620 Forecasting Severe Convective Storms with WRF-Based RTFDDA Radar Data Assimilation in Shenzhen, China

Tuesday, 9 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Yongjie Huang, NCAR, Boulder, CO; and Y. Liu, M. Xu, Y. Liu, L. Pan, H. Wang, W. Y. Y. Cheng, Y. Jiang, X. Mao, H. Yang, R. Zong, C. Cao, and X. Wei

Weather radar measures detailed wind and microphysical properties of convective storms, and radar data assimilation is a very valuable measure for short-term convection forecasting or nowcasting. There are several methods to assimilate radar observations into a mesoscale model, including 3DVAR, 4DVAR, EnKF, and hybrid approaches. One of the approaches is hydrometeor and latent heat nudging (HLHN). An analysis nudging (Newtonian relaxation) based HLHN technique was developed to effectively assimilate radar reflectivity data in a WRF-based real-time four-dimensional data assimilation and short-term forecasting system (RTFDDA), developed at NCAR, to improve the short-term quantitative precipitation forecasts (QPFs). The purpose of this study is to investigate the performance of this assimilation scheme for its rapid-cycling forecasting applications to Shenzhen, a sub-tropical coastal metropolis in the southern China. Firstly, sensitivity of nudging parameters (nudging time window and nudging coefficients) of the RTFDDA-RDA scheme is studied with observing system simulation experiments (OSSEs). The OSSE study helps to choose the adequate RDA nudging parameters. Then the RTFDDA-RDA system is run to produce hindcasts for five severe convective storm events collected in the Shenzhen region during the 2017 raining season. Results show that, the RTFDDA-RDA system is able to analyze certain mesoscale and convective-scale features in a good accuracy and improve the short-term precipitation forecasting of convective storm through nudging cloud hydrometeors retrieved from radar reflectivity and latent heat. Subjective and statistical verification results demonstrated that RTFDDA-RDA presents a reasonable capability for forecasting convective systems by improving the initial conditions and then improve skills on QPFs, especially for the 0 - 3h nowcasting range.
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