Black carbon (BC) aerosol, as a typical optical absorption aerosol, is of great significance to the study of climate and radiation. The China Atmosphere Watch Network (CAWNET), established by the China Meteorological Administration (CMA), contains 35 BC-monitored stations, which have been collecting data using commercial Aethalometer instruments (AEs) since 2006. Element carbon (EC) data measured from the thermal/optical reflectance (TOR) method was used to correct the BC monitoring data from the AEs, which are affected by various sampling and analytical artifacts. The average difference before and after the revision was about 17.3% (±11.5%). Furthermore, we analyzed the variations of BC in China from 2006 to 2017 using a revised dataset. The ten-year averaged concentration of BC would have been applicable for climate analysis, and can be a comparison sample in future research. The concentrations of BC across the stations in China showed a general downward trend, with occasional fluctuations, and the concentrations at urban sites decreased more significantly. The average concentrations of BC in urban sites are higher than rural and remote sites. The 10-year averaged concentration of BC ranges from 11.13 μg m⁻³ in Gucheng to 0.19 μg m⁻³ in Shangri-La, showing a strong spatial variation; the proportion of BC aerosol in PM_2.5 is generally less than 20%. The BC showed obvious seasonal and diurnal variation; and the highest concentration occurred in winter, with more dramatic diurnal variation, followed by autumn and spring. There was a significant increase in concentration between local time 7:00–9:00 and 18:00–0:00. The distribution and trend of BC concentration in China showed a consistency with emissions of BC.

To enhance the accuracy of cloud classification, this study proposes cloud classification models based on machine learning algorithms. The models take as input the observed reflectance or brightness temperature of 12 channels of the Advanced Geostationary Radiation Imager (AGRI) on Fengyun-4A satellite, and multi-channel clear sky brightness temperature. The classification results of the CPR-CALIOP merged product are used as the truth for training and validating the models. These models are developed to reliably detect and classify the clouds during daytime as well as for all-time (including both day and night). The results obtained from the developed models show better accuracies relative to those of the Fengyun 4A Level-2 cloud products in terms of cloud detection and classification. The models provide a feasible method for the detection of multi-layer clouds and classification of clouds at night. The applicability of cloud classification results based on CPR-CALIOP from the perspective of spectral sensitivity is analyzed on AGRI observations, providing valuable prior knowledge for cloud classification methods based on geostationary satellite imagers. The accuracies of single-layer cloud type classification during the day and all-time are 83.4% and 79.4%, respectively. Compared with the ISCCP classification method, the model’s identification of Nimbostratus and the Deep convection clouds (Ni/DC) has better consistency with precipitation observed by GPM satellite, which helps to track and monitor precipitation processes. This study also evaluates the model results using CALIPSO products and ground-based cloud radar, demonstrating that they can obtain accurate and robust results in different time periods and regions.

A physics-driven machine learning (ML) algorithm that integrates radiative transfer model (RTM) simulation and ML techniques has been developed to facilitate multilayer cloud property retrievals from advanced Himawari imager (AHI) measurements based on the geostationary satellite Himawari-8. Theoretical sensitive study revealed that integrating RTM-simulated clear-sky radiances into the retrieval model holds substantial potential to enhance multilayer cloud property retrieval. Thus, a convolutional neural network (CNN) model called CNN_TL was designed for simultaneous retrieval of ice and water properties in multilayer clouds, based on the combined use of visible/near-infrared (VNIR) and thermal infrared (TIR) measurements and RTM-simulated clear-sky radiances as predictors. The CNN_TL model was initially trained using the simulated datasets generated by RTM to gain better model initiation and generalization and further tuned using the standard references from collocated active sensor products through transfer learning (TL) method. Validation on independent dataset shows CNN_TL-retrieved cloud top heights agreeing well with active sensor products for both overlaying ice and underlying water (RMSEs: 1.219 and 0.863 km), substantially outperforming AHI official products. Additionally, due to separate retrieval of ice and water microphysical properties in multilayer clouds, CNN_TL retrieved overlying ice microphysical properties exhibited a substantial correlation with active sensor products, showing Pearson coefficients above 0.78. Notably, CNN_TL model outperformed the purely ML-based model, demonstrating the advantage of integrating physical RTM simulation and ML techniques through TL method. Finally, a novel investigation into the spatial distribution of multilayer cloud properties across full disk is conducted, revealing distinct seasonal variations and notable latitudinal dependencies.

