TY - JOUR
T1 - Technical note
T2 - Parameterising cloud base updraft velocity of marine stratocumuli
AU - Ahola, Jaakko
AU - Raatikainen, Tomi
AU - Alper, Muzaffer Ege
AU - Keskinen, Jukka Pekka
AU - Kokkola, Harri
AU - Kukkurainen, Antti
AU - Lipponen, Antti
AU - Liu, Jia
AU - Nordling, Kalle
AU - Partanen, Antti Ilari
AU - Romakkaniemi, Sami
AU - Räisänen, Petri
AU - Tonttila, Juha
AU - Korhonen, Hannele
N1 - Publisher Copyright:
© 2022 Jaakko Ahola et al.
PY - 2022/4/7
Y1 - 2022/4/7
N2 - 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 process 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.
AB - 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 process 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.
UR - http://www.scopus.com/inward/record.url?scp=85128296513&partnerID=8YFLogxK
U2 - 10.5194/acp-22-4523-2022
DO - 10.5194/acp-22-4523-2022
M3 - Article
SN - 1680-7316
VL - 22
SP - 4523
EP - 4537
JO - Atmospheric Chemistry and Physics
JF - Atmospheric Chemistry and Physics
IS - 7
ER -