Quantitative Precipitation Forecast of a Tropical Cyclone through Optimal Parameter Estimation in a Convective Parameterization

Abstract

This study focuses on improving quantitative precipitation forecast (QPF) related to a tropical cyclone by optimal estimation of two parameters of the Kain-Fritsch convective parameterization scheme in a high-resolution regional model - the Weather Research and Forecasting (WRF). The micro-genetic algorithm (GA) is employed for optimization, and a QPF skill score is used as a fitness function. The target parameters include the autoconversion rate (c) and the convective time scale (T_c). An interface between the micro-GA and WRF is developed and applied to an extreme heavy rainfall case in Korea, related to Typhoon Rusa (2002), at a grid spacing of 10 km. To produce the best QPF skill for this tropical cyclone case, the default parameter values are adjusted by significant amount. Our results indicate that the micro-GA is effective to retrieve the optimal parameter values, which are especially important in improving forecast skill of heavy rainfall events.