Comparison of Environmental and Mesoscale Characteristics of Two Types of Mountain‐to‐Plain Precipitation Systems in the Beijing Region, China

Abstract

Beijing, China, is located in a region of complex terrain with high mountain ridges to the northwest and the Bohai Sea to the southeast. The origin of convective storms occurring on the plains can often be traced to the upstream mountains. Under weakly forced conditions, these convective storms most frequently evolve into squall lines (SL) and convective clusters (CC) when reaching the plains. In this study, we analyze 18 SL and 15 CC storm systems and assess their environmental and mesoscale differences between the two phenomena. By analyzing the frequency of convective occurrence for the two types of storms based on composite radar reflectivity, it is found that the high frequencies are located in the south of Beijing for SL and near the center of Beijing for CC. Using storm‐scale reanalysis data produced by a rapid update four‐dimensional variational analysis system that assimilates Doppler radar observations, distinct features of the SL and CC storms are revealed in terms of their convective environments and mesoscale structures, such as cold pool, horizontal wind convergence, and humidity distribution. It is found that low convective inhibition and high low‐level wind speed on the plains are common to both SL and CC, whereas higher vertical shear over the plains and stronger wind speed on both mountains and plains distinguishes SL from CC. We further show that the stronger wind and vertical shear in SL generate stronger and more organized downdrafts, producing a deeper cold pool, strong outflow and convergence, which explains the formation of the high‐frequency center in the south of Beijing. In contrast, the cold pool produced in CC is shallower and weaker, resulting in weaker outflow and convergence and convective activities that are located only in central Beijing.

VDRAS and Polarimetric Radar Investigation of a Bow Echo Formation After a Squall Line Merged With a Preline Convective Cell

“…VDRAS is a four‐dimensional variational data assimilation system that can assimilate radar reflectivity and Doppler velocities from single or multiple radars into a simplified cloud model to yield an optimal solution to satisfy both the model physics and the radar observations simultaneously (X Chen, Zhao, et al, 2016; Z He et al, 2018; Sun & Crook, 1997, 2001; Sun & Zhang, 2008; Sun et al, 2010; Xiao et al, 2019). It has been successfully implemented in the United States, China, Australia, and so forth, for analyses and short‐term forecasts of convective systems including SLs (e.g., Sun & Zhang, 2008; Z He et al, 2018) and supercells (e.g., Sun, 2005).…”

“…In addition, the sampling density and coverage of low‐level radar observations decrease as the distance from a radar increases. By assimilating surface observations, VDRAS can deduce the low‐level kinematic and thermodynamic structures, as well as the prominent features of these convective systems (e.g., X Chen, Zhao, et al, 2016; Sun & Zhang, 2008; Sun et al, 2010; Xiao et al, 2019). Similar results are confirmed in this case (not shown).…”

Joint influence of anomalous medium‐ and small‐scale circulations on short‐term heavy rainfall events over Beijing

“…For example, the characteristics of weakly forced mountain‐to‐plain precipitation systems were analysed based on radar observations and high‐resolution reanalysis data produced by VDRAS (Xiao et al ., 2017). Using storm‐scale reanalysis data produced by VDRAS, distinct features of the squall lines and convective clusters storms are revealed in terms of their convective environments and mesoscale structures, such as cold pool, horizontal wind convergence, and humidity distribution (Xiao et al ., 2019).…”

Effects of Local‐Scale Orography and Urban Heat Island on the Initiation of a Record‐Breaking Rainfall Event

“…In addition to the mechanism analysis, X. Xiao, et al. (2019) and Wu et al. (2020) investigated different data assimilation strategies for the initialization of convective‐scale numerical models and found that the assimilation of near‐surface temperature and the geostationary satellite data could improve forecasting skills for this event.…”