Clouds are diverse and complex, making accurate cloud type identification vital in improving the accuracy of weather forecasting and the effectiveness of climate monitoring. However, current cloud classification research has largely focused on daytime data. The lack of visible light data at night presents challenges in characterizing nocturnal cloud attributes, leading to difficulties in achieving continuous all-day cloud classification results. This study proposed an all-day infrared cloud classification model (AInfraredCCM) based on XGBoost. Initially, the latitude/longitude, 10 infrared channels, and 5 brightness temperature differences of the Himawari-8 satellite were selected as input features. Then, 1,314,275 samples were collected from the Himawari-8 full-disk data and cloud classification was conducted using the CPR/CALIOP merged cloud type product as training data. The key cloud types included cirrus, deep convective, altostratus, altocumulus, nimbostratus, stratocumulus, stratus, and cumulus. The cloud classification model achieved an overall accuracy of 86.22%, along with precision, recall, and F1-score values of 0.88, 0.84, and 0.86, respectively. The practicality of this model was validated across all-day temporal, daytime/nighttime, and seasonal scenarios. The results showed that the AInfraredCCM consistently performed well across various time periods and seasons, confirming its temporal applicability. In conclusion, this study presents an all-day cloud classification approach to obtain comprehensive cloud information for continuous weather monitoring, ultimately enhancing weather prediction accuracy and climate monitoring.

As carbon dioxide (CO₂) concentrations continue to rise, climate change, characterized by global warming, presents a significant challenge to global sustainable development. Currently, most global shortwave infrared CO₂ retrievals rely on fully physical retrieval algorithms, for which complex calculations are necessary. This paper proposes a method to predict the concentration of column-averaged CO₂ (XCO₂) from shortwave infrared hyperspectral satellite data, using machine learning to avoid the iterative computations of the physical method. The training dataset is constructed using the Orbiting Carbon Observatory-2 (OCO-2) spectral data, XCO₂ retrievals from OCO-2, surface albedo data, and aerosol optical depth (AOD) measurements for 2019. This study employed a variety of machine learning algorithms, including Random Forest, XGBoost, and LightGBM, for the analysis. The results showed that Random Forest outperforms the other models, achieving a correlation of 0.933 with satellite products, a mean absolute error (MAE) of 0.713 ppm, and a root mean square error (RMSE) of 1.147 ppm. This model was then applied to retrieve CO₂ column concentrations for 2020. The results showed a correlation of 0.760 with Total Carbon Column Observing Network (TCCON) measurements, which is higher than the correlation of 0.739 with satellite product data, verifying the effectiveness of the retrieval method.

The FY-4A satellite represents a new generation of geostationary platforms, providing high-temporal-resolution observations over China. However, challenges remain in effectively leveraging the FY-4A satellite data for high-temporal-resolution PM_2.5 concentration estimation, particularly regarding the unclear key parameters required for accurate estimation and the limited interpretability of models. This study utilizes an interpretable deep learning framework that integrates FY-4A Top-of-Atmosphere (TOA) reflectance data, meteorological variables, and auxiliary data to estimate surface high-temporal-resolution PM_2.5 concentrations from 2019 to 2023. A multicollinearity test was applied to optimize feature selection, while the SHapley Additive exPlanations (SHAP) method was used to enhance model interpretability. The results indicate that parameters such as TOA02, TOA03, TOA04, and boundary layer height (BLH) significantly influence model performance across years. The model demonstrates strong predictive ability in the Beijing–Tianjin–Hebei (BTH) region, achieving an average R² of 0.83. Root mean square error (RMSE) values remained below 15 µg/m³, aligning well with ground-based monitoring data. These findings demonstrate that combining high temporal satellite data with interpretable deep learning provides a reliable approach for long-term, high-temporal-resolution PM_2.5 monitoring in regions.