- 1Institute of Atmospheric Physics, Key Laboratory of Middle Atmosphere and Global Environment Observation, Beijing, China (dishaoxuan22@mails.ucas.ac.cn)
- 2Institute of Atmospheric Physics, Key Laboratory of Middle Atmosphere and Global Environment Observation, Beijing, China (qiex@mail.iap.ac.cn)
- 3Chinese Academy of Meteorological Sciences, the Satellite Data Assimilation Division, Beijing, China (hanwei@cma.gov.cn)
Lightning can indicate the location of strong convection in thunderstorms. We develop a lightning data assimilation observational operator based on a 2D-to-3D Bayesian method, which converts the 2D lightning distribution into vertical velocity profiles and RH profiles for each grid point in the plane. The new lightning observational operator provides a good representation of the shape and peak height of the instantaneous vertical velocity profiles in thunderstorms, rather than using a fixed or long-term averaged profile distribution. After 1-hour forecasting, experiments that assimilated both vertical velocity and water vapor still maintained a close vertical distribution to the observations in the lower layers. It also shows significant improvement in heavy rainfall forecasting within 1 hour, with a notable increase in precipitation scores. The improvement in heavy rainfall prediction primarily lies in the positive adjustment of the location of intense rainfall and the enhancement of rainfall intensity.
How to cite: Shaoxuan, D., Xiushu, Q., and Wei, H.: Lightning Assimilation based on a 2D-to-3D Bayesian method for Vertical Velocity and Water Vapor, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11142, https://doi.org/10.5194/egusphere-egu25-11142, 2025.
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