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−1 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.
|Title of host publication||Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II)|
|Publisher||Springer Berlin Heidelberg|
|Number of pages||9|
|State||Published - 1 Jan 2013|