Technical note: Parameterising cloud base updraft velocity of marine stratocumuli

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dc.contributor.author Ahola, Jaakko
dc.contributor.author Raatikainen, Tomi
dc.contributor.author Alper, Muzaffer Ege
dc.contributor.author Keskinen, Jukka-Pekka
dc.contributor.author Kokkola, Harri
dc.contributor.author Kukkurainen, Antti
dc.contributor.author Lipponen, Antti
dc.contributor.author Liu, Jia
dc.contributor.author Nordling, Kalle
dc.contributor.author Partanen, Antti-Ilari
dc.contributor.author Romakkaniemi, Sami
dc.contributor.author Räisänen, Petri
dc.contributor.author Tonttila, Juha
dc.contributor.author Korhonen, Hannele
dc.date.accessioned 2022-05-06T11:46:59Z
dc.date.available 2022-05-06T11:46:59Z
dc.date.issued 2022
dc.identifier.uri http://hdl.handle.net/10138/343388
dc.description.abstract The number of cloud droplets formed at the cloud base depends on both the properties of aerosol particles and the updraft velocity of an air parcel at the cloud base. As the spatial scale of updrafts is too small to be resolved in global atmospheric models, the updraft velocity is commonly parameterised based on the available turbulent kinetic energy. Here we present alternative methods through parameterising updraft velocity based on high-resolution large-eddy simulation (LES) runs in the case of marine stratocumulus clouds. First we use our simulations to assess the accuracy of a simple linear parameterisation where the updraft velocity depends only on cloud top radiative cooling. In addition, we present two different machine learning methods (Gaussian rocess emulation and random forest) that account for different boundary layer conditions and cloud properties. We conclude that both machine learning parameterisations reproduce the LES-based updraft velocities at about the same accuracy, while the simple approach employing radiative cooling only produces on average lower coefficient of determination and higher root mean square error values. Finally, we apply these machine learning methods to find the key parameters affecting cloud base updraft velocities.
dc.language.iso en
dc.publisher Copernicus Publ.
dc.relation.ispartofseries Atmospheric chemistry and physics
dc.rights CC BY 4.0
dc.subject cloud droplets
dc.subject machine learning methods
dc.title Technical note: Parameterising cloud base updraft velocity of marine stratocumuli
dc.format.volume 22
dc.format.issue 7
dc.identifier.urn URN:NBN:fi-fe2022050332143
dc.contributor.organization Ilmatieteen laitos fi
dc.contributor.organization Finnish Meteorological Institute en
dc.format.pagerange 4523-4537
dc.relation.doi 10.5194/acp-22-4523-2022
dc.relation.issn 1680-7316
dc.relation.issn 1680-7324
dc.type.okm A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.type.okm A1 Journal article (refereed), original research en

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