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Tropics nurture three different types of convective clouds, i.e., shallow cumulus, cumulus congestus, and deep cumulonimbus. The vertical structure of clouds holds a crucial metric in studying tropical clouds. Ground-based high-resolution cloud radar measurements are the potential candidate in exploring the characteristics of various types of tropical clouds and their evolution. Quality-controlled cloud radar data containing a total of five million vertical profiles of equivalent reflectivity factor (VPR) are used to examine the intra-seasonal variation of cloud vertical structure (VSC) during the Indian summer monsoon (ISM) over Mandhardev (18.04° N, 73.87° E, and ~ 1.3 km AMSL) in the Indian Western Ghats. The cumulus congestus (Cc) in the transition of shallow to deep clouds is investigated for the first time using the hourly VPR data for 60 consecutive ISM days. Mid-level moistening plays a vital role in this non-precipitating shallow to precipitating congestus transformation and increment of the rain accumulation. Low cloud reflectivity distribution can distinguish precipitating and non-precipitating clouds that help to classify the observed monsoon as normal or below normal. More than 150 mm of rain accumulation during ISM is associated with more than 22% of high clouds. This particular aspect indicates that cold rain processes are essential to assess the ISM over the observational site. FFT analysis on the time series of low-, mid-, and high-level cloud regions with the VPR shows prominent intra-seasonal variability of 5–10, 10–20, and 30–60 days periodicities. This study highlights the significance of VSC over tropics pertinent to the monsoon large scale atmospheric condition.

The cloud vertical structure (CVS) is important, yet operational CVS products depend on active observation or reanalysis fields, limiting high‐frequency monitoring. In this study, we propose a lightweight and satellite‐only model that reconstructs volumetric cloud masks from geostationary multispectral imagery. This method employs a compact one‐dimensional convolutional neural network that combines three convolutional layers, channel attention and L1 regularization, which is trained on CALIPSO/CloudSat joint profiles and Himawari‐8 multispectral observations. The network produces per‐pixel 38‐layer cloud masks at 500 m vertical resolution and attains strong performance (Intersection over Union = 0.8730; mean absolute error of cloud thicknes = 0.4651 km; cloud top height bias ≈453.25 m). Ablation experiments demonstrate that the chosen architecture and regularization considerably improve layer discrimination. A case study of Typhoon Yutu shows that the reconstructed three‐dimensional structure is consistent with active‐sensor profiles. This observation‐only retrieval reconstructs CVS independent of meteorological inputs, avoiding potential double‐use of geostationary data.

Multilayer clouds, comprising vertically stacked cloud layers with distinct microphysical characteristics, constitute a critical yet complex atmospheric phenomenon influencing regional to global climate patterns. Advances in observational techniques, particularly the application of high-resolution humidity vertical profiling via radiosondes, have significantly enhanced multilayer cloud detection capabilities. Multilayer clouds are widely distributed around the world, showing significant regional differences. Many studies have been carried out on the formation mechanism of multilayer clouds, and observational evidence indicates a close relationship between multilayer cloud development and water vapor supply, updraft, atmospheric circulation, as well as wind shear; however, a unified and comprehensive theoretical framework has not yet been constructed to fully explain the underlying mechanism. In addition, the unique vertical structure of multilayer clouds exhibits different climate effects when compared with single-layer clouds, affecting global climate patterns by regulating precipitation processes and radiative energy budgets. This article reviews the research progress related to multilayer cloud observations and their climate effects and looks forward to the research that needs to be carried out in the future.

Clouds are important for Earth’s energy budget and water cycles, and precisely characterizing their vertical structure is essential for understanding their impact. Although passive remote sensing offers broad coverage and high temporal resolution, sensor and algorithmic limitations impede the accurate depiction of cloud vertical profiles. To improve estimates of their key structural parameters, e.g., cloud top height (CTH) and cloud vertical extent (CVE), we propose a multi-source collaborative optimization algorithm. The algorithm synergizes the wide-coverage FY-4A (FengYun-4A) and DSCOVR (Deep Space Climate Observatory) cloud products with high-precision CloudSat vertical profile data and establishes LightGBM-based CTH/CVE optimization models. The models effectively reduce systematic errors in the FY-4A and DSCOVR cloud products, lowering the CTH Mean Absolute Error (MAE) to 1.8 km for multi-layer clouds, an improvement of 4–8 km over the original. The CVE MAEs for single- and multi-layer clouds are ~2.5 km. Some bias remains in complex cases, e.g., multi-layer thin clouds at low altitudes, and error tracing analysis suggests this may be related to cloud layer number misclassification. The proposed algorithm facilitates daytime near-hourly cloud retrievals over China and neighboring regions.

