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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Cited articles

Alfonso, L. and Zamora, J. M.: A two-moment machine learning parameterization of the autoconversion process, Atmos. Res., 249, 105269, https://doi.org/10.1016/j.atmosres.2020.105269, 2021. 
Alfonso, L., Raga, G. B., and Baumgardner, D.: The validity of the kinetic collection equation revisited, Atmos. Chem. Phys., 8, 969–982, https://doi.org/10.5194/acp-8-969-2008, 2008. 
Alfonso, L., Raga, G., and Baumgardner, D.: A Monte Carlo framework to simulate multicomponent droplet growth by stochastic coalescence, in: Applications of Monte Carlo Method in Science and Engineering, edited by: Mordechai, S., InTech., https://doi.org/10.5772/14522, 2011. 
Barros, A. P., Prat, O. P., Shrestha, P., Testik, F. Y., and Bliven, L. F.: Revisiting Low and List (1982): Evaluation of raindrop collision parameterizations using laboratory observations and modeling, J. Atmos. Sci., 65, 2983–2993, https://doi.org/10.1175/2008JAS2630.1, 2008. 
Berry, E. X.: Cloud droplet growth by collection, J. Atmos. Sci., 24, 688–701, https://doi.org/10.1175/1520-0469(1967)024<0688:CDGBC>2.0.CO;2, 1967. 
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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.