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Improvement of typhoon rainfall prediction based on optimization of the Kain-Fritsch convection parameterization scheme using a micro-genetic algorithm

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Abstract

Inclusion of cloud processes is essential for precipitation prediction with a numerical weather prediction model. However, convective parameterization contains numerous parameters whose values are in large uncertainties. In particular, it is still not clear how the parameters of a sub-grid-scale convection scheme can be modified to improve high-resolution precipitation prediction. To address these issues, a micro-genetic (micro-GA) algorithm is coupled to the Kain-Fritsch (KF) convective parameterization scheme (CPS) in the WRF to improve the quantitative precipitation forecast (QPF). The optimization focuses on two parameters in the KF scheme: the convective time scale and the conversion rate. The optimizing process is controlled by the micro-GA using a QPF skill score as the fitness function. Two heavy rainfall events related to typhoons that made landfall over the south-east coastal region of China are selected, and for each case the parameter values are adjusted to achieve the best QPF skill. Significant improvements in QPF are evident with an increase in the average equitable threat score (ETS) by 5.8% for the first case, and by 18.4% for the second case. The results demonstrate that the micro-GAKF coupling system is effective in optimizing the parameter values, which affect the applicability of CPS in a high-resolution model, and therefore improves the rainfall prediction in both ETS and spatial distribution.

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Acknowledgements

This study was financially supported by the National Basic Research Program of China (Grant No. 2015CB452806). The computation was supported by the ECNU Multifunctional Platform for Innovation (001).

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Authors and Affiliations

  1. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China

    Jia Zhu & Jiong Shu

  2. School of Geographic Sciences, East China Normal University, Shanghai, 200241, China

    Jia Zhu & Jiong Shu

  3. Beijing Thematic Technology Co., Ltd., Beijing, 100020, China

    Xing Yu

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  1. Jia Zhu
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  2. Jiong Shu
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Correspondence to Jiong Shu.

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Zhu, J., Shu, J. & Yu, X. Improvement of typhoon rainfall prediction based on optimization of the Kain-Fritsch convection parameterization scheme using a micro-genetic algorithm. Front. Earth Sci. 13, 721–732 (2019). https://doi.org/10.1007/s11707-019-0798-0

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