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Understanding the characteristics of the raindrop size distribution (DSD) is crucial to improve our knowledge of the microphysical processes of precipitation and to improve the accuracy of radar quantitative precipitation estimation (QPE). In this study, the spatial variability of DSD in different regions of Beijing and its influence on radar QPE are analyzed using 11 disdrometers. The DSD data are categorized into three regions: Urban, suburban, and mountainous according to their locations. The DSD exhibits evidently different characteristics in the urban, suburban, and mountain regions of Beijing. The average raindrop diameter is smaller in the urban region compared to the suburban region. The average rain rate and raindrop number concentration are lower in the mountainous region compared to both urban and suburban regions. The difference in DSD between urban and suburban regions is due to the difference in DSD for the same precipitation types, while the difference in DSD between mountain and plains (i.e., urban and suburban regions) is the combined effect of the convection/stratiform ratio and the difference of DSD for the same precipitation types. Three DSD-based polarimetric radar QPE estimators were retrieved and estimated. Among these three QPE estimators, R(ZH), R(Kdp), and R(Kdp, ZDR), R(Kdp, ZDR) performs best, followed by R(Kdp), and R(ZH) performs worst. R(Kdp) is more sensitive to the representative parameters, while R(ZH) and R(Kdp, ZDR) are more sensitive to observational error and systematic bias (i.e., calibration). [ABSTRACT FROM AUTHOR]

We studied the characteristics of quasi-two-day wave (QTDW) using the meridional wind in the mesosphere and lower thermosphere (MLT) obtained from a meteor radar over Kototabang (KB, 0.2 ° S, 100.3 ° E) from 2003 to 2012. Atmospheric oscillations have a crucial impact on atmospheric dynamics, which contributes to more accurate space weather forecasting, thus providing a more secure space environment for human space exploration activities such as remote sensing and satellite navigation. QTDWs are typical atmospheric oscillations in the upper stratosphere, mesosphere and lower thermosphere. The occurrence time, amplitudes, periods and vertical wavelengths of QTDW events are analyzed statistically. Data obtained from the TIMED Doppler Interferometer (TIDI), which can measure wind and temperature and is onboard the Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics (TIMED) satellite, are used to analyze the global distribution and spatial structure of QTDWs with different zonal wavenumbers. The characteristics of the QTDWs over KB are compared with the QTDWs at the middle latitudes using the meridional wind data from a meteor radar over Wuhan (114.4 ° E, 30.6 ° N), Beijing (116.5 ° E, 39.9 ° N) and Mohe (121.1 ° E, 50.1 ° N). The amplitudes of the QTDW and spectral analysis are calculated by the least squares fitting method. Our results demonstrate that QTDWs are present almost all year around over KB. The occurrence time, amplitudes, periods and vertical wavelengths of QTDW events with different zonal wavenumbers are determined in this study. We also find that the statistical characteristics of the QTDWs in KB are different from those at middle latitudes. The westward zonal wavenumber −4 (W4) events gradually increase with increasing latitude, whereas westward zonal wavenumbers −1, −2, and −3 (W1, W2 and W3, respectively) events all decrease with increasing latitude. [ABSTRACT FROM AUTHOR]

Based on the quality-controlled observational spectral width data of the Beijing mesosphere–stratosphere–troposphere (MST) radar in the altitudinal range of 3–19.8 km from 2012 to 2014, this paper analyses the relationship between the proportion of negative turbulent kinetic energy (N-TKE) and the horizontal wind speed and the vertical shear of horizontal wind domain and gives the distributional characteristics of atmospheric turbulence parameters obtained by using different calculation models. Three calculation models of the spectral width method were used in this study – namely the H model (Hocking, 1985), N-2D model (Nastrom, 1997) and D–H model (Dehghan and Hocking, 2011). The results showed that the proportion of N-TKE in the H model, N-2D model and D–H model increases with the horizontal wind speed u and/or the vertical shear of horizontal wind speed ∂u∂z , and the maximum values are 60 %, 45 % and 35 %, respectively. When the ∂u∂z is greater than 0.006 s -1 , the N-TKE of the H model increases sharply with ∂u∂z ; the increase rate is about 20%0.002s-1. For these three models, the results are similar except that the vertical shear of the horizontal wind speed is greater than 0.006 s -1. When ∂u∂z>0.006 s -1 , the proportion of N-TKE in the N-2D and H models increases with ∂u∂z , while the proportion in the D–H model is less than 10 % and has slight variation. However, it is still necessary to consider the applicability of the N-2D model and D–H model in some weather processes with strong winds. The distributional characteristics with height of the turbulent kinetic energy dissipation rate ε and the vertical eddy diffusion coefficient Kz derived by the three models are consistent with previous studies. Still, there are differences in the values of turbulence parameters. Also, the range resolution of the radar has little effect on the differences in the range of turbulence parameters' values. The median values of ε in the H model, N-2D model and D–H model is 10 -3.2 –10 -2.7 , 10 -3.0 –10 -2.6 and 10 -3.3 –10 -2.8 m 2 s -3 , respectively. The median values of Kz in these three models are 10 0.3 –10 0.7 , 10 0.4 –10 0.7 and 10 0.1 –10 0.5 m 2 s -1. [ABSTRACT FROM AUTHOR]

