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A method to update model kinematic states by assimilating satellite-observed total lightning data to improve convective analysis and forecasting
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  • Zhixiong Chen,
  • Xiushu Qie,
  • Jenny Sun,
  • Ying Zhang,
  • Zhuming Ying,
  • Xian Xiao,
  • DongJie Cao
Zhixiong Chen
Institute of Atmospheric Physics, Chinese Academy of Science

Corresponding Author:chenzx@mail.iap.ac.cn

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Xiushu Qie
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, P. R. China
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Jenny Sun
National Center for Atmospheric Research (UCAR)
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Ying Zhang
National Center for Atmospheric Research (UCAR)
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Zhuming Ying
National Center for Atmospheric Research (UCAR)
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Xian Xiao
Institute of Urban Meteorology, China Meteorological Administration
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DongJie Cao
National Satellite Meteorological Center, Beijing, China
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The close connection between the total lightning flash rate and storm updraft has been well recognized. In this study, we assessed the benefit of such a relationship in convective-scale data assimilation (DA) for model initialization. A lightning DA scheme to update model kinematic states was developed in the Weather Research and Forecasting Data Assimilation (WRFDA) three-dimensional variational (3DVar) system. This scheme combines total lightning observations with model-based prescribed vertical velocity profiles to retrieve kinematic information useful to DA. With the availability of space-borne lightning imagers in recent years, total lightning data observations from the Lightning Mapping Imager (LMI) on board the FY-4A geostationary satellite were assimilated in combination with radar DA. A detailed analysis of the impact of the lightning DA scheme on convective precipitation forecasting was conducted using a squall line case over Beijing on 13 July 2017. The results showed that the assimilation of LMI data further improves the analyses of dynamical conditions from assimilating radar radial winds. Although the microphysical states are identical due to the assimilation of reflectivity, updrafts directly form at lightning observation locations via lightning DA and hence improve the convective-scale dynamical balance. The quantitative verification of short-term convective forecasts indicated that the lightning DA adds value to current radar DA by improving the precipitation forecast skill. The new lightning DA scheme was further applied to a heavy rainfall case in 2018, and the results confirmed the effective and robust improvement in storm forecasting.
27 Nov 2020Published in Journal of Geophysical Research: Atmospheres volume 125 issue 22. 10.1029/2020JD033330