Technical note: Parameterising cloud base updraft velocity of marine stratocumuli

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http://urn.fi/URN:NBN:fi-fe2022050332143 http://hdl.handle.net/10138/343388
Titel: Technical note: Parameterising cloud base updraft velocity of marine stratocumuli
Författare: Ahola, Jaakko; Raatikainen, Tomi; Alper, Muzaffer Ege; Keskinen, Jukka-Pekka; Kokkola, Harri; Kukkurainen, Antti; Lipponen, Antti; Liu, Jia; Nordling, Kalle; Partanen, Antti-Ilari; Romakkaniemi, Sami; Räisänen, Petri; Tonttila, Juha; Korhonen, Hannele
Upphovmannens organisation: Ilmatieteen laitos
Finnish Meteorological Institute
Utgivare: Copernicus Publ.
Datum: 2022
Språk: en
Tillhör serie: Atmospheric chemistry and physics
ISSN: 1680-7316
1680-7324
DOI: https://doi.org/10.5194/acp-22-4523-2022
Permanenta länken (URI): http://urn.fi/URN:NBN:fi-fe2022050332143
http://hdl.handle.net/10138/343388
Abstrakt: 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.
Subject: cloud droplets
machine learning methods
Licens: CC BY 4.0


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