Severe hailstorms frequently occurred in Beijing between May and August 2021, leading to extensive hail damage. These hailstorms were observed by radar and satellite data, and reported by surface observers. In this study, the spectral and cloud microphysical characteristics of typical Beijing events in 2021 were analyzed using Himawari-8 satellite products and ground-based S-band weather radar data obtained from the Beijing Meteorological Bureau. The relationship between Himawari-8 brightness temperature differences (BTD) and radar reflectivity was also investigated. The results revealed that the significant spectral depression of brightness temperatures (BTs) in hail clouds was observed by a satellite. Furthermore, the stronger the radar reflectivity was, the more rapidly BTD decreased, with a nonlinear relationship between them. The results of cloud physical characteristics show that, for cloud-top heights above 10 km, the cloud effective radius was about 25 μm, with a cloud-top temperature of 225 K during these hail events. By means of Gaussian fitting, the BT threshold value (11.2 μm) was determined by satellite at 230 K, with a BTD focused on 1.9 K when hailstorms occurred. These results will help us better understand the characteristics of hailstorms, while also providing information for future hail suppression in Beijing. [ABSTRACT FROM AUTHOR]

The evolution of the atmospheric boundary layer (ABL) varies greatly with terrain, so that the spatial and temporal variabilities of the ABL height remain poorly understood over complex terrain. Using radar wind-profiler measurements obtained from rural mountainous (Yanqing) and adjoining urban-plain (Haidian) landscapes of Beijing, China in 2019, ABL heights are calculated based on a normalized signal-to-noise-ratio threshold. The seasonally contrasting features of ABL height variation and growth rate over the two sites are revealed for clear-sky conditions. Interestingly, the ABL in spring remains suppressed during the morning and evolves rapidly in the afternoon over Haidian; however, a usual diurnal ABL evolution is observed over Yanqing. During the winter, more rapid evolution of the ABL is observed over Haidian, although on average the daytime ABL height remains less than 800 m above ground level. The growth rate of ABL height is found to undergo a more pronounced seasonal variation over Haidian while being relatively less variable over Yanqing. As expected, the lowest (highest) growth rate of 90 m h−1 (188 m h−1) occurs in winter (summer) over Haidian. The analysis of the seasonal variations in wind profiles reveals deeper insights into the development of the local plain-to-mountain flow circulation over the region and possible implications on the contrasting seasonal ABL variations, particularly during the spring and summer. Additionally, the slower ABL evolution over Haidian in autumn and winter could be associated with an aerosol-induced stable ABL as well as stronger urban heat accumulation. The findings have implications for the better understanding of air pollution meteorology in regions with mountainous terrain. [ABSTRACT FROM AUTHOR]

