The Dynamical-microphysical-electrical Processes in Severe Thunderstorms and Lightning Hazards(STORM973)project conducted coordinated comprehensive field observations of thunderstorms in the Beijing metropolitan region(BMR)during the warm season from 2014 to 2018.The aim of the project was to understand how dynamical,microphysical and electrical processes interact in severe thunderstorms in the BMR,and how to assimilate lightning data in numerical weather prediction models to improve severe thunderstorm forecasts.The platforms used in the field campaign included the Beijing Lightning Network(BLNET,consisting of 16 stations),2 X-band dual linear polarimetric Doppler radars,and 4 laser raindrop spectrometers.The collaboration also made use of the China Meteorological Administration's mesoscale meteorological ob-servation network in the Beijing-Tianjin-Hebei region.Although diverse thunderstorm types were documented,it was found that squall lines and multicell storms were the two major categories of severe thunderstorms with frequent lightning activity and extreme rainfall or unexpected local short-duration heavy rainfall resulting in inundations in the central urban area,influenced by the terrain and environmental conditions.The flash density maximums were found in eastern Changping District,central and eastern Shunyi District,and the central urban area of Beijing,suggesting that the urban heat island effect has a crucial role in the intensification of thunderstorms over Beijing.In addition,the flash rate associated with super thunderstorms can reach hundreds of flashes per minute in the central city regions.The super(5％of the total),strong(35％),and weak(60％)thunderstorms contributed about 37％,56％,and 7％to the total flashes in the BMR,respectively.Owing to the close connection between lightning activity and the thermodynamic and microphysical characteristics of the thunderstorms,the lightning flash rate can be used as an indicator of severe weather events,such as hail and short-duration heavy rainfall.Lightning data can also be assimilated into numerical weather prediction models to help improve the forecasting of severe convection and precipitation at the cloud-resolved scale,through adjusting or correcting the thermodynamic and microphysical parameters of the model.