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.

Surface ozone increased unexpectedly over northern China during the COVID-19 lockdown (CLD) period (23 January–29 February 2020), which was characterized by vigorous emission reduction. The reasons for this ozone enhancement have been speculated from perspectives of chemical responses to the emissions and meteorology. As known, the processes of natural stratospheric ozone injecting to the troposphere are most active in winter and spring. Yet, little attention was paid to stratospheric influences on this ozone enhancement. Here we report a stratospheric intrusion (SI) that reached the surface over northern China on 15–17 February during the CLD. The coevolution of enhanced ozone and sharply declined carbon monoxide and relative humidity (RH) was indicative of the SI occurrence. We show that the SI was facilitated by a cutoff low system that led to abnormally high surface ozone in most part of northern China. We estimate that over the SI period, the injected stratospheric ozone constituted up to 40–45% of the surface ozone over northern China. If the stratospheric ozone inputs were scaled over the entire CLD period, these inputs would account for 4–8% of the surface ozone. In view of the unexpected ozone increase during the CLD, this SI event could explain up to 18% of the ozone increase in some cities, and average 5–10% over larger areas that were affected. Hence, the nonnegligible stratospheric influences urge extra consideration of natural ozone sources in disentangling the role of emission reduction and meteorological conditions during the CLD in China and elsewhere in the world.

This study investigates the characteristics of space-borne Lightning Mapping Imager (LMI) lightning products and their relationships with cloud properties using ground-based total lightning observations from the Beijing Broadband Lightning Network (BLNET) and cloud information from S-band Doppler radar data. LMI showed generally consistent lightning spatial distributions with those of BLNET, and yielded a considerable lightning detection capability over regions with complex terrain. The ratios between the LMI events, groups and flashes were approximately 9:3:1, and the number of LMI-detected flashes was roughly one order of magnitude smaller than the number of BLNET-detected flashes. However, in different convective episodes, the LMI detection capability was likely to be affected by cloud properties, especially in strongly electrified convective episodes associated with frequent lightning discharging and thick cloud depth. As a result, LMI tended to detect lightning flashes located in weaker and shallower cloud portions associated with fewer cloud shielding effects. With reference to the BLNET total lightning data as the ground truth of observation (both intra-cloud lightning and cloud-to-ground lightning flashes), the LMI event-based detection efficiency (DE) was estimated to reach 28% under rational spatiotemporal matching criteria (1.5 s and 65 km) over Beijing. In terms of LMI flash-based DE, it was much reduced compared with event-based DE. The LMI flash-based ranged between 1.5% and 3.5% with 1.5 s and 35–65 km matching scales. For 330 ms and 35 km, the spatiotemporal matching criteria used to evaluate Geostationary Lightning Mapper (GLM), the LMI flash-based DE was smaller (<1%).

The Beijing Broadband Lightning Network (BLNET) was successfully set up in North China and had yielded a considerable detection capability of total lightning (intracloud and cloud to ground) over the regions with complex underlying (plains, mountains, and oceans). This study set up a basic framework for the operational application of assimilating total lightning activities from BLNET and assesses the potential benefits in cloud-scale, very short-term forecast (nowcasting) by modulating the vertical velocity using the 4DVar technique. Nowcast statistics aggregated over 11 cycles show that the nowcasting performances with the assimilation of BLNET lightning datasets outperform RAD and the assimilation of GLD360 (Global Lightning) datasets. The assimilation of BLNET data improves the model's dynamical states in the analysis by enhancing the convergence and updraft in and near the convective system. To better implement of assimilating real-time lightning data, this study also conducts sensitivity experiments to investigate the impact of the horizontal length scale of a distance-weighted interpolation, binning time intervals, and different vertical profile or distance weights prior to the DA. The results indicate that the best forecast performance for assimilating BLNET lightning datasets is obtained in a 4DVar cycle when the lightning accumulation interval is 3 min, the radius of horizontal interpolation is 5 × 5, and the statistically vertical velocity profile and the distance weights obtained from cumulus cloud.