Proxy for accumulation mode particle concentrations using machine learning and reanalysis data

0561542 - ÚCHP 2023 GR eng A - Abstrakt
Ovaska, A. - Rauth, E. - Holmberg, D. - Bergmans, B. - Collins, D. - Ding, A. - Franco, M.A. - Gani, S. - Hussein, T. - Hyvärinen, A. - Leaitch, R. - Mihalopoulos, N. - O´Dowd, C. - Sporre, M. - Tunved, P. - Ulevicius, V. - Wiedensohler, A. - Ždímal, Vladimír - Makkonen, R. - Puolamäki, K. - Nieminen, T. - Paasonen, P.
Proxy for accumulation mode particle concentrations using machine learning and reanalysis data.
Abstract Book. Athens, 2022. s. 1329, č. článku ATAS-eP3_007..
[International Aerosol Conference IAC 2022. 04.09.2022-09.09.2022, Athens]
Grant ostatní: AF(FI) 337549; AF(FI) 311932; ERDF(XE) UIA03-240
Institucionální podpora: RVO:67985858
Klíčová slova: aerosol-cloud interactions * cloud condensation nuclei * machine learning
Obor OECD: Meteorology and atmospheric sciences
https://iac2022.gr/abstracts/

In this study we develop a novel approach which uses machine learning methods combined with in-situ measurements and modelled
aerosol precursors collected from a reanalysis dataset. The aim is to produce a model that can predict CCN concentrations in continental areas. The results can be used to validate global models.

Trvalý link: https://hdl.handle.net/11104/0334130