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The planetary boundary layer (PBL) height (PBLH) is an important parameter for weather, climate, and air quality models. Radiosonde is one of the most commonly used instruments for PBLH determination and is generally accepted as a standard for other methods. However, mainstream approaches for the estimation of PBLH from radiosonde present some uncertainties and even show disadvantages under some circumstances, and the results need to be visually verified, especially during the transition period of different PBL regimes. To avoid the limitations of individual methods and provide a benchmark estimation of PBLH, we propose an ensemble method based on high-resolution radiosonde data collected in Beijing in 2017. Seven existing methods including four gradient-based methods are combined along with statistical modification. The ensemble method is verified at 08:00, 14:00, and 20:00 Beijing time (BJT = UTC + 8), respectively. The overestimation of PBLH can be effectively eliminated by setting thresholds for gradient-based methods, and the inconsistency between individual methods can be reduced by clustering. Based on the statistics of a 1-year observational analysis, the effectiveness (E) of the ensemble method reaches up to 62.6 %, an increase of 6.5 %–53.0 % compared to the existing methods. Nevertheless, the ensemble method suffers to some extent from uncertainties caused by the consistent overestimation of PBLH, the profiles with a multi-layer structure, and the intermittent turbulence in the stable boundary layer (SBL). Finally, this method has been applied to characterize the diurnal and seasonal variations of different PBL regimes. Particularly, the average convective boundary layer (CBL) height is found to be the highest in summer, and the SBL is lowest in summer with about 200 m. The average PBLH at the transition stage lies around 1100 m except in winter. These findings imply that the ensemble method is reliable and effective. [ABSTRACT FROM AUTHOR]

The vertical distribution of atmospheric aerosol components is vital to the estimation of radiative forcing and the catalysis of atmospheric photochemical processes. Based on the synergy of ground-based lidar and sun-photometer in Generalized Aerosol Retrieval from Radiometer and Lidar Combined data (GARRLiC), this paper developed a new algorithm to get the vertical mass concentration profiles of fine-mode aerosol components for the first time. Retrieval of aerosol properties was achieved based on the sky radiance at multiple scatter angles, total optical depth (TOD) at 440, 675, 870, and 1020 nm, and lidar signals at 532 and 1064 nm. In addition, the internal mixing model and normalized volume size distribution (VSD) model were established according to the absorption and water solubility of the aerosol components, to separate the profiles of black carbon (BC), water-insoluble organic matter (WIOM), water-soluble organic matter (WSOM), ammonium nitrate-like (AN), and fine aerosol water (AW) content. Results showed a reasonable vertical distribution of aerosol components compared with in situ observations and reanalysis data. The estimated and observed BC concentrations matched well with a correlation coefficient up to 0.91, while there was an evident overestimation of organic matter (OM = WIOM + WSOM, NMB = 0.98). Moreover, the retrieved AN concentrations were closer to the simulated results (R = 0.85), especially in polluted conditions. The BC and OM correlations were relatively weaker, with a correlation coefficient of ∼ 0.5. Besides, the uncertainties caused by the input parameters (i.e., relative humidity (RH), volume concentration, and extinction coefficients) were assessed using the Monte Carlo method. The AN and AW had smaller uncertainties at higher RH. Herein, the proposed algorithm was also applied to remote-sensing measurements in Beijing with two typical cases. In the clean condition with low RH, there were comparable AN and WIOM, but peaking at different altitudes. On the other hand, in the polluted case, AN was dominant and the maximum mass concentration occurred near the surface. We expected that the algorithm could provide a new idea for lidar inversion and promote the development of aerosol component profiles. [ABSTRACT FROM AUTHOR]

Mineral dust is of great importance to climate change, air quality, and human health. In this study, multisource data, including the reanalysis data and remote sensing data, were used to compare the three dust events that occurred in the March of 2021 over Beijing and reveal the effects of atmospheric vertical structure on near-surface dust concentration. The combined effect of the Mongolian cyclone and a wide persistent cold-front induced two events (E1: from March 15 to 16 and E3: from March 28 to 29). E1 was more intense, more extensive, and longer-lasting than E3 due to the combination of the stronger Mongolian cyclone, slower high/cold surface pressure, and the low-level jet. However, under the appropriate configurations of temperature and pressure fields between high and low altitudes, weak updrafts were still induced and could elevate dust up to 850 hPa, as occurred during E2 on March 22 and 23. The dust emission was inferior to E1 and E3, which contributes to the low dust concentration near the surface in E2. On the other hand, the downdraft strength directly affected both the vertical distribution of dust and the concentration of surface particles. There was a strong temporal consistency between the occurrence of the downdraft and the dust touchdown. In E1, the continuous strong downdraft caused the maximum dust concentration to be above 4000 μg/m3 at around 200 m. In contrast, the maximum height of the dust mass concentration in E3 occurred at about 800 m due to the transient downdraft, which weakened its effect on surface visibility. Besides, the weak vertical motion in E2 caused most of the dust to become suspended in the air. Overall, the large dust emission resulted from active updrafts in the source region, and the lengthy strong downdrafts led to the ultrahigh particle concentration near the surface. [ABSTRACT FROM AUTHOR]