Most existing cloud base height (CBH) retrieval algorithms are only applicable for daytime satellite observations due to their dependence on visible observations. This study presents a novel algorithm to retrieve day and night CBH using infrared observations of the geostationary Advanced Himawari Imager (AHI). The algorithm is featured by integrating deep learning techniques with a physical model. The algorithm first utilizes a convolutional neural network-based model to extract cloud top height (CTH) and cloud water path (CWP) from the AHI infrared observations. Then, a physical model is introduced to relate cloud geometric thickness (CGT) to CWP by constructing a look-up table of effective cloud water content (ECWC). Thus, the CBH can be obtained by subtracting CGT from CTH. The results demonstrate good agreement between our AHI CBH retrievals and the spaceborne active remote sensing measurements, with a mean bias of −0.14 ± 1.26 km for CloudSat-CALIPSO observations at daytime and −0.35 ± 1.84 km for EarthCARE measurements at nighttime. Additional validation against ground-based millimeter wave cloud radar (MMCR) measurements further confirms the effectiveness and reliability of the proposed algorithm across varying atmospheric conditions and temporal scales.

Severe convective weather events, such as heavy rainfall and flooding, are serious threats to human society. However, accurate nowcasting of these events using radar remains challenging due to the complex dynamics and nonlinear physical processes involved. Moreover, existing radar echo extrapolation methods primarily rely on time‐domain analysis, leaving a considerable gap in capturing frequency‐domain features. Therefore, this paper proposes a Multi‐variable Fusion UNet‐Fourier Neural Operator (MF‐UFNO) model, which combines multiple radar variables through a late‐fusion strategy for extrapolation tasks. The model integrates Fast Fourier Transform to extract frequency‐domain features, enhancing the representation of the spatiotemporal evolution of radar echoes. The MF‐UFNO model is trained and validated on polarimetric radar variables collected from an S‐band dual‐polarization radar between April 2020 and June 2021. Experimental results indicate that the MF‐UFNO model achieves high forecasting accuracy, with statistical Threat Scores exceeding 0.5 and 0.4 for 15 and 25 dBZ thresholds, respectively, over the forecast period. Compared to existing models such as the SmaAt‐UNet model and the Rainymotion model, the proposed model demonstrates superior performance in radar echo extrapolation, particularly in accurately predicting fine‐scale structures within a 60‐min forecast window. With the precise nowcasting capabilities, the MF‐UFNO model can improve the generation of severe convective weather warnings and enhance short‐term weather guidance.

Layered cloud cover (LCC) describes the vertical distribution of cloud occurrence and is a key variable for assessing the radiation budget of the Earth-atmosphere system. However, ground-based radars have limited spatial coverage, while existing satellite cloud-cover products rarely provide both spatiotemporal continuity and high accuracy. Because nighttime satellite observations lack visible-channel information, conventional passive satellite remote sensing remains limited in providing day-night continuous LCC retrievals. In this study, we propose an infrared-based framework for retrieving large-scale day-night LCC from geostationary satellite observations. The framework first resolves cloud vertical structure using a hybrid machine learning and physical algorithm for day-night cloud-base height (CBH) retrieval, and then derives cloud cover in different vertical layers. Validation against active measurements from spaceborne and ground-based cloud radar demonstrates that the satellite-retrieved LCC captures cloud vertical distributions and their diurnal variations. The cloud-layer identification accuracies reach 76.3% and 77.9% for daytime and nighttime, respectively, with corresponding Cohen’s kappa coefficients of 0.66 and 0.68. The primary source of algorithmic uncertainty is the low precision of low-cloud identification, which is constrained by objective factors and physical characteristics. The retrieved annual mean LCC fields reproduce major climatological features, including enhanced high and deep convective clouds over the tropical western Pacific and dominant low-cloud occurrence over the mid-latitude oceans. A case study of Typhoon Doksuri further shows that the 10 min LCC retrievals capture the vertical evolution of the typhoon cloud system during intensification, eyewall structural adjustment, landfall, and post-landfall decay. These results indicate that the proposed infrared-based retrieval framework provides a promising basis for constructing large-scale day-night LCC datasets and can support cloud-radiation studies, climate-model evaluation, and weather monitoring.