Preprints
https://doi.org/10.5194/gmd-2021-143
https://doi.org/10.5194/gmd-2021-143

Submitted as: development and technical paper 12 Jul 2021

Submitted as: development and technical paper | 12 Jul 2021

Review status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

A micro-genetic algorithm for combinatorial optimization of physics parameterizations in Weather Research and Forecasting model for quantitative precipitation forecast in Korea

Sojung Park1 and Seon K. Park1,2,3,4 Sojung Park and Seon K. Park
  • 1Department of Climate and Energy Systems Engineering, Ewha Womans University, Seoul, 03760, Korea
  • 2Department of Environmental Science and Engineering, Ewha Womans University, Seoul, 03760, Korea
  • 3Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, 03760, Korea
  • 4Severe Storm Research Center, Ewha Womans University, Seoul, 03760, Korea

Abstract. One of biggest uncertainties in Numerical Weather Predictions (NWPs) comes from treating the subgrid-scale physical processes. For the more accurate regional weather/climate prediction by improving physics parameterizations, it is important to optimize a combination of physics schemes as well as unknown parameters in NWP models. We have developed an interface system between micro-Genetic Algorithm (μ-GA) and the WRF model for the combinatorial optimization of CUmulus (CU), MicroPhysics (MP), and Planetary Boundary Layer (PBL) schemes in terms of quantitative precipitation forecast for heavy rainfall events in Korea. The μ-GA successfully improved simulated precipitation despite the non-linear relationship among the physics schemes. During the evolution process, MP schemes control grid-resolving scale precipitation while CU and PBL schemes determine subgrid-scale precipitation. This study has demonstrated the combinatorial optimization of physics schemes in the WRF model is one of possible solutions to enhance the forecast skill of precipitation.

Sojung Park and Seon K. Park

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2021-143', Astrid Kerkweg, 14 Jul 2021
    • AC1: 'Reply on CEC1', Sojung Park, 05 Aug 2021
  • RC1: 'Comment on gmd-2021-143', Anonymous Referee #1, 02 Sep 2021
    • AC2: 'Reply on RC1', Sojung Park, 11 Sep 2021
  • RC2: 'Comment on gmd-2021-143', Anonymous Referee #2, 07 Sep 2021
    • AC3: 'Reply on RC2', Sojung Park, 14 Sep 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2021-143', Astrid Kerkweg, 14 Jul 2021
    • AC1: 'Reply on CEC1', Sojung Park, 05 Aug 2021
  • RC1: 'Comment on gmd-2021-143', Anonymous Referee #1, 02 Sep 2021
    • AC2: 'Reply on RC1', Sojung Park, 11 Sep 2021
  • RC2: 'Comment on gmd-2021-143', Anonymous Referee #2, 07 Sep 2021
    • AC3: 'Reply on RC2', Sojung Park, 14 Sep 2021

Sojung Park and Seon K. Park

Data sets

NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999 NCEP https://rda.ucar.edu/datasets/ds083.2/

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

Sojung Park and Seon K. Park

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Short summary
One of biggest uncertainties in Numerical Weather Predictions (NWPs) comes from treating subgrid-scale physical processes. The 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 micro-Genetic Algorithm and WRF model for a combinatorial optimization of physics for heavy rainfall events in Korea. The system successfully improved precipitation forecast.