Case studies of atmospheric rivers over China and Australia: new insight into their rainfall generation
Notice bibliographique
Résumé
While the Australia–Asian (A-A) monsoon is a prominent feature of weather and climate in China and Australia, there are significant differences in their dominant weather patterns and climate drivers. In order to explore different characteristics of atmospheric rivers (ARs) affecting weather and climate in these two countries, this paper compares two typical AR events that occurred in the boreal summer (austral winter) in 2016. The event in China produced record-breaking rainfall in North China, whereas the event in Australia was accompanied by a classic Northwest Cloud Band (NWCB) and produced a rainfall belt across the continent. Using global reanalysis products and ground-based observational data, we analysed the synoptic backgrounds, vertical structures, water vapour sources and relationship between ARs and cloud distributions. In both China and Australia, heavy precipitation was triggered by strong water vapour transport by ARs ahead of midlatitude frontal systems. The main differences between these two AR events and their associated rainfall effectiveness were that (i) the AR intensity in the Asian summer monsoon was stronger than that in the austral winter season over Australia; (ii) the centre of AR maximum moisture transport in China was around 850 hPa, whereas in Australia, it was located at around 700 hPa; and (iii) the AR-induced rainfall was heavier in China than in Australia. These differences were caused by numerous factors, including a lack of topographic influence, a dry climate background in Australia, and different interactions between warm and moist air conveyed by ARs from the tropics with cold air from the midlatitudes. We paid particular attention to the relationship between the Australian AR and its associated cloud structure and rainfall to understand precipitation efficiency of the NWCB. In addition, we assessed the forecast skills of an Australian numerical weather prediction system (ACCESS-APS2) for the two events with different lead times. The model produced reasonable forecasts of the occurrence and intensity of both AR events several days in advance, and the AR forecast skill was better than its forecasts of rainfall location and intensity. This demonstrates the value of using AR analysis in guiding extreme rainfall forecasts with longer lead time.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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) | 0,000 | 0,000 |
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 tête enseignante, 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 ».