Ozonesonde launches were routinely performed in Beijing from March 2001 to February 2019 to generate a unique long-term (18 years) vertical ozone profile dataset over mainland China. This study elucidates the vertical ozone structure on various temporal scales during this 18 years period by using the entire ozonesonde data product for the first time. Moreover, the long-term variability in the integrated ozone column over the North China Plain (NCP) is also explored by comparing the retrievals from ozonesonde at the Beijing urban site and a Dobson ozone spectrometer at the Xianghe suburban site. Our results indicate that vertical ozone exhibited clear monthly variability characterized by high values of tropospheric ozone during warm seasons and high values of stratospheric ozone during cold seasons. Stratospheric intrusions frequently occurred during spring and effectively transported cold air masses with high ozone from the lower stratosphere downward into the upper troposphere. Evident interannual variability in the lower troposphere and in ozone-rich areas of the stratosphere was revealed by vertical ozone distributions. The integrated total ozone columns retrieved from ozonesonde and Dobson bear close resemblance and exhibit strong sinusoidal monthly variations. In the troposphere and boundary layer, the integrated ozone column presented a significant positive trend during 2001–2012 in Beijing; a sudden decline occurred between 2011 and 2013, which was followed by a slow and insignificant increase after the implementation of the Clean Air Action plan in 2013 on the NCP.

The first Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on Suomi National Polar-orbiting Partnership (S-NPP) satellite in late 2011. Similar to the Moderate resolution Imaging Spectroradiometer (MODIS), VIIRS observes top-of-atmosphere spectral reflectance and is potentially suitable for retrieval of the aerosol optical depth (AOD). The VIIRS Environmental Data Record data (VIIRS_EDR) is produced operationally by NOAA, and is based on the MODIS atmospheric correction algorithm. The “MODIS-like” VIIRS data (VIIRS_ML) are being produced experimentally at NASA, from a version of the “dark-target” algorithm that is applied to MODIS. In this study, the AOD and aerosol model types from these two VIIRS retrieval algorithms over the North China Plain (NCP) are evaluated using the ground-based CE318 Sunphotometer (CE318) measurements during 2 May 2012–31 March 2014 at three sites. These sites represent three different surface types: urban (Beijing), suburban (XiangHe) and rural (Xinglong). Firstly, we evaluate the retrieved spectral AOD. For the three sites, VIIRS_EDR AOD at 550 nm shows a positive mean bias (MB) of 0.04–0.06 and the correlation of 0.83–0.86, with the largest MB (0.10–0.15) observed in Beijing. In contrast, VIIRS_ML AOD at 550 nm has overall higher positive MB of 0.13–0.14 and a higher correlation (0.93–0.94) with CE318 AOD. Secondly, we evaluate the aerosol model types assumed by each algorithm, as well as the aerosol optical properties used in the AOD retrievals. The aerosol model used in VIIRS_EDR algorithm shows that dust and clean urban models were the dominant model types during the evaluation period. The overall accuracy rate of the aerosol model used in VIIRS_ML over NCP three sites (0.48) is higher than that of VIIRS_EDR (0.27). The differences in Single Scattering Albedo (SSA) at 670 nm between VIIRS_ML and CE318 are mostly less than 0.015, but high seasonal differences are found especially over the Xinglong site. The values of SSA from VIIRS_EDR are higher than that observed by CE318 over all sites and all assumed aerosol modes, with a positive bias of 0.02–0.04 for fine mode, 0.06–0.12 for coarse mode and 0.03–0.05 for bi-mode at 440 nm. The overestimation of SSA but positive AOD MB of VIIRS_EDR indicate that other factors (e.g., surface reflectance characterization or cloud contamination) are important sources of error in the VIIRS_EDR algorithm, and their effects on aerosol retrievals may override the effects from non-ideality in these aerosol models.

