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

Xing Yu, Seon Ki Park, Yong Hee Lee

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations

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.

Original languageEnglish
Title of host publicationData Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II)
PublisherSpringer Berlin Heidelberg
Pages707-715
Number of pages9
ISBN (Electronic)9783642350887
ISBN (Print)9783642350870
DOIs
StatePublished - 1 Jan 2013

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