A method to update model kinematic states by assimilating satellite�observed total lightning data to improve convective analysis and forecasting

This study assesses the benefit of convective-scale data assimilation (DA) for model initialization using well-known functional relationships between lightning flash rate and vertical velocity. Based on the relationships, a lightning DA scheme to update model kinematic states was implemented 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 through a DA scheme. With the availability of space-borne lightning imagers in recent years, total lightning 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 assimilation of LMI data provides added benefits to the assimilation of radar radial winds by reducing wind errors and strengthening convergence along the squall line in the analysis. Although the microphysical states are identical due to the assimilation of reflectivity, the lightning DA scheme helps in promoting updraft developments at lightning observation locations, which improves the representation of mixed-phase convection. The quantitative verification of short-term convective forecasts indicated that the lightning DA adds value to the radar DA by improving the precipitation forecast skill. The lightning DA scheme was evaluated further for a heavy rainfall case in 2018 over the Beijing metropolitan area and revealed overall similar forecast improvements for composite reflectivity and accumulated rainfall.