The relationship between aerosol optical depth (AOD) and PM_2.5 is often investigated in order to obtain surface PM_2.5 from satellite observation of AOD with a broad area coverage. However, various factors could affect the AOD–PM_2.5 regressions. Using both ground and satellite observations in Beijing from 2011 to 2015, this study analyzes the influential factors including the aerosol type, relative humidity (RH), planetary boundary layer height (PBLH), wind speed and direction, and the vertical structure of aerosol distribution. The ratio of PM_2.5 to AOD, which is defined as η, and the square of their correlation coefficient (R²) have been examined. It shows that η varies from 54.32 to 183.14, 87.32 to 104.79, 95.13 to 163.52, and 1.23 to 235.08 µg m⁻³ with aerosol type in spring, summer, fall, and winter, respectively. η is smaller for scattering-dominant aerosols than for absorbing-dominant aerosols, and smaller for coarse-mode aerosols than for fine-mode aerosols. Both RH and PBLH affect the η value significantly. The higher the RH, the smaller the η, and the higher the PBLH, the smaller the η. For AOD and PM_2.5 data with the correction of RH and PBLH compared to those without, R² of monthly averaged PM_2.5 and AOD at 14:00 LT increases from 0.63 to 0.76, and R² of multi-year averaged PM_2.5 and AOD by time of day increases from 0.01 to 0.93, 0.24 to 0.84, 0.85 to 0.91, and 0.84 to 0.93 in four seasons respectively. Wind direction is a key factor for the transport and spatial–temporal distribution of aerosols originated from different sources with distinctive physicochemical characteristics. Similar to the variation in AOD and PM_2.5, η also decreases with the increasing surface wind speed, indicating that the contribution of surface PM_2.5 concentrations to AOD decreases with surface wind speed. The vertical structure of aerosol exhibits a remarkable change with seasons, with most particles concentrated within about 500 m in summer and within 150 m in winter. Compared to the AOD of the whole atmosphere, AOD below 500 m has a better correlation with PM_2.5, for which R² is 0.77. This study suggests that all the above influential factors should be considered when we investigate the AOD–PM_2.5 relationships.

Aeolus is the first satellite mission to directly observe wind profile information on a global scale. After implementing a set of bias corrections, the Aeolus data products went public on 12 May 2020. However, Aeolus wind products over China have thus far not been evaluated extensively by ground-based remote sensing measurements. In this study, the Mie-cloudy and Rayleigh-clear wind products from Aeolus measurements are validated against wind observations from the radar wind profiler (RWP) network in China. Based on the position of each RWP site relative to the closest Aeolus ground tracks, three matchup categories are proposed, and comparisons between Aeolus wind products and RWP wind observations are performed for each category separately. The performance of Mie-cloudy wind products does not change much between the three matchup categories. On the other hand, for Rayleigh-clear and RWP wind products, categories 1 and 2 are found to have much smaller differences compared with category 3. This could be due to the RWP site being sufficiently approximate to the Aeolus ground track for categories 1 and 2. In the vertical, the Aeolus wind products are similar to the RWP wind observations, except for the Rayleigh-clear winds in the height range of 0–1 km. The mean absolute normalized differences between the Mie-cloudy (Rayleigh-clear) and the RWP wind components are 3.06 (5.45), 2.79 (4.81), and 3.32 (5.72) m/s at all orbit times and ascending and descending Aeolus orbit times, respectively. This indicates that the wind products for ascending orbits are slightly superior to those for descending orbits, and the observation time has a minor effect on the comparison. From the perspective of spatial differences, the Aeolus Mie-cloudy winds are consistent with RWP winds in most of east China, except in coastal areas where the Aeolus Rayleigh-clear winds are more reliable. Overall, the correlation coefficient R between the Mie-cloudy (Rayleigh-clear) wind and RWP wind component observation is 0.94 (0.81), suggesting that Aeolus wind products are in good agreement with wind observations from the RWP network in China. The findings give us sufficient confidence in assimilating the newly released Aeolus wind products in operational weather forecasting in China.

