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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 observation 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.

The bright band, a layer of enhanced radar reflectivity associated with melting ice particles, is a major source of significant overestimation in quantitative precipitation estimation (QPE) based on the Z–R (reflectivity factor–rain rate) relationship. The effects of the bright band on radar-based QPE can be eliminated by vertical profile of reflectivity (VPR) correction. In this study, we applied bright-band correction algorithms to evaluate three different bands (S-, C- and X-band) of dual-polarized radars and to reduce overestimation errors in Z–R relationship–based QPEs. After the reflectivity was corrected by the algorithms using average VPR (AVPR) alone and a combination of average VPR and the vertical profile of the copolar correlation coefficient (AVPR+CC), the QPEs were derived. The bright-band correction and resulting QPEs were evaluated in eight precipitation events by comparing to the uncorrected reflectivity and rain-gauge observations, separately. The overestimation of Z–R relationship–based QPEs associated with the bright band was reduced after correction by the two schemes for which hourly rainfall was less than 5 mm. For the verification metrics of RMSE (root-mean-square error), RMAE (relative mean absolute error) and RMB (relative mean bias) of QPEs, averaged over all eight cases, the AVPR method improved from 2.28, 0.94 and 0.78 to 1.55, 0.60 and 0.40, respectively, while the AVPR+CC method improved to 1.44, 0.55 and 0.30, respectively. The QPEs after AVPR+CC correction had less overestimation than those after AVPR correction, and similar conclusions were drawn for all three different bands of dual-polarized radars.

Two intense quasi-linear mesoscale convective systems (QLMCSs) in northern China were simulated using the WRF (Weather Research and Forecasting) model and the 3D-Var (three-dimensional variational) analysis system of the ARPS (Advanced Regional Prediction System) model. A new method in which the lightning density is calculated using both the precipitation and non-precipitation ice mass was developed to reveal the relationship between the lightning activities and QLMCS structures. Results indicate that, compared with calculating the results using two previous methods, the lightning density calculated using the new method presented in this study is in better accordance with observations. Based on the calculated lightning densities using the new method, it was found that most lightning activity was initiated on the right side and at the front of the QLMCSs, where the surface wind field converged intensely. The CAPE was much stronger ahead of the southeastward progressing QLMCS than to the back it, and their lightning events mainly occurred in regions with a large gradient of CAPE. Comparisons between lightning and non-lightning regions indicated that lightning regions featured more intense ascending motion than non-lightning regions; the vertical ranges of maximum reflectivity between lightning and non-lightning regions were very different; and the ice mixing ratio featured no significant differences between the lightning and non-lightning regions.

The variation of air temperature measurement errors using two different radiation shields (DTR502B Vaisala, Finland, and HYTFZ01, Huayun Tongda Satcom, China) was studied. Datasets were collected in the field at the Daxing weather station in Beijing from June 2011 to May 2012. Most air temperature values obtained with these two commonly used radiation shields were lower than the reference records obtained with the new Fiber Reinforced Polymers (FRP) Stevenson screen. In most cases, the air temperature errors when using the two devices were smaller on overcast and rainy days than on sunny days; and smaller when using the imported rather than the Chinese shield. The measured errors changed sharply at sunrise and sunset, and reached maxima at noon. Their diurnal variation characteristics were, naturally, related to changes in solar radiation. The relationships between the record errors, global radiation, and wind speed were nonlinear. An improved correction method was proposed based on the approach described by Nakamura and Mahrt (2005) (NM05), in which the impact of the solar zenith angle (SZA) on the temperature error is considered and extreme errors due to changes in SZA can be corrected effectively. Measurement errors were reduced significantly after correction by either method for both shields. The error reduction rate using the improved correction method for the Chinese and imported shields were 3.3% and 40.4% higher than those using the NM05 method, respectively.

The Nested Air Quality Prediction Model System (NAQPMS) was used to investigate the temporal and spatial variations of PM 2.5 over tropospheric central eastern China in January 2013. The impact of regional transport and its implications on pollution prevention and control were also examined. Comparison between simulated and observed PM 2.5 showed NAQPMS was able to reproduce the evolution of PM 2.5 during heavy haze episodes. The results indicated that regional transport of PM 2.5 played an important role in regional haze episodes in the city cluster including Hebei, Beijing and Tianjin (HBT). The cross-city clusters transport outside HBT and transport among cities inside HBT contributed 20%-35% and 26%-35% of PM 2.5 as compared with local emission, in HBT respectively. To meet the Air Quality Standards for Grade Ⅱ, 90%, 90% and 65% of emissions would have to be cut down in Hebei, Tianjin and Beijing, if non-control strategy was taken in the surrounding city clusters of HBT. This implicated that control of emissions in one city cluster is not sufficient to reduce regional haze events, and joint efforts among city clusters are essential. Besides regional transports, two-way feedback between boundary-layer evolution and PM 2.5 also significantly contributed to the formation of heavy hazes, which contributed 30% of monthly average PM 2.5 concentration in HBT.