This study presents a framework based on conditional diffusion probabilistic models for reconstructing vertical cloud structures from passive satellite remote sensing observations. The retrieval is formulated as a conditional denoising diffusion process, in which randomly initialized latent fields are progressively refined through iterative sampling guided by Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. Compared with generative adversarial network (GAN)-based approaches, the proposed model more effectively captures the intrinsic variability, multiscale organization, and stochastic nature of atmospheric cloud fields. It exhibits superior performance in reconstructing complex multilayer systems, intense convective structures, and mesoscale cloud features. Quantitative evaluation against CloudSat radar reflectivity data demonstrates that the method consistently attains high structural similarity and accurately reproduces the vertical distribution of cloud reflectivity. These findings indicate that conditional diffusion probabilistic models provide a novel generative modeling paradigm for atmospheric remote sensing, providing physically consistent reconstructions, quantitative uncertainty characterization, and robust generalization to diverse atmospheric regimes. Furthermore, the framework can be extended to the three-dimensional reconstruction of other meteorological variables, supporting broader application of generative AI in satellite-based atmospheric analyses.

The uncertainty of water cloud feedback on radiative forcing is one of the largest obstacles to producing confident projections of the global climate. Sufficient measurements of water clouds are crucial to addressing this issue. However, existing techniques based on remote sensing or in situ instruments face limitations in data capacity attributed to the short lifetime, high temporal variability, and complex vertical structure of water clouds. In this study, taking advantage of a dual-field-of-view (dual-FOV) high-spectral-resolution lidar (HSRL), we developed a novel algorithm to obtain diurnal simultaneous profiles of water cloud optical and microphysical properties with high temporal-spatial resolution. This technique does not rely on the widely used subadiabatic assumption about the vertical structure of water clouds. The retrieval algorithm, validated by simulations and cloud radar measurements, was applied to field experiment data collected at the Beijing and Hangzhou sites in China. The relationship functions between water cloud properties are presented to enhance our understanding of the underlying processes. Furthermore, the vertical distributions of retrieved properties are compared to the subadiabatic assumption. The dual-FOV HSRL technique enables comprehensive observations, enhancing our understanding of water clouds and providing significant insights into the interactions among clouds, aerosols, precipitation, and radiation.

As a core element of weather and climate change research, convective clouds have complex physical structures and dynamic processes, and knowledge of their evolution is limited by remote sensing data along with detection and tracking algorithms. In this study, we construct convective cloud detection and tracking algorithms for the Himawari-8 AHI data by combining machine learning model, area overlapping, and Kalman filter algorithms. First, we establish complicated and strict spatiotemporal data-matching conditions between the AHI and CloudSat CPR data, and a parallax correction model is developed to minimize the impact of parallax. To expand the sample volume of convective clouds, region growing algorithm is further used. Then, convective clouds during the day and night are detected based on the machine learning model which is trained through feature selection and hyper-parameter optimization. Finally, the automatic tracking of convective clouds, including those with relatively small scales or fast-moving speeds, is achieved by comprehensively using the area overlapping and Kalman filter algorithms. Validation results indicate that the algorithm developed can detect convective clouds of different scales with high accuracy, and the results show good continuity during day to night transitions. Moreover, the convective cloud detection algorithm can capture the convective clouds in advance compared with the traditional threshold algorithm. A case study of convective cloud demonstrates that the tracking algorithms we constructed can track convection at different scales. Overall, our study provides a new approach for convective cloud detection and tracking, which can contribute to better understanding of weather and climate change.

Discovering causal relationships among observed variables is a new research focus in the area of data mining. Methods based on the additive noise model have been proved to be efficient in the identification of cause-effect pairs. However, when trying to determine many-to-one causality, additive noise models often fail to identify the causal direction due to the complex interrelationships and interactions even though the generation of each causal relation follows the additive noise model, and become unreliable in practical applications. In this work, to identify the causal direction, we propose a Hierarchical Additive Noise Model (HANM) to convert many-to-one causality into an approximate one-to-one causality by generalizing multiple factors into an intermediate variable with a variational approach, and use asymmetry in the forward model and backward model of HANM to identify causal direction. Experiments using synthetic data show that many-to-one causality can be effectively identified through asymmetry with our proposed HANM and the accuracy of HANM is higher than the best existing model. By applying the model to real-world data, it can be seen that HANM can greatly augment the application scope of functional causal models for causal discovery.

The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) is a new-generation polar-orbiting satellite imaging sensor. It has generated a variety of operational products similar to the widely used Moderate Resolution Imaging Spectroradiometer (MODIS) products. However, there are high uncertainties in official VIIRS aerosol products based on our previous validations, and a reduction in these uncertainties is needed before they can be used with confidence. To this end, we developed a revised high-spatial-resolution aerosol retrieval algorithm which can considerably improve the aerosol optical depth (AOD) estimations. The improvements mainly arise from: 1) correction of the surface bidirectional reflectance using the RossThick-LiSparse model with parameters obtained from the MODIS bidirectional reflectance distribution function (BRDF)/Albedo products; 2) finer customized monthly aerosol types assumed from the historical Aerosol Robotic Network (AERONET) measurements of optical properties; and 3) improved cloud screening with the revised dynamic threshold cloud detection algorithm. The new 750-m AOD retrievals are validated against AERONET AOD measurements and compared with the official VIIRS AOD products from 2014 to 2017 over the Beijing-Tianjin-Hebei region in China. The results illustrated that the retrievals are highly consistent with ground measurements ($R = 0.926$ ), with ~72% of them falling within the expected error of [±(0.05 + 20%)] on a regional scale. The mean absolute error is 0.082 and the root-mean-square error is 0.120. The new algorithm can significantly reduce the overestimations and improve the aerosol estimations over heterogeneous urban surfaces compared to the official aerosol products, especially in winter. This new VIIRS AOD product will thus be more useful for air pollution studies over medium- or small-scale areas.