Supercooled water clouds are prevalent in the atmosphere and crucial for global and local radiation balance, aviation safety, and weather modification techniques like artificial precipitation. Therefore, there is an imperative need for the continuous and precise monitoring of supercooled water clouds at high temporal and spatial resolution, encompassing observations under all sky conditions. This study aims to enhance the identification of supercooled water clouds by leveraging thermal infrared channels of the Himawari-8 geostationary satellite, regardless of solar illumination or sun glint effects, which can be problematic for reflectivity bands. Principal component analysis is utilized to perform a sensitivity analysis on a dataset comprising thermal infrared bands from the Himawari-8 satellite and labels derived from the CALIPSO cloud profile products, in order to assess the efficacy of the data in differentiating between supercooled water and ice clouds. Subsequently, machine learning techniques are employed to develop an all-day supercooled water cloud identification model. The model is assessed using a time-independent dataset, yielding an overall accuracy rate for cloud phase identification of over 90%, as well as high performance for detecting supercooled water clouds. The model demonstrates consistent performance across various surfaces, times of day, and seasons. Notably, it outperforms traditional algorithms that rely on reflectivity bands by accurately identifying supercooled water clouds even in sun glint regions, thus improving the reliability of cloud phase detection for applications in meteorology, climate research, and aviation safety.

Clouds play a crucial role in the Earth's energy budget and the hydrological cycle. However, differences in the spatiotemporal resolution of satellite sensors and in retrieval algorithms lead to substantial heterogeneity among retrieved cloud products. Based on global geostationary satellite thermal infrared brightness temperature data from the Gridded Satellite (GridSat-B1) project, this study applied the single-layer Cloud retrieval model – Small Attention-UNet (Cloud-SmaAtUNet) algorithm in the DaYu CLoud Analysis System (DaYu-CLAS) to retrieve a global cloud product with a 3 h temporal resolution, 0.07° spatial resolution, and 23 year temporal span (2000–2022). This product is referred to as the DaYu Global Cloud physical properties Products (DaYu-GCP). The DaYu-GCP includes CLoud Phase (CLP), Cloud Top Height (CTH), Cloud Optical Thickness (COT), and Cloud Effective Radius (CER), covering all regions between 70° S–70° N and 180° W–180° E. Evaluation based on the Moderate-resolution Imaging Spectroradiometer (MODIS) official cloud products shows that the annual CLP identification accuracy of DaYu-GCP remains stable at 85 % ± 0.7 %, while the annual RMSE for CTH, COT, and CER stabilize at 1.50 ± 0.03 km, 10.71 ± 0.15, and 6.75 ± 0.10 µm, respectively. The multi-year variations in accuracy are within 2 %, with no evident interannual differences, and the spatiotemporal distributions are continuous. In addition, evaluation based on observations from the Cloud Profiling Radar and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) indicates that the DaYu-GCP products show reasonable day–night consistency for optically thin cloud. Furthermore, the DaYu-GCP products are compared with other global cloud products. Taking the Northern Hemisphere as an example, the interannual variations of Cloud Cover Frequency (CCF), CTH, COT, and CER retrieved from DaYu-GCP show correlation coefficients of 0.760, 0.486, 0.764, and 0.514 with the ISCCP product, respectively, and 0.444, 0.778, 0.171, and 0.412 with the CLARA-A3 product. The DaYu-GCP dataset, which is stored in the Network Common Data Format (NetCDF), is freely available on the Science Data Bank at https://doi.org/10.57760/sciencedb.26292 (Zhao et al., 2026). The corresponding code can be found at https://github.com/lingxiao-zhao/DaYu-GCP (last access: 25 June 2025).