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

Park, Sojung; Park, Seon K.

doi:https://doi.org/10.5194/gmd-14-6241-2021

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

Data sets

NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999 NCEP (National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce) https://doi.org/10.5065/D6M043C6

Model code and software

Genetic Algorithm and WRF model v4.0.3 Sojung Park and Seon Ki Park https://doi.org/10.5281/zenodo.5076930

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.