Work

Machine learning in TerraDT: from emulators to impact models for the cryosphere and land surface

Year: 2026

Type: article

Abstract: This file covers all the presentations held on 17th September 2026, during the TerraDT webinar: "Machine learning in TerraDT: from emulators to impact models for the cryosphere and land surface". More details and the agenda are available on the website: https://terradt.eu/events/machine-learning-terradt-emulators-impact-models-cryosphere-and-land-surface

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.24014034

Authors: Devaraju, Narayanappa, Mozaffari, Amirpasha, Castaño Ishizaka, Marina, MATERIA, Stefano, Rodenberg, Benjamin, Dueben, Peter, Giuffrida, Maria

Institutions: Commissariat à l'Énergie Atomique et aux Énergies Alternatives, CEA Paris-Saclay - Etablissement de Saclay, CEA Paris-Saclay, CSC - IT Center for Science (Finland), Barcelona Supercomputing Center, European Centre for Medium-Range Weather Forecasts, University of Oxford

Language: English

Topic: Cryospheric studies and observations

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: green

OpenAlex ID: W7225423871

FWCI:

Cites:

Cited by:

Related to: 10