Comparisons of the Thermal Effects of the Tibetan Plateau with NCEP-I and ERA-40 Reanalysis Data
Bibliographic record
Abstract
Because of a lack of sufficient observations over the Tibetan Plateau (TP), the measurement of the strength of the thermal effects of the TP is a challenging issue, yet of crucial importance for climate research. In this study, two sets of daily reanalysis data (1979–2001) from the National Centers for Environmental Prediction reanalysis version 1 (NCEP-I) and the European Centre for Medium-Range Weather Forecasts 40-year Reanalysis data (ERA-40) are used to calculate the atmospheric diabatic heating effects of the TP and the adjacent regions with an inverse algorithm. The observational data obtained from the recent atmospheric experiments over the TP are applied to verify and evaluate the results from NCEP-I and ERA-40 reanalysis data, respectively. It is found that the ERA-40 results are closer to the observations in terms of the structure and seasonal variation of the vertical profile of the diabatic heating over the TP. The horizontal distributions of the apparent heat source are quite reasonable over Asia for both the NCEP-I and ERA-40 results. RÉSUMÉ [Traduit par la rédaction] À cause d'un manque d'observations sur le plateau tibétain, la mesure de la force des effets thermiques du plateau tibétain est un problème difficile tout en étant d'importance cruciale pour la recherche climatique. Dans cette étude, nous utilisons deux ensembles de données quotidiennes de réanalyse (1979–2001) issues de la version 1 de la réanalyse des National Centers for Environmental Prediction (NCEP-I) ainsi que les données de réanalyse de 40 ans du Centre européen pour les prévisions météorologiques à moyen terme (ERA-40) pour calculer les effets du réchauffement atmosphérique diabatique sur le plateau tibétain et les régions adjacentes au moyen d'un algorithme inverse. Nous appliquons les données d'observation recueillies lors des récentes expériences atmosphériques sur le plateau tibétain pour vérifier et évaluer ces résultats des données de réanalyse de NCEP-I et ERA-40, respectivement. Nous trouvons que les résultats de ERA-40 sont plus proches des observations en ce qui concerne la structure et la variation saisonnière du profil vertical du réchauffement diabatique sur le plateau tibétain. Les distributions horizontales de la source de chaleur apparente sont très raisonnables au-dessus de l'Asie, tant pour les résultats de NCEP-I que de ERA-40.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".