Clouds, as one of the most uncertain factors in climate system, have been intensively studied as satellites with advanced instruments emerged in recent years. However, few studies examine the vertical distributions of cloud top and their temporal variations over East Asia based on geostationary satellite data. In this study, the vertical structures of cloud top and its diurnal variations in summer of 2016 are analyzed using the Advanced Himawari Imager/Himawari‐8 cloud products. Results show that clouds occur most frequently over the southern Tibetan Plateau and the Bay of Bengal. We find a steep gradient of cloud occurrence frequency extending from southwest to northeast China and low‐value centers over the eastern Pacific and the Inner Mongolia Plateau. The vertical structures of cloud top are highly dependent on latitude, in addition to the nonnegligible roles of both terrain and land‐sea thermal contrast. In terms of the diurnal cycle, clouds tend to occur more often in the afternoon, peaking around 1700 local time over land and ocean. The amplitude of cloud diurnal variation over ocean is much smaller than that over land, and complex terrain tends to be linked to larger amplitude. In vertical, the diurnal cycle of cloud frequency exhibits bimodal pattern over both land and ocean. The high‐level peaks occur at almost the same altitude over land and ocean. In contrast, the low‐level peaks over ocean mainly reside in the boundary layer, much lower than those over land, which could be indicative of the frequent occurrence of marine boundary layer clouds.

An optical flow algorithm based on polynomial expansion (OFAPE) was used to derive atmospheric motion vectors (AMVs) from geostationary satellite images. In OFAPE, there are two parameters that can affect the AMV results: the sizes of the expansion window and optimization window. They should be determined according to the temporal interval and spatial resolution of satellite images. A helpful experiment was conducted for selecting those sizes. The limitations of window sizes can cause loss of strong wind speed, and an image-pyramid scheme was used to overcome this problem. Determining the heights of AMVs for semitransparent cloud pixels (STCPs) is challenging work in AMV derivation. In this study, two-dimensional histograms (H2Ds) between infrared brightness temperatures (6.7- and 10.8-μm channels) formed from a long time series of cloud images were used to identify the STCPs and to estimate their actual temperatures/heights. The results obtained from H2Ds were contrasted with CloudSat radar reflectivity and CALIPSO cloud-feature mask data. Finally, in order to verify the algorithm adaptability, three-month AMVs (JJA 2013) were calculated and compared with the wind fields of ERA data and the NOAA/ESRL radiosonde observations in three aspects: speed, direction, and vector difference.

Tropical overshooting convection has a strong impact on both heat budget and moisture distribution in the upper troposphere and lower stratosphere, and it can pose a great risk to aviation safety. Cloud-top height is one of the essential concerns of overshooting convection for both the climate system and the aviation weather forecast. The main purpose of our work is to verify the application of the machine learning method, taking the random forest (RF) model as an instance, in overshooting cloud-top height retrieval from Himawari-8 data. By using collocated CloudSat observations as a reference, we utilize several infrared indicators of Himawari-8 that are commonly recognized to relate to cloud-top height, along with some temporal and geographical parameters (latitude, month, satellite zenith angle, etc.), as predictors to construct and validate the model. Analysis of variable importance shows that the brightness temperature of 6.2 um acts as the dominant predictor, followed by satellite zenith angle, brightness temperature of 13.3 um, latitude, and month. In the comparison between the RF model and the traditional single-channel interpolation method, retrievals from the RF model agree well with observation with a high correlation coefficient (0.92), small RMSE (222 m), and small MAE (164 m), while these metrics from traditional single-channel interpolation method shows lower skills (0.70, 1305 m, and 1179 m). This work presents a new sight of overshooting cloud-top height retrieval based on the machine learning method.

Retrievals of ice cloud properties require accurate estimates of ice particle mass. Empirical mass–dimensional (m–D) relationships in the form $m=a{D}^b$ are widely used and usually universally applied across the complete range of particle sizes. For the first time, the dependence of a and b coefficients in m–D relationships on median mass diameter (D_mm) is studied. Using combined cloud microphysical data collected during the Olympic Mountains Experiment and coincident observations from Airborne Precipitation Radar Third Generation, D_mm-dependent (a, b) coefficients are derived and represented as surfaces of equally plausible solutions determined by some tolerance in the chi-squared difference ${\chi }^2$ that minimizes the difference between observed and retrieved radar reflectivity. Robust dependences of a and b on D_mm are shown with both parameters significantly decreasing with D_mm, leading to smaller effective densities for larger D_mm ranges. A universally applied constant m–D relationship overestimates the mass of large aggregates when D_mm is between 3–6 mm and temperatures are between −15–0 °C. Multiple m–D relations should be applied for different D_mm ranges in retrievals and simulations to account for the variability of particle sizes that are responsible for the mass and thus for the variability of particle shapes and densities.

Three infrared (IR) indicators were included in this study: the 10.8-μm brightness temperature (BT10.8), the BT difference between 12.0- and 10.8-μm (BTD12.0−10.8) and the BT difference between 6.7- and 10.8-μm (BTD6.7−10.8). Correlations among these IR indicators were investigated using MTSAT-1R satellite images in summer 2007 over East Asia. Temporal, spatial and numerical frequency distributions were used to represent the correlations. The results showed that: 1) Large BTD12.0−10.8 values can be observed in the growth of cumulus congestus, and associated with the boundary of different terrain where convection was more likely to generate and develop. 2) The numerical correlation between any two IR indicators could be expressed by two-dimensional histograms (HT2D). Because of differences in the tropopause heights and in the temperature and water vapor fields, shapes of the HT2Ds varied with latitudes and types of underlying surface. After carefully analyzing the correlations among the IR indicato...

Vertically developing convective clouds (VDCCs), characterized by cloud-top ascent and cooling, are critical precursors to severe convective weather due to their association with intense updrafts. However, existing studies are constrained by limited spatiotemporal resolution of data and tracking methodologies, hindering real-time and pixel-level capture of VDCC evolution. Furthermore, large-scale statistical analyses of VDCC spatiotemporal distribution remain scarce compared with mature convective systems, particularly in topographically complex regions like the Tibetan Plateau (TP). To address these challenges, we integrated an optical flow algorithm (for dense atmospheric motion vector (AMV) retrieval) with cloud-top cooling rates (CTCRs, as indicators of vertical development), leveraging the high spatiotemporal resolution and multispectral capabilities of the GEO-KOMPSAT-2A (GK2A) satellite. This approach achieved pixel-level VDCC detection at 10 min intervals across diurnal cycles, enabling comprehensive statistical analysis. Based on this technical foundation, the most important finding in the study was the distinct convective spatiotemporal distribution over the TP and East Asia (EA) by analyzing VDCC detection data in three summers (2021–2023). Specifically, VDCC diurnal peaks preceded precipitation by 2–3 h, confirming their precursor roles in both study regions. Regional comparisons revealed that topographic and thermal forcing strongly influenced VDCC distribution patterns. The TP exhibited earlier and more frequent daytime convection at middle-to-low levels than EA, driven by intense thermal forcing, yet vertical development was limited by moisture scarcity. In contrast, EA’s monsoonal moisture sustained deeper convection, with more VDCCs penetrating the upper troposphere. The detection and statistical studies of VDCCs offer new insights into convective processes over the TP and surrounding regions, offering potential improvements in severe weather monitoring and early warning systems.