Machine learning and meteorological data for spatio-temporal prediction of tropospheric parameters

Crocetti, L., Soja, B., Klopotek, G., Awadaljeed, M., Rothacher, M., See, L. ORCID: https://orcid.org/0000-0002-2665-7065, Weinacker, R., Sturn, T., et al. (2022). Machine learning and meteorological data for spatio-temporal prediction of tropospheric parameters. In: EGU General Assembly 2022, 23-27 May 2022, Vienna.

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Official URL: https://www.egu22.eu/
Item Type: Conference or Workshop Item (Paper)
Research Programs: Advancing Systems Analysis (ASA)
Advancing Systems Analysis (ASA) > Novel Data Ecosystems for Sustainability (NODES)
Depositing User: Luke Kirwan
Date Deposited: 30 May 2022 10:59
Last Modified: 30 May 2022 10:59
URI: http://pure.iiasa.ac.at/18036

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