The knowledge of mesoscale convective system (MCS) in the Asian monsoon region remains still deficient due to the limited available data and less powerful algorithms. Here, using the data from Advanced Himawari Imager onboard Himawari‐8 (HW8), an improved algorithm combining the area overlapping with the Kalman filter is developed, which captures much smaller MCSs that are unavailable otherwise. Several influential factors like the overlapping rate and splitting/merging in the area overlapping method, and the initial state variable in the Kalman filter method, all of which were less appreciated, are handled explicitly. The occurrence frequency, and moving trajectory of two types of MCS, including the ordinary MCS and superconvective system, has been comprehensively examined in the Asian monsoon region for the warm season (April to September) of 2016. Comparison analyses with ground precipitation and radar measurements confirm the good performance of our algorithm. In particular, the moving direction of MCS strongly depends on latitudes, so does the horizontal velocity. Compared with over ocean, the frequency of MCSs dominates over land or along coasts in the tropics, where strong moisture flux convergence is frequently observed in the low troposphere. In addition, the MCSs detected in eastern China can roughly capture the meridional propagation over time, which corresponds well to the precipitation belts linked to Meiyu front systems. The superconvective systems dominate over the Bay of Bengal and South China Sea due to the large‐scale circulation. Our findings provide new insights to spatiotemporal patterns of MCSs during warm season in the Asian monsoon region.

The planetary boundary layer (PBL) is the lowermost part of the troposphere that governs the exchange of momentum, mass and heat between surface and atmosphere. To date, the radiosonde measurements have been extensively used to estimate PBL height (PBLH); suffering from low spatial coverage and temporal resolution, the radiosonde data are incapable of providing a diurnal description of PBLH across the globe. To fill this data gap, this paper aims to produce a temporally continuous PBLH dataset during the course of a day over the global land by applying machine learning algorithms to integrate high-resolution radiosonde measurements, ERA5 reanalysis, and the Global Land Data Assimilation System (GLDAS) product. This dataset covers the period from 2011 to 2021 with a temporal resolution of 3 h and a horizontal resolution of $\mathrm{0.25}{}^{\circ }\times \mathrm{0.25}{}^{\circ }$ . The radiosonde dataset contains around 180 million profiles over 370 stations across the globe. The machine learning model was established by taking 18 parameters derived from ERA5 reanalysis and GLDAS as input variables, while the PBLH biases between radiosonde observations and ERA5 reanalysis were used as the learning targets. The input variables were presumably representative regarding the land properties, near-surface meteorological conditions, terrain elevations, lower tropospheric stabilities, and solar cycles. Once a state-of-the-art model had been trained, the model was then used to predict the PBLH bias at other grids across the globe with parameters acquired or derived from ERA5 and GLDAS. Eventually, the merged PBLH can be taken as the sum of the predicted PBLH bias and the PBLH retrieved from ERA5 reanalysis. Overall, this merged high-resolution PBLH dataset was globally consistent with the PBLH retrieved from radiosonde observations in terms of both magnitude and spatiotemporal variation, with a mean bias of as low as −0.9 m. The dataset and related codes are publicly available at https://doi.org/10.5281/zenodo.6498004 (Guo et al., 2022), and are of significance for a multitude of scientific research endeavors and applications, including air quality, convection initiation, climate, and climate change, to name but a few.

Many efforts have been taken to investigate aerosol–cloud interactions from space, but only a few studies have examined the response of vertical cloud structure to aerosol perturbations. Three-dimensional cloud climatologies of eight different cloud types identified from the CloudSat level-2 cloud product during the warm season (May–September) in 2008–10 over eastern China were first generated and analyzed. Using visibility as a proxy for cloud condensation nuclei, in combination with satellite-observed radar reflectivity, normalized contoured frequency by altitude diagrams of the differences in cloud radar reflectivity Z profiles under polluted and clean conditions were constructed. For shallow cumulus clouds (shallow Cu) Z tends to be inhibited, and it is enhanced in the upper layers for deep cumulus (deep Cu), nimbostratus (Ns), and deep convective clouds (DCC) under polluted conditions. Overall, analyses of the modified center of gravity (MCOG) and cloud-top height (CTH) also point to a similar aerosol effect, except for the nonsignificant changes in MCOGs and CTHs in deep Cu. The impacts of environmental factors such as lower-tropospheric stability and vertical velocity are also discussed for these types of clouds. Although consistent aerosol-induced elevations in MCOGs and CTHs for Ns and DCC clouds are observed, the effect of meteorology cannot be completely ruled out, which merits further analysis.