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Parameter estimation using an evolutionary algorithm for QPF in a tropical cyclone

Title
Parameter estimation using an evolutionary algorithm for QPF in a tropical cyclone
Authors
Yu X.Park S.K.Lee Y.H.
Ewha Authors
박선기
SCOPUS Author ID
박선기scopus
Issue Date
2013
Journal Title
Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II)
Publisher
Springer Berlin Heidelberg
Indexed
SCOPUS scopus
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 (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. © Springer-Verlag Berlin Heidelberg 2013.
DOI
10.1007/978-3-642-35088-7_27
ISBN
9783642350887; 9783642350870
Appears in Collections:
엘텍공과대학 > 환경공학전공 > Journal papers
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