General Circulation Model Regional Predictions Using MOS techniques: Seasonal Forecasting
Notice bibliographique
Résumé
Ocean and atmospheric Coupled Global Climate Models (CGCMs) have been widely used to provide more accurate and coherent seasonal forecasts. However, they still show some limitations. Model Output Statistics (MOS) approaches may improve performance if observed and forecast values are available for a long record. This study investigates the skills of a MOS approach on ECHAM4p5 in simulating rainfall and temperature on a seasonal time scale over the South West (SW) Ontario region. ECHAM4p5 model has 20 ensemble members and those 20 members along with their mean are compared with real time observational data collected locally by Environment and Climate Change Canada (ECCC) weather stations. Presently, the ECHAM4p5 model is run by the Foundation Cearense for Meteorology and Water Management (FUNCEME), Brazil. The model is run at the beginning of every month based on persisted Sea Surface Temperature (SST) from 0000 of 1st day of that month. An ensemble average of 20 realizations is used for the forecasts. The model uses for eight-month weather predictions ahead of the start date. \nHistorical model data were available from International Research Institute for Climate and Society (IRI), Columbia University and used together with Global Precipitation Climatology Centre (GPCC) rainfall data and average daily temperature obtained from the Climatic Research Unit (CRU) at University of East Anglia. Ten years of daily forecasts for SW Ontario from the ECHAM4p5 model are used to develop Regional Correction Factors (RCF) to help in improving the model seasonal forecast confidence level. The basic (bias correction based) MOS technique is applied for seasonal and regional bias corrections. Our focus has been on the first three months of the forecast and comparisons are made against Meteorological Terminal Air Report (METAR) and other data for SW Ontario. The comparison of tuned data and observations has been made over SW Ontario. The motivation of taking this domain is that our industrial partner is mainly working with the farmers in SW Ontario. The approach used had given encouraging results (based on personal communication) in larger geographic areas and improved seasonal predictions in Pakistan. The results so far in the much smaller SW Ontario domain, with a somewhat different climatology, have not been as successful but have provided ideas for future research. We have worked on both monthly and daily precipitation and temperature, but in particular we have focused to investigate day to day differences between different ensemble members to see what information might be gained from them. Our results show that there is huge variation among 20 ensemble members and those variations are canceled out while taking their mean for ensemble mean forecasting technique. Furthermore, while comparing individual ensemble members with observation data, we also get the idea that few ensemble members are following observation data closely. We also compare the same method for larger domain to improve our forecasting results.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».