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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]

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]

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]

The seasonal variations of raindrop size distribution (DSD) and rainfall are investigated using three-year (2016–2018) observations from a two-dimensional video disdrometer (2DVD) located at a suburban station (40.13° N, 116.62° E, ~30 m AMSL) in Beijing, China. The annual distribution of rainfall presents a unimodal distribution with a peak in summer with total rainfall of 966.6 mm, followed by fall. Rain rate (R), mass-weighted mean diameter (Dm), and raindrop concentration (Nt) are stratified into six regimes to study their seasonal variation and relative rainfall contribution to the total seasonal rainfall. Heavy drizzle/light rain (R2: 0.2~2.5 mm h−1) has the maximum occurrence frequency throughout the year, while the total rainfall in summer is primarily from heavy rain (R4: 10~50 mm h−1). The rainfall for all seasons is contributed primarily from small raindrops (Dm2: 1.0~2.0 mm). The distribution of occurrence frequency of Nt and the relative rainfall contribution exhibit similar behavior during four seasons with Nt of 10~1000 m−3 registering the maximum occurrence and rainfall contributions. Rainfall in Beijing is dominated by stratiform rain (SR) throughout the year. There is no convective rainfall (CR) in winter, i.e., it occurs most often during summer. DSD of SR has minor seasonal differences, but varies significantly in CR. The mean values of log10Nw (Nw: mm−1m−3, the generalized intercept parameter) and Dm of CR indicate that the CR during spring and fall in Beijing is neither continental nor maritime, at the same time, the CR in summer is close to the maritime-like cluster. The radar reflectivity (Z) and rain rate () relationship (Z = R) showed seasonal differences, but were close to the standard NEXRAD Z-R relationship in summer. The shape of raindrops observed from 2DVD was more spherical than the shape obtained from previous experiments, and the effect of different axis ratio relations on polarimetric radar measurements was investigated through T-matrix-based scattering simulations. [ABSTRACT FROM AUTHOR]

Cloud top height (CTH) is an essential pareter for the general circulation model in understanding the impact of clouds on the Earth's radiation budget and global climate change. This paper compares the CTH products, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the Aqua and Terra satellites with ground-based Ka band radar data in Beijing from 2014 to 2017. The aim was to investigate the data accuracy and the difference in CTH measurements between passive satellite data and active ground-based radar data. The results show that MODIS, on average, underestimates CTH relative to radar by −1.08 ± 2.48 km, but with a median difference of −0.65 km and about 48% of differences are within 1 km. Statistically, MODIS CTHs which are greater than 6 km show lower discrepancy to radar CTH than those of MODIS CTHs less than 4 km. The CTH difference is independent of cloud fraction and cloud layer. It shows strong dependence on cloud depth, decreasing as cloud depth increases. There is a tendency for MODIS to underestimate high thin clouds but overestimate low thin clouds relative to radar. Total ozone, SO2, CO, NO2, aerosol PM10, total water vapor and temperature inversion show unobvious influences in the CTH discrepancy. It is shown that the MODIS CO2-slicing technique performs much better than IRW (infrared window) technique when cloud layer is higher than 2 km. The average difference calculated from all comparisons by CO2-slicing technique and IRW technique is 0.09 ± 1.58 km, and −2.20 ± 2.73 km, respectively. [ABSTRACT FROM AUTHOR]

The millimeter-wave cloud radar, ceilometer, and disdrometer have been widely used to observe clouds and precipitation. However, there are some drawbacks when those three instruments are solely employed due to their own limitations, such as the fact that radars usually suffer from signal attenuation and ceilometers/disdrometers cannot provide measurements of the hydrometeors of aloft clouds and precipitation. Thus, in this paper, we developed an integrated technology by combining and utilizing the advantages of three instruments together to investigate the vertical structure and diurnal variation of warm clouds and precipitation, and the raindrop size distribution. Specifically, the technology consists of appropriate data processing, quality control, and retrieval methods. It was implemented to study the warm clouds and precipitation in South China during the pre-flood season of 2016. The results showed that the hydrometeors of warm clouds and precipitation were mainly distributed below 2.5 km and most of the rainfall events were very light with a rain rate less than 1 mm h−1, however, the stronger precipitation primarily contributed the accumulated rain amount. Furthermore, a rising trend of cloud base height from 1000 to 1900 BJT was found. The cloud top height and cloud thickness gradually increased from 1200 BJT to reach a maximum at 1600 BJT (Beijing Standard Time, UTC+8), and then decreased until 2000 BJT. Also, three periods of the apparent rainfall on the ground of the day, namely, 0400–0700 BJT, 1400–1800 BJT, and 2300–2400 BJT were observed. During three periods, the raindrops had wider size spectra, higher number concentrations, larger rain rates, and higher water contents than at other times. The hydrometeor type, size, and concentration were gradually changed in the vertical orientation. The raindrop size distributions of warm precipitation in the air and on the ground were different, which can be expressed by γ distributions N(D) = 1.49 × 104D−0.9484exp(−6.79D) in the air and N(D) = 1.875 × 103D0.862exp(−2.444D) on the ground, where D and N(D) denote the diameter and number concentration of the raindrops, respectively. [ABSTRACT FROM AUTHOR]