Cloud fraction is a great source of uncertainty in current models. By utilizing cloudiness fields from CloudSat/cloud‐aerosol lidar and infrared pathfinder satellite observations (CALIPSO), the three widely used reanalyses including the Interim ECWMF Re‐Analysis (ERA‐Interim), Japanese 55‐yar Reanalysis (JRA‐55), and the Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2) are assessed for their representation of cloudiness. Results show all three reanalyses can basically capture the cloud horizontal pattern and vertical structure as in CloudSat/CALIPSO, yet the magnitude is markedly underestimated, in particular for JRA‐55 and MERRA‐2. Besides, all reanalyses struggle to simulate the mid‐level clouds at low latitudes. In addition to these common deficiencies, the three reanalyses have their own distinctive behaviors and differ from one another. While ERA‐Interim and JRA‐55 show better performance for low‐level clouds in the tropics, they exhibit remarkable underestimation for high‐level clouds. On the contrary, MERRA‐2 succeeds in representing high‐level clouds but dramatically underestimates the low and mid‐level clouds at low latitudes. As a measure of subgrid‐scale variability of moisture, the derived “critical relative humidity (RH c)” from CloudSat/CALIPSO exhibits distinctive vertical structures at different latitudes, it is thus speculated that poor specification or parameterization of RH c is responsible for these bias behaviors. Fractional cloudiness remains great uncertainty in the modeling community. The figure displays the cloud vertical structures along the Global Energy and Water Cycle Experiment Cloud System Study/Working Group on Numerical Experimentation (GCSS/WGNE) Pacific Cross‐Section Intercomparison (GPCI) transect in CloudSat/CALIPSO and the three reanalyses. In general, the transition from stratocumulus to shallow cumulus and eventually to deep convection with increasing SST from the west coast of California to the equator is well captured in all reanalyses; yet owe their distinct advantages and disadvantages. The low‐level stratocumulus clouds in the west of California are well reproduced in ERA‐Interim, whereas only marginally observed in Japanese 55‐yar Reanalysis (JRA‐55) and Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2). The high‐level cirrus clouds in the tropics are well represented in MERRA‐2, but remain significant underestimation in ERA‐Interim and JRA‐55. This points to the model deficiency in representing interactions between boundary layer turbulence, shallow and deep convections, and stratiform condensation process.

Layered cloud cover (LCC), that is, cloud cover at different levels, is crucial for estimating cloud radiative effects and modeling climate change. However, accurate LCC characterization using passive satellite measurements is challenging because of the difficulties in resolving cloud vertical structures. In this study, we developed a novel method to estimate LCC from geostationary satellite radiometric measurements. The proposed method resolves cloud vertical structures by retrieving cloud-top and cloud-base heights for both single- and multi-layer clouds; thus, better estimating LCC. Our results agreed well with active satellite measurements, showing identification accuracies of 86%, 90%, and 91% for high, medium, and low clouds, respectively. Additionally, our LCC estimates derived from satellite measurements were used to evaluate those from atmospheric reanalysis. The annual averaged total, high, medium, and low cloud covers given by our methods were 0.681, 0.393, 0.356, and 0.455, respectively, while those from ERA-5 were 0.623, 0.415, 0.274, and 0.392, respectively. These results indicate that the total cloud cover determined by ERA-5 was lower than that derived from satellite measurements, potentially as a result of medium and low-level clouds.

The eastern slope of the Tibetan Plateau is a crucial corridor of water-vapor transport from the Tibetan Plateau to Eastern China. This is also a region with active cloud initiation, and the locally hatched cloud systems have a profound impact on the radiation budget and hydrological cycle over the downstream Sichuan Basin and the middle reach of the Yangtze River. It is noteworthy that there is a strong diversification in the characteristics and evolution of the ESTP cloud systems due to the complex terrain. Therefore, in this study, ground-based Ka-band millimeter-wave cloud radar measurements collected at the Ganzi (GZ), Litang (LT), Daocheng (DC), and Jiulong (JL) sites of the ESTP in 2019 were analyzed to compare the vertical structures of summer nonprecipitating clouds, including cloud occurrence frequency, radar reflectivity factor, cloud base height, cloud top height, and cloud thickness. The occurrence frequency exhibits two peaks on the ESTP with maximum values of ~20% (2–4 km) and 15% (7–9 km), respectively. The greatest (smallest) occurrence frequency occurs in the JL (GZ). The cloud occurrence frequency of all sites increases rapidly in the afternoon, and the occurrence frequency of the DC presents larger values at 2–4 km. In contrast, the occurrence frequency in the JL shows another increase from 2000 LT to midnight at 7–11 km. Stronger radar echoes occur most frequently in the LT at 5–7 km, and hydrometeor sizes and phase states vary dramatically in mixed-phase clouds. A small number of radar echoes occur at midnight in the JL. A characteristic bimodality of the cloud base height and top height for single-layer, double-layer, and triple-layer clouds was observed. Clouds show a higher base height in the GZ and higher top height in the JL. The ESTP is dominated by thin clouds with thicknesses of 200–400 m. The cloud base height, top height, and thickness exhibit an increase in the afternoon, and higher top height occurs more frequently from midnight to the next early morning in the JL because of its mountain-valley terrain.

