Exploring the Cloud Top Phase Partitioning ...
Type de document :
Article dans une revue scientifique: Article original
DOI :
URL permanente :
Titre :
Exploring the Cloud Top Phase Partitioning in Different Cloud Types Using Active and Passive Satellite Sensors
Auteur(s) :
Bruno, Olimpia [Auteur]
Hoose, Corinna [Auteur]
Storelvmo, Trude [Auteur]
Coopman, Quentin [Auteur]
Stengel, Martin [Auteur]
Hoose, Corinna [Auteur]
Storelvmo, Trude [Auteur]
Coopman, Quentin [Auteur]
Stengel, Martin [Auteur]
Titre de la revue :
Geophysical Research Letters
Nom court de la revue :
Geophysical Research Letters
Numéro :
48
Éditeur :
American Geophysical Union (AGU)
Date de publication :
2021-01-18
Résumé en anglais : [en]
One of the largest uncertainties in numerical weather prediction and climate models is the representation of mixed‐phase clouds. With the aim of understanding how the supercooled liquid fraction (SLF) in clouds with ...
Lire la suite >One of the largest uncertainties in numerical weather prediction and climate models is the representation of mixed‐phase clouds. With the aim of understanding how the supercooled liquid fraction (SLF) in clouds with temperature from −40°C to 0°C is related to temperature, geographical location, and cloud type, our analysis contains a comparison of four satellite‐based datasets (one derived from active and three from passive satellite sensors), and focuses on SLF distribution near‐globally, but also stratified by latitude and continental/maritime regions. Despite the warm bias in cloud top temperature of the passive sensor compared to the active sensor and the phase mismatch in collocated data, all datasets indicate, at the same height‐level, an increase of SLF with cloud optical thickness, and generally larger SLF in the Southern Hemisphere than in the Northern Hemisphere (up to about 20% difference), with the exception of continental low‐level clouds, for which the opposite is true.Lire moins >
Lire la suite >One of the largest uncertainties in numerical weather prediction and climate models is the representation of mixed‐phase clouds. With the aim of understanding how the supercooled liquid fraction (SLF) in clouds with temperature from −40°C to 0°C is related to temperature, geographical location, and cloud type, our analysis contains a comparison of four satellite‐based datasets (one derived from active and three from passive satellite sensors), and focuses on SLF distribution near‐globally, but also stratified by latitude and continental/maritime regions. Despite the warm bias in cloud top temperature of the passive sensor compared to the active sensor and the phase mismatch in collocated data, all datasets indicate, at the same height‐level, an increase of SLF with cloud optical thickness, and generally larger SLF in the Southern Hemisphere than in the Northern Hemisphere (up to about 20% difference), with the exception of continental low‐level clouds, for which the opposite is true.Lire moins >
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
Université de Lille
CNRS
CNRS
Collections :
Équipe(s) de recherche :
Interactions Rayonnement Nuages (IRN)
Date de dépôt :
2024-01-09T17:34:23Z
Fichiers
- Geophysical Research Letters - 2020 - Bruno - Exploring the Cloud Top Phase Partitioning in Different Cloud Types Using.pdf
- Version éditeur
- Accès libre
- Main article
- Accéder au document