Work

Machine learning of cloud types in satellite observations and climate models

Year: 2022

Type: article

Abstract: Presentation given at the 13th Annual SeRC Meeting, Bro, Sweden on 13 May 2022.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.6551525

Authors: Kuma, Peter, Bender, Frida A.-M., Schuddeboom, Alex, McDonald, Adrian J., Seland, Øyvind

Institutions: Stockholm University, University of Canterbury, Norwegian Meteorological Institute

Language: English

Topic: Atmospheric aerosols and clouds

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: green

Funders: National Aeronautics and Space Administration, Swedish e-Science Research Centre, European Commission, Met Office, Goddard Space Flight Center, Langley Research Center, Horizon 2020 Framework Programme

Awards: Constrained aerosol forcing for improved climate projections, G3952839068, G434337789, G4629197022

OpenAlex ID: W4286780200

FWCI: 0.196

Cites:

Cited by: 2

Related to: 10