Articles | Volume 15, issue 2
Geosci. Model Dev., 15, 493–507, 2022
https://doi.org/10.5194/gmd-15-493-2022
Geosci. Model Dev., 15, 493–507, 2022
https://doi.org/10.5194/gmd-15-493-2022
Model description paper
21 Jan 2022
Model description paper | 21 Jan 2022

Parameterization of the collision–coalescence process using series of basis functions: COLNETv1.0.0 model development using a machine learning approach

Camilo Fernando Rodríguez Genó and Léster Alfonso

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Short summary
The representation of the collision–coalescence process in models of different scales has been a great source of uncertainty for many years. The aim of this paper is to show that machine learning techniques can be a useful tool in order to incorporate this process by emulating the explicit treatment of microphysics. Our results show that the machine learning parameterization mimics the evolution of actual droplet size distributions very well.