Title: Reducing Model Uncertainty in Physical Parameterizations: Combinational Optimizations Using Genetic Algorithm
Abstract: The numerical weather predictionNumerical weather prediction (NWP) skills are strongly affected by the uncertainties in parameterizations of subgrid-scale physical processes and by the undetermined parameters. Recently, various artificial intelligence algorithms have been used to reduce these uncertainties. In particular, the genetic algorithmGenetic Algorithm (GA) (GA), including micro-GA, has been extensively applied in hydrology and meteorology. This chapter introduces two recent studies on scheme- and parameter-based optimization using micro-GA. First, scheme-based optimization is conducted to find the optimal combination of four physical parameterization schemes associated with the sea breeze circulation, which includes the planetary boundary layerPlanetary boundary layer (PBL), land surface, shortwave radiation, and longwave radiation, in the Weather Research and ForecastingWeather Research and Forecasting (WRF) (WRF) model. Second, parameter-based optimization is performed to seek the optimal values of six parameters in the snow-related processes—snow cover fraction, snow albedoSnow albedo (SA), and snow depthSnow depth (SD)—of the Noah land surface model (LSM) for South KoreaSouth Korea (SK). In both applications, the optimal scheme combination and the optimally-estimated parameters, obtained through the coupled system of micro-GA and the WRFWeather Research and Forecasting (WRF) model or the Noah LSM improved the forecasting skill by reducing model uncertainties related to physical parameterizations.
Publication Year: 2023
Publication Date: 2023-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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