This study investigates the impact of the aerosol hygroscopic growth effect on haze events in Xingtai, a heavily polluted city in the central part of the North China Plain (NCP), using a large array of instruments measuring aerosol optical, physical, and chemical properties. Key instruments used and measurements made include the Raman lidar for atmospheric water vapor content and aerosol optical profiles, the PC-3016A GrayWolf six-channel handheld particle and mass meter for atmospheric total particulate matter (PM) that has diameters less than 1 and 2.5 µm (PM₁ and PM_2.5, respectively), the aerosol chemical speciation monitor (ACSM) for chemical components in PM₁, and the hygroscopic tandem differential mobility analyzer (H-TDMA) for aerosol hygroscopicity. The changes in PM₁ and PM_2.5 agreed well with that of the water vapor content due to the aerosol hygroscopic growth effect. Two cases were selected to further analyze the effects of aerosol hygroscopic growth on haze events. The lidar-estimated hygroscopic enhancement factor for the aerosol backscattering coefficient during a relatively clean period (Case I) was lower than that during a pollution event (Case II) with similar relative humidity (RH) levels of 80 %–91 %. The Kasten model was used to fit the aerosol optical hygroscopic growth factor (GF) whose parameter b differed considerably between the two cases, i.e., 0.1000 (Case I) versus 0.9346 (Case II). The aerosol acidity value calculated from ACSM data for Case I (1.35) was less than that for Case II (1.50) due to different amounts of inorganics such as NH₄NO₃, NH₄HSO₄, and (NH₄)₂SO₄. Model results based on H-TDMA data showed that aerosol hygroscopic growth factors in each size category (40, 80, 110, 150, and 200 nm) at different RH levels (80 %–91 %) for Case I were lower than those for Case II. For similar ambient RH levels, the high content of nitrate facilitates the hygroscopic growth of aerosols, which may be a major factor contributing to heavy haze episodes in Xingtai.

The Taklamakan desert is an important dust source for the global atmospheric dust budget and a cause of the dust weather in East Asia. The characterization of Taklamakan dust in the source region is still very limited. To fill this gap, the DAO (dust aerosol observation) was conducted in April 2019 in Kashi, China. The Kashi site is about 150 km from the western rim of the Taklamakan desert and is strongly impacted by desert dust aerosols, especially in spring time, i.e., April and May. According to sun–sky photometer measurements, the aerosol optical depth (at 500 nm) varied in the range of 0.07–4.70, and the Ångström exponent (between 440 and 870 nm) in the range of 0.0–0.8 in April 2019. In this study, we provide the first profiling of the $\mathrm{2}\mathit{\alpha }+\mathrm{3}\mathit{\beta }+\mathrm{3}\mathit{\delta }$ parameters of Taklamakan dust based on a multiwavelength Mie–Raman polarization lidar. For Taklamakan dust, the Ångström exponent related to the extinction coefficient (EAE, between 355 and 532 nm) is about 0.01 ± 0.30, and the lidar ratio is found to be 45 ± 7 sr (51 ± 8–56 ± 8 sr) at 532 (355) nm. The particle linear depolarization ratios (PLDRs) are about 0.28–0.32 ± 0.07 at 355 nm, 0.36 ± 0.05 at 532 nm and 0.31 ± 0.05 at 1064 nm. Both lidar ratios and depolarization ratios are higher than the typical values of Central Asian dust in the literature. The difference is probably linked to the fact that observations in the DAO campaign were collected close to the dust source; therefore, there is a large fraction of coarse-mode and giant particles (radius >20 µm) in the Taklamakan dust. Apart from dust, fine particles coming from local anthropogenic emissions and long-range transported aerosols are also non-negligible aerosol components. The signatures of pollution emerge when dust concentration decreases. The polluted dust (defined by PLDR₅₃₂≤0.30 and EAE${}_{\mathrm{355}-\mathrm{532}}\ge \mathrm{0.20}$ ) is featured with reduced PLDRs and enhanced EAE_355−532 compared to Taklamakan dust. The mean PLDRs of polluted dust generally distributed in the range of 0.20–0.30. Due to the complexity of the nature of the involved pollutants and their mixing state with dust, the lidar ratios exhibit larger variabilities compared to those of dust. The study provides the first reference of novel characteristics of Taklamakan dust measured by Mie–Raman polarization lidar. The data could contribute to complementing the dust model and improving the accuracy of climate modeling.

