Correcting coarse-grid weather and climate models by machine learning from global storm-resolving simulations

Autor:	Jeremy McGibbon, Brian Henn, W. Andre Perkins, Oliver Watt-Meyer, Noah D. Brenowitz, Lucas M. Harris, Spencer K. Clark, Christopher S. Bretherton, Anna Kwa
Rok vydání:	2021
Předmět:	Physics::Fluid Dynamics Cumulus convection Meteorology Flow (psychology) Environmental science Terrain Storm Weather and climate Grid Reference model Physics::Atmospheric and Oceanic Physics
DOI:	10.1002/essoar.10507879.1
Popis:	Global atmospheric `storm-resolving' models with horizontal grid spacing of less than 5~km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that coul...
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::437e959904b14af7349cf0bb91397c1f https://doi.org/10.1002/essoar.10507879.1 Zobrazit plný text záznamu