Stable oxygen isotope composition (δ18O p) and deuterium excess (d p) in precipitation carry important climate and hydrological information on the history of water from evaporation, transport to precipitation. This study aims to quantify the effect of potential factors on site-specific intra-event δ18O p and d p dynamics during three precipitation events in a mid-latitude region Beijing, China. The tested factors included moisture source (M s) and upstream rainout fraction (U p) obtained by HYSPLIT backward trajectory model, radar reflectivity factor (Z e) that corresponds to raindrop size by weather radar, sub-cloud evaporation fractionation (f e) by an isotope-evaporation model, local precipitation amount (L p) and precipitation intensity (I) by rain gauge, and relative location of the sampling sites (downwind or upwind). Intra-event δ18O p had an amplitude of 10.3‰, and 84% of the variance could be explained, in the descending order of importance, by M s , site, Z e , f e and I. Intra-event d p had an amplitude of 16.5‰, and 57% of the variance could be explained by f e , site, M s and I , in the descending order of importance. Absolute values of intra-event δ18O p and d p differed between two sites, 30 km apart, by up to 4.7‰ and 9.7‰, respectively. This site-specificity in δ18O p and d p was mainly caused by differences in rainout effects and I. This work provides new insights into separating the effects of various climatic and hydrological variables on high-frequency dynamics of δ18O p and d p. • Intra-event δ18O p and d p vary largely over time and between neighboring sites. • Moisture sources and sub-cloud evaporation mainly impact intra-event δ18O p and d p. • Rainout and precipitation intensity cause site-specific intra-event δ18O p and d p. [ABSTRACT FROM AUTHOR]

In this study, a lightning data assimilation (LDA) method adjusting dynamical fields was examined in the rapid cycle with Weather Research and Forecasting (WRF) model and WRF Data Assimilation (WRFDA). This method retrieves pseudo vertical velocity profiles from total lightning observations and promotes wind convergence over lightning regions. This newly developed LDA scheme is compared with radar data assimilation (RDA) that has been routinely applied in severe storm nowcasting models. Depending on whether the radar or lightning data is assimilated, four experiments were designed to evaluate the positive impacts of dynamical adjustment from LDA. Using a typical severe mesoscale convection system occurred in Beijing, we found that the effect of RDA and LDA are different. The assimilation of radar radial velocity mainly improves the forecast in a longer time, while assimilation of pseudo-vertical-velocity from lightning data mainly corrects intensity and location of the forecasted precipitation. When both radar and lightning data are assimilated, the small-scale wind convergence are promoted and contributes to the intensified updrafts. Thermal and water vapor fields are also adjusted indirectly. Consequently, the convective precipitation is significantly improved and the positive impacts from the combined assimilation scheme persisted over a longer period (at least 3 h). It can be concluded that a combined assimilation of convective data from multiple sources such as radar and lightning data enhance prominently the accuracy of short-term convection forecasts. • A lightning data assimilation method adjusting dynamical fields is applied in nowcasting through rapid update cycling. • Radar data and lightning data assimilation are combined to improve the accuracy of short-term precipitation forecasts. • Radar radial velocity improves forecast in a longer time, while lightning data corrects its intensity and location. [ABSTRACT FROM AUTHOR]

Based on three years of data observed by Beijing MST radar between 2012 and 2014, this study gives the distribution characteristics of the turbulence energy dissipation rate ε and the vertical turbulence diffusion coefficient K z in the troposphere–lower stratosphere over Beijing based on the spectral width method. Additionally, from the perspective of atmospheric stability expressed by the Brunt–Väisälä frequency N and the gradient Richardson number Ri, the seasonal variations of turbulence parameters at different heights are analyzed. The results show that ε ( K z ) is distributed in the range of 10−4 to 10−1.5 m2 s−3 (100 to 101 m2 s−1), with median values of 10−3 to 10−2.5 m2 s−3 (100.4 to 100.75 m2 s−1). In the height range of −1 km to about +1 km from the tropopause, the profiles of ε have no noticeable seasonal difference from October to next May, and ε increases sharply with the slope of linear regression ∇log(ε)/∇h = 0.25 (m2 s−3) km−1. The seasonal variation of turbulence parameters has noticeable differences at different atmospheric layers. Furthermore, the atmospheric static/dynamic stability and turbulence intensity are the influencing factors of turbulence parameters. From the tropopause to 14 km, the values of ε and N both have a peak from October to next May, and this peak layer disappears from July to August. Within the range of 2–4 km from the ground, K z and the frequency of Ri < 0.25 have a significant positive correlation, with a Pearson correlation coefficient greater than 0.5, and both have large value layers in March to May and September to October. The turbulence intensity above 15 km is likely the main factor affecting the turbulence dissipation rate and vertical turbulence diffusion coefficient. [ABSTRACT FROM AUTHOR]