The atmospheric mixing layer height (MLH) determines the space in which pollutants diffuse and is thus conducive to the estimation of the pollutant concentration near the surface. The study evaluates the capability of lidar to describe the evolution of the atmospheric mixing layer and then presents a long-term observed climatology of the MLH diurnal cycle. Detection of the mixing layer heights (MLH_L and MLH${}_{\mathrm{L}}^{\prime }$ ) using the wavelet method based on lidar observations was conducted from January 2013 to December 2018 in the Beijing urban area. The two dataset results are compared with radiosonde as case studies and statistical forms. MLH_L shows good performance in calculating the convective layer height in the daytime and the residual layer height at night. While MLH${}_{\mathrm{L}}^{\prime }$ has the potential to describe the stable layer height at night, the performance is limited due to the high range gate of lidar. A nearly 6-year climatology for the diurnal cycle of the MLH is calculated for convective and stable conditions using the dataset of MLH_L from lidar. The daily maximum MLH_L characteristics of seasonal change in Beijing indicate that it is low in winter (1.404±0.751 km) and autumn (1.445±0.837 km) and high in spring (1.647±0.754 km) and summer (1.526±0.581 km). A significant phenomenon is found from 2014 to 2018: the magnitude of the diurnal cycle of MLH_L increases year by year, with peak values of 1.291±0.646 km, 1.435±0.755 km, 1.577±0.739 km, 1.597±0.701 km and 1.629±0.751 km, respectively. It may partly benefit from the improvement of air quality. As to converting the column optical depth to surface pollution, the calculated PM_2.5 using MLH_L data from lidar shows better accuracy than that from radiosonde compared with observational PM_2.5. Additionally, the accuracy of calculated PM_2.5 using MLH_L shows a diurnal cycle in the daytime, with the peak at 14:00 LST. The study provides a significant dataset of MLH_L based on measurements and could be an effective reference for atmospheric models of surface air pollution calculation and analysis.

Accurate measurement of the radiative properties of clouds and aerosols is of great significance to global climate change and numerical weather prediction. The multi-angle polarization imager (MAPI) onboard the Fengyun-3 precipitation satellite, planned to be launched in 2023, will provide the multi-angle, multi-shortwave infrared (SWIR) channels and multi-polarization satellite observation of clouds and aerosols. MAPI operates in a non-sun-synchronized inclined orbit and provides images with a spatial resolution of 3 km (sub-satellite) and a swath of 700 km. The observation channels of the MAPI include 1030 nm, 1370 nm, and 1640 nm polarization channels and corresponding non-polarization channels, which provide observation information from 14 angles. In-flight radiometric and polarimetric calibration strategies are introduced, aiming to achieve radiometric accuracy of 5% and polarimetric accuracy of 2%. Simulation experiments show that the MAPI has some unique advantages of characterizing clouds and aerosols. For cloud observation, the polarization phase functions of the 1030 nm and 1640 nm around the scattering angle of a cloudbow show strong sensitivity to cloud droplet radius and effective variance. In addition, the polarized observation of the 1030 nm and 1640 nm has a higher content of information for aerosol than VIS-NIR. Additionally, the unique observation geometry of non-sun-synchronous orbits can provide more radiometric and polarization information with expanded scattering angles. Thus, the multi-angle polarization measurement of the new SWIR channel onboard Fengyun-3 can optimize cloud phase state identification and cloud microphysical parameter inversion, as well as the retrieval of aerosols. The results obtained from the simulations will provide support for the design of the next generation of polarized imagers of China.

In April 2023, China launched its first precipitation measurement satellite, FengYun-3G (FY-3G), to accurately measure the spatial and vertical structures of precipitation in the middle and lower latitudes of Earth. Equipped with advanced instruments, including a dual-frequency precipitation radar (DPR), a microwave radiometer, a visible and infrared (IR) imager, and a radio occultation sounder with reflectometry, FY-3G is designed for simultaneous measurements of precipitation, atmospheric temperature/moisture profiles, and cloud properties while capturing land and sea imaging at the same time. Additionally, it carries a polarized multiangle imager (PMAI) as an experimental payload to enhance the understanding of cloud microphysics. Six months after its launch, FY-3G has successfully completed the in-orbit commissioning phase. The detailed evaluation of the performance and the functionality of the platform as well as the instruments is presented in this article. Calibration and validation processes are also discussed. Since the end of 2023, sensor-dependent data records have been carefully validated and made available for public use. As the first of two planned precipitation missions in the FengYun satellite series, FY-3G offers independent and high-quality data on the occurrence, type, and intensity of precipitation, complementing the current observations made by the Global Precipitation Measurement satellite. This mission is expected to significantly advance the global understanding of precipitation dynamics, mechanisms, and the water cycle.