High-Resolution GEM-LAM Application in Marine Fog Prediction: Evaluation and Diagnosis
Bibliographic record
Abstract
Abstract A three-level nested rendering of a high-resolution limited-area model version of the Global Environment Multiscale configuration (GEM-LAM), running quasi-operationally at the Canadian Meteorological Centre, is evaluated for its capabilities in marine fog prediction. The model shows a general underestimation of the cloud water content at lower levels that is utilized as one of the proxies for fog and/or low stratus. A warm and dry tendency also appears at the lowest layer (a few hundreds of meters above the surface) of the vertical profiles and at screen level. The condensation scheme directly generates/dissipates the cloud water content (or fog) while boundary layer processes [such as moist turbulent kinetic energy (MoisTKE)] vertically redistribute it. However, the results presented here emphasize the significance of the accurate initial and vertical velocity fields, as well as the interactions between the condensation scheme and the radiation scheme that interacts fully with clouds. These conclusions suggest that a delicate balance among the different physical processes and dynamics is needed for a successful fog forecast.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".