Parameter Estimation Using an Evolutionary Algorithm for QPF in a Tropical Cyclone

Abstract

In this study the quantitative precipitation forecast (QPF) related to a tropical cyclone is performed using a high-resolution mesoscale model and an evolutionary algorithm. For this purpose two parameters of the Kain-Fritsch convective parameterization scheme, in the Weather Research and Forecasting (WRF) model, are optimized using the micro-genetic algorithm (GA). The auto-conversion rate (c) and the convective time scale (T c ) are target parameters. The fitness function is based on a QPF skill score. Typhoon Rusa (2002) is simulated in a grid spacing of 25 km. The default value of c is 0. 03 s− 1 while that of T c is limited to a range between 1800 s and 3600 s as a function of grid resolution. To produce the best QPF skill, at least for this tropical cyclone case, c is optimized to 0. 0004 s− and T c to 1922s. Our results indicate that parameters of subgrid-scale physical processes need to be adjusted to produce better QPF in a tropical cyclone, sometimes to values far different from the default values in a numerical model. Such adjustment may be dependent on the characteristics of weather systems as well as geographical locations.