Could Machine Learning Break the Convection Parameterization Deadlock?

Representing unresolved moist convection in coarse‐scale climate models remains one of the main bottlenecks of current climate simulations. Many of the biases present with parameterized convection are strongly reduced when convection is explicitly resolved (i.e., in cloud resolving models at high sp...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Geophysical research letters 2018-06, Vol.45 (11), p.5742-5751
Hauptverfasser:	Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., Yacalis, G.
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial intelligence Artificial neural networks Atmospheric heating Atmospheric models Brain Climate models Climate prediction Clouds Computer simulation Convection Convection heating Convective heating ENVIRONMENTAL SCIENCES Heating Learning algorithms Machine learning MATHEMATICS AND COMPUTING Moist convection Neural networks Parameterization Representations Resolution Sea surface Sea surface temperature Spatial discrimination Spatial resolution Surface temperature Temperature (air-sea) Training Wetting
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!