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Assimilation of Doppler Radar Data with an Ensemble 3DEnVar Approach to Improve Convective Forecasting
Advances in Atmospheric Sciences ( IF 5.8 ) Pub Date : 2020-12-23 , DOI: 10.1007/s00376-020-0081-z Shibo Gao , Haiqiu Yu , Chuanyou Ren , Limin Liu , Jinzhong Min
Advances in Atmospheric Sciences ( IF 5.8 ) Pub Date : 2020-12-23 , DOI: 10.1007/s00376-020-0081-z Shibo Gao , Haiqiu Yu , Chuanyou Ren , Limin Liu , Jinzhong Min
An ensemble three-dimensional ensemble-variational (3DEnVar) data assimilation (E3DA) system was developed within the Weather Research and Forecasting model’s 3DVar framework to assimilate radar data to improve convective forecasting. In this system, ensemble perturbations are updated by an ensemble of 3DEnVar and the ensemble forecasts are used to generate the flow-dependent background error covariance. The performance of the E3DA system was first evaluated against one experiment without radar DA and one radar DA experiment with 3DVar, using a severe storm case over southeastern China on 5 June 2009. Results indicated that E3DA improved the quantitative forecast skills of reflectivity and precipitation, as well as their spatial distributions in terms of both intensity and coverage over 3DVar. The root-mean-square error of radial velocity from 3DVar was reduced by E3DA, with stronger low-level wind closer to observation. It was also found that E3DA improved the wind, temperature and water vapor mixing ratio, with the lowest errors at the surface and upper levels. 3DVar showed moderate improvements in comparison with forecasts without radar DA. A diagnosis of the analysis revealed that E3DA increased vertical velocity, temperature, and humidity corresponding to the added reflectivity, while 3DVar failed to produce these adjustments, because of the lack of reasonable cross-variable correlations. The performance of E3DA was further verified using two convective cases over southern and southeastern China, and the reflectivity forecast skill was also improved over 3DVar. 本文在Weather Research and Forecasting model (WRF模式)的三维变分(3DVar)框架下,发展了Ensemble-3DEnVar(E3DA)同化系统来提高强对流天气的预报。E3DA系统通过一组集合3DEnVar对预报扰动进行更新,并根据集合预报结果计算流依赖性质的背景误差协方差。首先利用2009年6月5日发生在我国华东地区的一次强对流天气过程,与未同化和利用3DVar同化雷达资料的试验对比,检验了E3DA方法的同化和预报效果。结果表明:与3DVar方法相比,E3DA显著提高了模式对反射率和降水的定量预报技巧,以及对强对流天气系统的强度和范围等空间分布的预报。E3DA预报的较强低层径向速度与观测相近,减少了3DVar的均方根误差,同时还改善了对地面及上层的风、温度和水汽等变量的预报。同化结果的诊断表明:E3DA能够根据同化进背景场的反射率相应地调整垂直速度、温度和相对湿度,而3DVar由于缺少合理的交叉变量协方差未能对上述变量进行调整。另外,华东和华南的两次强对流天气过程进一步验证了E3DA方法在雷达资料同化上的有效性。
更新日期：2020-12-23
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