Geoscientific Model Development
Geoscientific Model Development
Geoscientific Model Development
Articles | Volume 15, issue 2
Articles | Volume 15, issue 2
Geosci. Model Dev., 15, 493–507, 2022
https://doi.org/10.5194/gmd-15-493-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 15, 493–507, 2022
https://doi.org/10.5194/gmd-15-493-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 2
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

Viewed

Total article views: 897 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
611 250 36 897 14 9
  • HTML: 611
  • PDF: 250
  • XML: 36
  • Total: 897
  • BibTeX: 14
  • EndNote: 9
Views and downloads (calculated since 21 May 2021)
Cumulative views and downloads (calculated since 21 May 2021)

Viewed (geographical distribution)

Total article views: 847 (including HTML, PDF, and XML) Thereof 847 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 Jun 2022
Download
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.
Read more