La représentation des surfaces continentales pour la prévisionnumérique du temps
Bibliographic record
Abstract
La représentation des processus physiques associés aux surfaces continentales, incluant les échanges de chaleur, d'humidité et de quantité de mouvement avec l'atmosphère, ainsi que l'analyse des conditions initiales pour ses principales variables influencent de manière substantielle la prévision atmosphérique près de la surface, en plus d'avoir un impact sur la production de nuages et des précipitations. Comment les surfaces continentales sont-elles représentées dans les modèles de prévision numérique du temps ? Quelles sont les problématiques propres à la prévision numérique du temps dans cette représentation ? Ces questions sont examinées dans cet article en utilisant des exemples tirées du modèle Isba (Interactions solbiosphère-atmosphère) développé à Météo-France et du système d'assimilation de surface du Service météorologique du Canada. The representation of physical processes over land, including heat, humidity, and momentum exchanges with the atmosphere, as well as accurate initialisation of its main prognostic variables, has a substantial influence on numerical prediction of the near-surface atmosphere and on the formation of clouds and precipitation. How are continental surfaces represented in numerical weather prediction (NWP) models? What are the scientific issues specif ic to NWP for this representation? These are questions examined in this study using examples from the Isba (Interactions Soil-Biosphere-Atmosphere) land surface scheme developed at Météo-France and the land data assimilation system from the Meteorological Service of Canada.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; both teacher heads agree on what is shown here.
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".