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Ann. Geophys., 24, 3185-3189, 2006
www.ann-geophys.net/24/3185/2006/
© European Geosciences Union 2006
Parameter estimation using the genetic algorithm and its impact on quantitative precipitation forecast
Y. H. Lee1, S. K. Park2, and D.-E. Chang1
1Meteorological Research Institute, Korea Meteorological Administration, Seoul, Republic of Korea
2Department of Environmental Science and Engineering, Ewha Womans University, Seoul, Republic of Korea
Abstract. In this study, optimal parameter estimations are performed for both
physical and computational parameters in a mesoscale meteorological
model, and their impacts on the quantitative precipitation
forecasting (QPF) are assessed for a heavy rainfall case occurred at
the Korean Peninsula in June 2005. Experiments are carried out using
the PSU/NCAR MM5 model and the genetic algorithm (GA) for two
parameters: the reduction rate of the convective available potential
energy in the Kain-Fritsch (KF) scheme for cumulus parameterization,
and the Asselin filter parameter for numerical stability. The
fitness function is defined based on a QPF skill score. It turns out
that each optimized parameter significantly improves the QPF skill.
Such improvement is maximized when the two optimized parameters are
used simultaneously. Our results indicate that optimizations of
computational parameters as well as physical parameters and their
adequate applications are essential in
improving model performance.
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