Abstract
Abstract. 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 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 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.
Highlights
For numerical weather forecasting to be accurate, a numerical model should be able to represent real atmospheric conditions in terms of dynamics, physics, and numerics
The uncertainties related to the subgrid-scale parameterizations significantly increase as numerical weather predictions (NWPs) models become more complex
The accuracy of subgrid-scale parameterizations depends on both parameters in the physics schemes and the choice of the parameterization schemes for each corresponding physical process
Summary
For numerical weather forecasting to be accurate, a numerical model should be able to represent real atmospheric conditions in terms of dynamics (i.e., governing equations), physics (i.e., parameterizations), and numerics (e.g., resolution and coordinate system). One of the biggest uncertainties in numerical weather predictions (NWPs) comes from treating the subgrid-scale physical processes that have not been sufficiently understood. The subgrid-scale physical processes are parameterized in NWP models through empirical evidence, such as the derived value from observations and/or theoretical backgrounds. Jamil and Yang (2013) reviewed and compiled benchmark functions found from all the available literature for global optimization problems. They focused on the diverse properties of objective functions such as continuity, linearity, modality, separability, and dimensionality. It is important to choose an algorithm that can handle these properties
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