Five years (2006–2010) of clouds and earth’s radiant energy system (CERES) and CloudSat data have been analyzed to examine the role of vertical structure of cloud microphysical properties on cloud radiative forcing (CRF) parameters at the top-of-the atmosphere over the Asian monsoon region during the summer monsoon season (June–September) and the Pacific warm pool region during April. Vertical profile of cloud properties (optical depth, cloud liquid water content and cloud ice water content) derived from CloudSat data has been used for the present analysis. Shortwave, longwave and net CRF derived from the CERES data have been used. The results suggest an imbalance between shortwave cloud radiative forcing and longwave cloud radiative forcing over the Asian monsoon region consistent with the results reported earlier. The present analysis suggests that over the Bay-of-Bengal (BoB), vertical profile of cloud microphysical properties determine more than 50 % of variance in CRF. However, over the Pacific warm pool region, cloud microphysical property profiles does not contribute significantly to variance in net CRF (<10 %). Over the BoB, large asymmetry between shortwave and longwave CRF is caused by large amounts of cloud liquid water content in the layer between the surface and 9 km. The present study highlights the importance of accurate representation of cloud microphysical properties in determining the influence of clouds on the radiative balance over the top-of-the atmosphere.

In this study, we investigate the seasonal and diurnal variations in cloud occurrence frequency, as well as cloud vertical structure (CVS) characteristics under different seasons and precipitation intensities over the Guangzhou region in South China, based on the analysis of millimeter-wave cloud radar (MMCR) and ground automatic weather station rainfall observations from May 2019 to August 2021. The results showed that the occurrence frequency of clouds exhibits a bimodal distribution throughout the year, with peaks in March to June and October, reaching its highest occurrence in May at approximately 80% and its lowest from December to February at around 40%. Additionally, there are distinct diurnal variations in occurrence frequency, with the lowest rates occurring around 0005 LST, rapidly increasing after 0006 LST, and peaking during the afternoon to evening hours. Cloud top height (CTH) shows bimodal distributions during the pre-flood and post-flood seasons. The most frequently occurring range of CTH during the pre-flood season is below 3 km, accounting for approximately 43%, while during the post-flood season, it ranges from 11 to 14 km, constituting about 37%. For precipitation clouds, CTH can extend beyond 12 km, with the radar reflectivity decreasing gradually with increasing height. The highest frequencies of radar echoes are observed below 2 km and between 4 and 7 km, exhibiting clear diurnal variations, with echoes mainly below 2 km and between 4 to 6 km during the early morning, intensifying and shifting to higher altitudes during the day and reaching their maximum below 4 km during the afternoon to nighttime hours, while both the frequency and intensity increase in the height range of 4 to 12 km. Vertical profiles of radar reflectivity and cloud ice/liquid water content (IWC/LWC) exhibit similar trends under different precipitation intensities. The main differences are observed below 4 km, where both radar reflectivity and IWC/LWC generally increase with increasing precipitation intensity. These findings contribute to a better understanding of cloud characteristics in the South China region, enhance the accuracy of model simulations, and provide a scientific basis for accurate forecasting and warning of meteorological disasters.

A macro-vertical structure is closely related to weather evolution and the energy budget balance of the atmospheric system of the Earth. In this study, radiosonde data were used to identify a cloud vertical structure (CVS) using the adjusted relative humidity threshold method. To evaluate the reliability and stability of this method, the results obtained based on the spatiotemporal matching criteria established in this study were compared with Ka-band millimetre-wave cloud radar (MMCR) observation data. This comparison showed that both devices exhibit high consistency in low-level cloud detection. With the increase in the cloud height, the frequency of the cloud appearance detection by the radiosonde became higher than that by the MMCR. In spring, the results of the CVS detection by the two devices were in good agreement. Specifically, the determination coefficients of the modified degrees of freedom (adjusted R-square) of the cloud base height (CBH) and cloud top height (CTH) detected by the two devices were 0.934 and 0.879, respectively. The horizontal drift of the radiosonde was the smallest in summer, and the adj. R-square values of the CBH and CTH were 0.814 and 0.852, respectively. The CVS observation results by the radiosonde and the MMCR were significantly different in autumn (the adj. R-Square values of the CBH and CTH were 0.715 and 0.629, respectively). In winter, the adj. R-Square values of the CBH and CTH observed by the radiosonde and the MMCR were 0.958 and 0.710, respectively. The statistics and analysis of the results of the distribution characteristics of the CVSs using radiosonde data from 2019 to 2021 from Xi’an showed that the average CTH and CBH were at 7–10 km and 3–5 km, respectively. The frequencies of the cloud absence, rainfall, and two- and three-layer clouds were the highest in the winter (34.36%), autumn (12.99%), and summer, respectively.