Geoscience Reference
In-Depth Information
Chapter 27
Parameter Estimation Using an Evolutionary
Algorithm for QPF in a Tropical Cyclone
Xing Yu, Seon Ki Park, and Yong Hee Lee
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 (
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
) and the convective time scale (
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
0:03
c
is optimized to
s
1
and
0:0004
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.
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