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

Tools

Crocetti, L., Soja, B., Klopotek, G., Awadaljeed, M., Rothacher, M., See, L. ORCID: https://orcid.org/0000-0002-2665-7065, Weinacker, R., Sturn, T., McCallum, I. ORCID: https://orcid.org/0000-0002-5812-9988, & Navarro, V. (2022). Machine learning and meteorological data for spatio-temporal prediction of tropospheric parameters. In: EGU General Assembly 2022, 23-27 May 2022, Vienna.

[thumbnail of 20220527_EGU22_Crocetti_MR.pptx] Slideshow
20220527_EGU22_Crocetti_MR.pptx - Published Version
Available under License Creative Commons Attribution.
Download (16MB)
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: https://pure.iiasa.ac.at/18036

Actions (login required)

View Item View Item
CORE (COnnecting REpositories)