Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-493-2022
© Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License.
Model description paper
| 
21 Jan 2022
Model description paper |
| 21 Jan 2022
| 21 Jan 2022
Parameterization of the collision–coalescence process using series of basis functions: COLNETv1.0.0 model development using a machine learning approach
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.
