Aerosol and terrain effects on winter cloud and precipitation over New York State
Notice bibliographique
Résumé
Prediction of regional weather and climate regarding precipitation for a region such as the Northeast US with its complex terrain and aerosol environment constitutes a major challenge. Furthermore, complex thermodynamic structures occur in and around New York State (NYS) owing to its complex topography paired with its coastal regions, ultimately affecting cloud and precipitation microphysics. The microphysical processes within the weather systems that produce orographic precipitation are not fully understood. These processes include, but are not limited to, liquid-ice interactions, ice growth through vapor deposition, riming, and aggregation, cloud-aerosol interactions, and melting and refreezing upon sedimentation to the surface. These processes vary with terrain height and width, wind speed, and the thermodynamic profile. And their accurate representation is necessary for effective microphysical modeling and precipitation prediction. Therefore, to better understand a) the impacts of aerosols on winter mixed-phase cloud properties and precipitation, as well as b) the effects of the complex underling terrain over NYS on the aerosol-cloud-precipitation interaction, a winter precipitation event was simulated using Weather Research and Forecasting (WRF) model coupled with a Spectral Bin Microphysical (SBM) scheme. Detailed ice nucleation parameterizations directly linking ice formation with ice nuclei (IN) have been implemented in the SBM model. Mixing ratios of several aerosol species from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provide the aerosol initial and boundary condition. On January 6th, 2014, a synoptic frontal system passed through NYS, and was followed by a lake effect event that lasted to January 7th. With a focus of aerosol indirect effects over the complex terrain of NYS, four sensitivity studies on this event were conducted: 1) a Control run where the ice nuclei (IN) and cloud condensate nuclei (CCN) concentration fields were derived from MERRA2 reanalysis, 2) a Clean run with the IN concentration, which was relatively high during the event, decreased to a third, and 3) a CCN run with tripled CCN number than the Clean run, and 4) a Polluted run where the CCN concentration was tripled than the Control run. In the frontal system, the ice-to-liquid partition within the cloud was strongly affected by extra CCN and IN. Particularly, extra CCN contributed to the formation of more numerous but smaller droplets and a higher liquid partition throughout the cloud, promoting the formation of ice crystals especially under high IN concentration condition, and contributed to the riming growth rate of graupels and snow aggregates, resulting in higher surface precipitation. Whereas adding IN to the system largely activated the ice-microphysical processes and promoted cloud glaciation. With higher snow formation and growth rate, snow melting buffered the loss of liquid droplets within the cloud and resulted in increased surface rainfall. For the lake effect cloud event, CCN were found to promote the formation of cloud droplets, which contributed to the riming rate of snow when the updraft in cloud was relatively strong. Extra CCN resulted in higher hydrometer content in cloud and consequently higher snowfall. Whereas IN largely increased the glaciation level of cloud as well as the riming and aggregation growth rate, invigorating the vertical motion, resulting in more precipitation that advected further downwind. Those effects were more significant when the background loading of the other type of nuclei was high. To address the effects of the terrain on winter precipitation, for the synoptic event, three sensitivity studies were designed with different model and terrain resolution. It was found that the large wind speed in coarse resolution simulation brought more vapor and cloud droplets into the domain. However, the warm temperature and inability to fully capture the gravity wave cloud resulted in weaker engagement of the frozen hydrometers, less efficient precipitation formation and therefore less precipitation. Whereas the Smooth terrain allowed more hydrometers in and efficiently translated those into surface precipitation. For the lake effect event, Snow cover over the shores of Lake Ontario intensified the surface convergence, though the effect was contoured by lower hydrometer content and riming growth rate within the cloud, resulted in lower averaged precipitation. Additionally, the convergence zone and cloud band were found to shift to the south when the northern shore was covered. The snow cover over the shores of Lake Ontario, especially the northern shore, played an important role in the amount and distribution of the lake effect snow.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 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 ».