Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 16
Articles | Volume 17, issue 16
https://doi.org/10.5194/gmd-17-6319-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-6319-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 16
Development and technical paper
 | 
27 Aug 2024
Development and technical paper |  | 27 Aug 2024

Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains

Tianning Su and Yunyan Zhang

Viewed

Total article views: 1,228 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
854 318 56 1,228 44 45
  • HTML: 854
  • PDF: 318
  • XML: 56
  • Total: 1,228
  • BibTeX: 44
  • EndNote: 45
Views and downloads (calculated since 11 Mar 2024)
Cumulative views and downloads (calculated since 11 Mar 2024)

Viewed (geographical distribution)

Total article views: 1,228 (including HTML, PDF, and XML) Thereof 1,227 with geography defined and 1 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
Download
Short summary
Using 2 decades of field observations over the Southern Great Plains, this study developed a deep-learning model to simulate the complex dynamics of boundary layer clouds. The deep-learning model can serve as the cloud parameterization within reanalysis frameworks, offering insights into improving the simulation of low clouds. By quantifying biases due to various meteorological factors and parameterizations, this deep-learning-driven approach helps bridge the observation–modeling divide.
Read more