强对流天气过程常伴随暴雨、强风、闪电、冰雹等灾害性天气，对人民群众生命和财产安全造成严重影响。多源对流观测数据和高分辨率数值天气预报模式是应对高影响天气的基本手段，而有效的资料同化技术则是提高强对流短临预报精度的关键一步。如何发展对流尺度资料同化技术，充分融合多源对流观测数据，是当前短临预报中的前沿科学问题。

闪电与强对流系统的动力-热力-微物理过程密切相关，可以准确指示强对流的发生和发展。近年来，大气所郄秀书团队自主发展了高精度闪电探测和三维定位系统，在多年外场观测实验的基础上，揭示华北地区雷暴天气特征和闪电发生发展规律（Qie et al.，2021；郄秀书等，2023），着力构建适用于短临预报的闪电资料同化方案（Qie et al., 2014; Chen et al., 2019，2020; Xiao et al. 2021a, b）。最近，郄秀书团队和美国国家大气研究中心NCAR高级科学家Juanzhen Sun博士合作开发的基于闪电资料同化方案（Chen et al., JGR-A, 2020）已被国际上广泛使用的中尺度模式WRF（Weather Research and Forecasting）官方采用（WRF-V4.6; https://github.com/wrf-model/WRF/releases），是首个针对闪电资料开发并具备业务化运行能力的闪电同化模块（da_lightning）。该方案由陈志雄博士（2020年大气所博士毕业，目前为福建师范大学副教授）作为第一作者开发，通过三维变分的方式有效引入闪电观测资料，改进模式初始场中的动力、热力和微物理场，可以形成更为平衡的热动力结构。在个例测试和汛期批量预报实验中，显著改进了对流性降水定量预报的准确性（Zhang et al., 2023）。该方案可同化地基和卫星平台的闪电观测资料，直接用于全球中尺度短临预报模式，为高影响天气的精准预报提供了新的支撑手段。

图1 闪电资料三维变分同化方案代码页面

图2 一次强对流过程中闪电同化方案的降水评分分布

WRF代码下载地址 https://github.com/wrf-model/WRF/releases

References：

Chen Z., Sun J., Qie X., Y. Zhang, Z. Ying, X. Xiao, et al., 2020: A method to update model kinematic states by assimilating satellite‐observed total lightning data to improve convective analysis and forecasting. J. Geophys. Res. Atmos., 125, JD033330. https://doi.org/10.1029/2020JD033330.

Chen, Z., Qie, X., Liu, D., Xiong, Y. (2019). Lightning data assimilation with comprehensively nudging water contents at cloud-resolving scale using WRF model. Atmospheric Research, 22, 72–87. https://doi.org/10.1016/j.atmosres.2019.02.001

Qie, X., Zhu, R., Yuan, T., X. Wu, W. Li, D. Liu. 2014. Application of total lightning data assimilation in a mesoscale convective system based on the WRF model. Atmospheric Research, 145, 255–266. https://doi.org/10.1016/j.atmosres.2014.04.012

Qie, X., S. Yuan, Z. Chen, D. Wang, D. Liu, M. Sun, et al., 2021. Understanding the dynamical–microphysical and lightning processes associated with severe thunderstorms over the Beijing metropolitan region. Science China Earth Sciences, 64(1):10-26. https://doi.org/10.1007/s11430-020-9656-8

郄秀书, 张义军，张大林, 银燕，余晔，陆高鹏，蒋如斌, 2023. 雷电天气系统原理和预报. 科学出版社, 北京

Xiao X., Sun J., Qie X., Z. Ying, L. Ji, M. Chen, et al., 2021. Lightning data assimilation scheme in a 4DVAR system and its impact on very-short-term convective forecasting. Mon. Weather Rev., 149(2): 353-373. https://doi.org/10.1175/MWR-D-19-0396.1

Xiao X., Qie X., Chen Z., J. Lu, L. Ji, D. Wang, L. Zhang, M. Chen, and M. Chen, 2021: Evaluating the performance of lightning data assimilation from BLNET observations in a 4DVAR-based weather nowcasting model for a high-impact weather over Beijing. Remote Sens., 13(11), 2084. https://doi.org/10.3390/rs13112084

Zhang, Y., Chen, Z., Xiao, X., Qie, X., Chen, M., J. Lu, D. Wang, S. Yuan, H. Lyu, J. Feng, S. Fan, and D. Liu. 2023. Combined assimilation of radar and lightning data for the short-term forecast of severe convection system, Atmospheric Research, 283, 106562. https://doi.org/10.1016/j.atmosres.2022.106562