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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[1] In this study, the performance of inverse threedimensional variational assimilation (I3D-Var) is investigated in terms of dissipation process for an advection-diffusion problem. The performance of I3D-Var becomes poorer with larger diffusion coefficients. However, even for strong dissipation, the cost function during early iterations in the I3D-Var decreases still much faster than it does in the standard four-dimensional variational assimilation (4D-Var). Based on this observation a hybrid approach that combines the I3D-Var and the 4D-Var is suggested to accelerate the performance of 4D-Var. Application of this hybrid method demonstrates that the I3D-Var can serve as a preconditioner for carrying minimization in the full 4D-Var framework. Using the initial conditions obtained through the I3D-Var, the 4D-Var showed much faster convergence in minimizing the cost function.

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