Developing a big data analytics framework for generating the Long-term Gap-free High-resolution Air Pollutant concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and Earth system science analysis. By synergistically integrating multimodal aerosol data acquired from diverse sources via a tensor-flow-based data fusion method, a gap-free aerosol optical depth (AOD) dataset with a daily 1 km resolution covering the period of 2000–2020 in China was generated. Specifically, data gaps in daily AOD imageries from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra were reconstructed based on a set of AOD data tensors acquired from diverse satellites, numerical analysis, and in situ air quality measurements via integrative efforts of spatial pattern recognition for high-dimensional gridded image analysis and knowledge transfer in statistical data mining. To our knowledge, this is the first long-term gap-free high-resolution AOD dataset in China, from which spatially contiguous PM_2.5 and PM₁₀ concentrations were then estimated using an ensemble learning approach. Ground validation results indicate that the LGHAP AOD data are in good agreement with in situ AOD observations from the Aerosol Robotic Network (AERONET), with an R of 0.91 and RMSE equaling 0.21. Meanwhile, PM_2.5 and PM₁₀ estimations also agreed well with ground measurements, with R values of 0.95 and 0.94 and RMSEs of 12.03 and 19.56 µg m⁻³, respectively. The LGHAP provides a suite of long-term gap-free gridded maps with a high resolution to better examine aerosol changes in China over the past 2 decades, from which three major variation periods of haze pollution in China were revealed. Additionally, the proportion of the population exposed to unhealthy PM_2.5 increased from 50.60 % in 2000 to 63.81 % in 2014 across China, which was then reduced drastically to 34.03 % in 2020. Overall, the generated LGHAP dataset has great potential to trigger multidisciplinary applications in Earth observations, climate change, public health, ecosystem assessment, and environmental management. The daily resolution AOD, PM_2.5, and PM₁₀ datasets are publicly available at https://doi.org/10.5281/zenodo.5652257 (Bai et al., 2021a), https://doi.org/10.5281/zenodo.5652265 (Bai et al., 2021b), and https://doi.org/10.5281/zenodo.5652263 (Bai et al., 2021c), respectively. Monthly and annual datasets can be acquired from https://doi.org/10.5281/zenodo.5655797 (Bai et al., 2021d) and https://doi.org/10.5281/zenodo.5655807 (Bai et al., 2021e), respectively. Python, MATLAB, R, and IDL codes are also provided to help users read and visualize these data.

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 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.