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
- 주제(기타) Geosciences, Multidisciplinary
- 설명문(일반) [Park, Sojung; Park, Seon K.] Ewha Womans Univ, Dept Climate & Energy Syst Engn, Seoul 03760, South Korea; [Park, Seon K.] Ewha Womans Univ, Dept Environm Sci & Engn, Seoul 03760, South Korea; [Park, Seon K.] Ewha Womans Univ, Ctr Climate Environm Change Predict Res, Seoul 03760, South Korea; [Park, Seon K.] Ewha Womans Univ, Severe Storm Res Ctr, Seoul 03760, South Korea
- 등재 SCIE, SCOPUS
- OA유형 Green Submitted, gold
- 발행기관 COPERNICUS GESELLSCHAFT MBH
- 발행년도 2021
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000183721
- 본문언어 영어
- Published As http://dx.doi.org/10.5194/gmd-14-6241-2021
초록/요약
One of the biggest uncertainties in numerical weather predictions (NWPs) comes from treating the subgrid-scale physical processes. For more accurate regional weather and climate prediction by improving physics parameterizations, it is important to optimize a combination of physics schemes and unknown parameters in NWP models. We have developed an interface system between a microgenetic algorithm (mu-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 mu-GA successfully improved simulated precipitation despite the nonlinear 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 demonstrates that the combinatorial optimization of physics schemes in the WRF model is one possible solution to enhance the forecast skill of precipitation.
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