CloudSat observations are used to characterize the morphologies and internal vertical structures of tropical deep convective systems (DCSs) at different life stages, that is, developing, mature, and dissipating stages according to the buoyancy at the cloud top. The DCSs are further partitioned into convective pillar, major anvil, and minor anvil and then discussed separately. The DCS exhibits an apparent mushroom‐like structure in the developing stage, with a horizontally narrow convective pillar and widespread anvil cloud. The convective pillar widens in the mature stage, while the anvil length remains quite similar. In the dissipating stage, the convective pillar shrinks vertically and horizontally, and the anvil length is shorter than that in the other two stages. The life stage has a larger impact on the vertical structure of the ice number concentration () and ice water content (IWC) than on the ice effective radius (). The high area at the top of the convective pillar expands vertically and horizontally as the DCS evolves, indicating that newly formed ice particles are likely to accumulate in the convective pillar and consequently increase the IWC in this section rather than being transmitted to anvil clouds. Both ice and liquid precipitation occur in a narrow region within the pillar during the developing stage, then widen at the mature stage and shrink at the dissipating stage. Our results provide detailed morphologies and internal structures of DCS evolution and would be helpful to improve DCS representation in numerical models.

Low-level clouds (LLC), mainly composed of liquid water droplets, cool the climate system by strongly reflecting solar radiation back to space, and thus play an important role in the Earth energy budget. However, the LLC properties and their radiative effects are poorly represented in climate models, leading to the largest source of uncertainty in the climate prediction. Liquid water content (LWC) is a key property of LLC determining cloud extinction characteristics and is a fundamental parameter in the radiative transfer model. To improve the understanding of LWC properties, algorithms have been proposed to retrieve LWC based on millimeter-wavelength radar. However, the traditional retrieval relies on pre-constructed empirical relationship between reflectivity and LWC and have noticeable limitations. Particularly, the retrieval uncertainty is strongly depended on the assumed particle size distribution, the existence of drizzle particle; and on the accuracy of reflectivity measurement. In this study, we develop a new self-consistent algorithm to retrieve LWC by constraining radar reflectivity factor and attenuation in the whole liquid cloud layer. A relationship between the radar measured reflectivity, LWC, and the intrinsic reflectivity is first constructed based on the radiative transfer theory under Rayleigh scattering regime. A nonlinear least-square regression technique is then applied to derive the optimal parameters in the retrieval equations to obtain the LWC. Comparison with the microwave radiometer (MWR) derived liquid water path (LWP) indicates that our proposed method retrieves more accurate LWC products than that from the traditional empirical algorithms.

Clouds play a pivotal role in the global radiation budget and energy cycle, making accurate cloud detection via satellite remote sensing essential for understanding their evolutions. However, existing machine learning (ML)-based cloud detection algorithms have limitations, particularly in data preprocessing and feature engineering, and few are designed to cover the entire geostationary satellite observation region. In this study, we integrate three deviation elimination schemes for cloud cover differences and various surface type characterization modes to develop an unbiased, high-precision cloud detection algorithm for the Advanced Himawari Imager (AHI) onboard Himawari-8, leveraging automatic machine learning (AutoML) techniques. Additionally, we provide a new perspective on model interpretability by incorporating concepts from cooperative game theory. Our results indicate that cloud detection algorithms constructed separately for different surfaces yield better results than those applied uniformly across the entire region by inputting surface feather. The optimal algorithm achieves unbiased cloud detection across multiple surfaces by adjusting the output threshold of the mapping function, allowing for cloud detection results with varying confidence levels as required. The true skill statistic (TSS) of the optimal AutoML-based cloud detection algorithm is 87.32%, which is 13.72% higher than that of the AHI official cloud mask. The deviation rate (DR) of the optimal algorithm is only 0.79%, significantly lower than the 20.48% of the AHI official cloud mask. Long-term cloud frequency (CF) tests show that our optimal algorithm's CF distribution is more consistent with CALIOP results compared to the AHI official products.