Journal of Meteorological Research

, Volume 31, Issue 4, pp 731–746 | Cite as

Assimilation of total lightning data using the three-dimensional variational method at convection-allowing resolution

  • Rong Zhang
  • Yijun Zhang
  • Liangtao Xu
  • Dong Zheng
  • Wen Yao


A large number of observational analyses have shown that lightning data can be used to indicate areas of deep convection. It is important to assimilate observed lightning data into numerical models, so that more small-scale information can be incorporated to improve the quality of the initial condition and the subsequent forecasts. In this study, the empirical relationship between flash rate, water vapor mixing ratio, and graupel mixing ratio was used to adjust the model relative humidity, which was then assimilated by using the three-dimensional variational data assimilation system of the Weather Research and Forecasting model in cycling mode at 10-min intervals. To find the appropriate assimilation time-window length that yielded significant improvement in both the initial conditions and subsequent forecasts, four experiments with different assimilation time-window lengths were conducted for a squall line case that occurred on 10 July 2007 in North China. It was found that 60 min was the appropriate assimilation time-window length for this case, and longer assimilation window length was unnecessary since no further improvement was present. Forecasts of 1-h accumulated precipitation during the assimilation period and the subsequent 3-h accumulated precipitation were significantly improved compared with the control experiment without lightning data assimilation. The simulated reflectivity was optimal after 30 min of the forecast, it remained optimal during the following 42 min, and the positive effect from lightning data assimilation began to diminish after 72 min of the forecast. Overall, the improvement from lightning data assimilation can be maintained for about 3 h.

Key words

lightning data assimilation three-dimensional variational (3DVAR) method Wether Research and Forecasting (WRF) model 


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Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Rong Zhang
    • 1
    • 2
    • 3
  • Yijun Zhang
    • 1
    • 3
  • Liangtao Xu
    • 1
    • 3
  • Dong Zheng
    • 1
    • 3
  • Wen Yao
    • 1
    • 3
  1. 1.State Key Laboratory of Severe WeatherChinese Academy of Meteorological SciencesBeijingChina
  2. 2.College of Earth SciencesUniversity of Chinese Academy of SciencesBeijingChina
  3. 3.Laboratory of Lightning Physics and Protection EngineeringChinese Academy of Meteorological SciencesBeijingChina

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