An aerosol layer in the upper troposphere and lower stratosphere over the Asian summer monsoon (ASM) regions, namely, the Asian tropopause aerosol layer (ATAL), has been observed based on satellite remote sensing and in situ measurements; however, its source is still under debate. In August 2018, an experimental campaign over the Tibetan Plateau at Golmud (GLM, 36.48 °N, 94.93 °E) was performed, during which a balloon-borne Portable Optical Particle Counter was used to measure the aerosol particle profile. Backward-trajectory simulations were conducted with the Massive-Parallel Trajectory Calculations model to investigate the possible sources and transport pathways of the observed particles. The in situ measurements showed a robust ATAL around the tropopause, 16 km above sea level, with a maximum aerosol number density of 35 cm⁻³ and a maximum aerosol mass concentration of 0.15 μg m⁻³ for particles with diameters between 0.14 and 3 μm. The aerosol particles in the ATAL are mostly smaller than 0.25 μm in diameter, accounting for 98% of all aerosol particles detected. The backward-trajectory analysis revealed that the air parcels arrived at the altitude of the ATAL through two separate pathways: (1) the uplift below the 360 K isentropic surface, where air parcels were first elevated to the upper troposphere and then joined the ASM anticyclonic circulation; and (2) the quasi-horizontal transport along the anticyclonic circulation, located approximately between the 360 and 420 K isentropic surfaces. The complex transport pathways may aggravate the challenge of analyzing the composition of the ATAL, and further observation campaigns are required to extend our knowledge.

Knowledge of vertical air motion in the atmosphere is important for both meteorological and climate studies due to its impact on clouds, precipitation and the vertical transport of air masses, heat, momentum, and composition. The vertical velocity (VV) of air is among the most difficult and uncertain quantities to measure due to its generally small magnitude and high temporal and spatial variability. In this study, a descending radiosonde system is developed to derive VV at the low and middle troposphere in north China during the summer months. The VV is estimated from the difference between the observed radiosonde descent speed and the calculated radiosonde descent speed in still air based on the fluid dynamic principle. The results showed that the estimated VV generally ranged from −1 m/s to 1 m/s, accounting for 80.2% of data points. In convective conditions, a wider distribution of the VV was observed, which was skewed to large values relative to those in nonconvective conditions. The average VV throughout the entire profile was close to 0 m/s under nonconvective conditions. In contrast, distinctive vertical air motions below 5 km above the ground were recorded under convective activities. Vigorous air motions with an absolute VV >2 m/s were occasionally observed and were often associated with the occurrence of cloud layers. Moreover, the detailed structure of the instant air motion near the cloud boundaries (i.e., top and base), with an absolute VV >10 m/s in convective weather systems, was clearly revealed by this technique. The uncertainty estimation indicated that this method has the potential to capture and describe events with vertical air motions >0.69 m/s, which is useful for a convective weather study. Further studies are required to carefully assess the accuracy and precision of this novel VV estimation technique.

Clear-sky detection (CSD) is of critical importance in solar energy applications and surface radiative budget studies. Existing CSD methods are not sufficiently validated due to the lack of high-temporal resolution and long-term CSD ground observations, especially at polluted sites. Using five-year high resolution ground-based solar radiation data and visual inspected Total Sky Imager (TSI) measurements at polluted Xianghe, a suburban site, this study validated 17 existing CSD methods and developed a new CSD model based on a machine-learning algorithm (Random Forest: RF). The propagation of systematic errors from input data to the calculated global horizontal irradiance (GHI) is confirmed with Mean Absolute Error (MAE) increased by 99.7% (from 20.00 to 39.93 W·m⁻²). Through qualitative evaluation, the novel Bright-Sun method outperforms the other traditional CSD methods at Xianghe site, with high accuracy score 0.73 and 0.92 under clear and cloudy conditions, respectively. The RF CSD model developed by one-year irradiance and TSI data shows more robust performance, with clear/cloudy-sky accuracy score of 0.78/0.88. Overall, the Bright-Sun and RF CSD models perform satisfactorily at heavy polluted sites. Further analysis shows the RF CSD model built with only GHI-related parameters can still achieve a mean accuracy score of 0.81, which indicates RF CSD models have the potential in dealing with sites only providing GHI observations.