cloud icing on structures such as transmission lines and wind turbines is an important consideration both for design and operations. It often occurs in coastal areas and over high terrain, where there are virtually no systematic observations. The regional mesoscale model GEM‐LAM of the Canadian Meteorological Center (CMC) was used to model three historical icing events on Mount Washington, where observational data were available. These three events are representative of the most frequent low level wind directions for seven available observation periods. A newly developed sophisticated two‐moment microphysics scheme (Milbrandt‐Yau) is used in GEM‐LAM. The simulated cloud properties and other meteorological data are compared with near surface observational data. Simulation results from the 1‐km resolution run agree best with the observations, with an average RMSE (root mean square error) of 1.6°C for near surface temperature, 4.6 m s−1 for wind speed, 0.23 g m−3 for liquid water content, and 5.8 μm for the median volume droplet diameter. These simulated meteorological fields and cloud properties were used as inputs to a cylindrical sleeve icing model. The modeled icing rate from the GEM‐LAM simulated fields follows the temporal evolution of the observed one with average RMSE of 1.53 g m−1 min−1 compared to an average measured icing rate of 1.98 g m−1 min−1 for all the three cases.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".