Parameter estimation using the genetic algorithm and its impact on quantitative precipitation forecast

Abstract

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.

Positive- and negative-feedback regulations coordinate the dynamic behavior of the Ras-Raf-MEK-ERK signal transduction pathway

“…However, a random search technique effectively probes the whole parameter space to minimize a given cost function (Khorasheh et al, 1999;Moles et al, 2003) and this includes genetic algorithms (Lee et al, 2006;Paterakis et al, 1998). In this study, we have developed a new parameter estimation method, a pseudo-random search algorithm (PRSA), as one of such random search techniques.…”

Assessing optimal set of implemented physical parameterization schemes in a multi-physics land surface model using genetic algorithm

“…These methods have been increasingly applied to parameter optimizations in various hydrological models (Bastani et al, 2010;Bulatewicz et al, 2009;Uddameri and Kuchanur, 2007) and to those in numerical weather predictions (Fang et al, 2009;Krishnakumar, 1989;Lee et al, 2006;Yu et al, 2013). Micro-GA applied to this study is an improved version of GA with smaller generation sizes and simplified genetic modifications, hence efficiently reducing the computational resources (Krishnakumar, 1989;Reeves, 1993;Wang et al, 2010).…”

Quantitative Precipitation Forecast of a Tropical Cyclone through Optimal Parameter Estimation in a Convective Parameterization

“…where H is the number of hits, F and O are the numbers of samples in which the precipitation amounts are greater than the specified threshold in forecast and observation, respectively, and R is the expected number of hits in a random forecast R = FO/N, where N is the total number of points being verified (Hamill 1999;Lee et al 2006;Yang and Tung 2003). Here ETS = 1 means a perfect forecast while ETS = 0 indicates that the forecast is same as a random forecast.…”

“…Implementation of a GA system does not require domain-specific (e.g., meteorological in this study) information for the coding and decoding process. Lee et al (2006) optimized both a physical and a computational parameter in MM5 using a standard GA to improve the quantitative precipitation forecast (QPF) in a heavy rainfall case in Korea. Our study will focus on optimal parameter estimation in a CP scheme to improve the QPF in the Weather Research and Forecasting (WRF) model using a micro-GA.…”