Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 10
Articles | Volume 14, issue 10
https://doi.org/10.5194/gmd-14-6241-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-6241-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 10
Development and technical paper
 | 
18 Oct 2021
Development and technical paper |  | 18 Oct 2021

A micro-genetic algorithm (GA v1.7.1a) for combinatorial optimization of physics parameterizations in the Weather Research and Forecasting model (v4.0.3) for quantitative precipitation forecast in Korea

Sojung Park and Seon K. Park

Viewed

Total article views: 3,161 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,012 1,080 69 3,161 47 51
  • HTML: 2,012
  • PDF: 1,080
  • XML: 69
  • Total: 3,161
  • BibTeX: 47
  • EndNote: 51
Views and downloads (calculated since 12 Jul 2021)
Cumulative views and downloads (calculated since 12 Jul 2021)

Viewed (geographical distribution)

Total article views: 3,161 (including HTML, PDF, and XML) Thereof 2,968 with geography defined and 193 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Nov 2024
Download
Short summary
One of the biggest uncertainties in numerical weather predictions (NWPs) comes from treating subgrid-scale physical processes. Physical processes, such as cumulus, microphysics, and planetary boundary layer processes, are parameterized in NWP models by empirical and theoretical backgrounds. We developed an interface between a micro-genetic algorithm and the WRF model for a combinatorial optimization of physics for heavy rainfall events in Korea. The system improved precipitation forecasts.
Read more