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The cloud classification algorithm widely used in the International Satellite Cloud Climatology Project (ISCCP) tends to underestimate low clouds over the Tibetan Plateau (TP), often mistaking water clouds for high‐level clouds. To address this issue, we propose a new algorithm based on cloud‐top temperature and optical thickness, which we apply to TP using Advanced Himawari Imager (AHI) geostationary satellite data. Compared with Clouds and the Earth's Radiant Energy System cloud‐type products and ISCCP results obtained from AHI data, this new algorithm markedly improved low‐cloud detection accuracy and better aligned with cloud phase results. Validation with lidar cloud‐type products further confirmed the superiority of this new algorithm. Diurnal cloud variations over the TP show morning dominance shifting to afternoon high clouds and evening mid‐level clouds. Winter is dominated by high clouds, summer by mid‐level clouds, spring by daytime low clouds and nighttime high clouds, and autumn by low and mid‐level clouds. Plain Language Summary The accurate identification of low clouds over the Tibetan Plateau (TP) is crucial for climate regulation, ecosystems, aviation safety, research, and modeling. However, satellite‐based methods often miss these clouds, misclassifying them as high‐level clouds. To remedy this, we developed a new algorithm using cloud‐top temperature and optical thickness, applied to Advanced Himawari Imager data. This significantly improves low‐cloud detection, better aligning with actual cloud phases. Simultaneously, we analyzed diurnal cloud variations over the TP with the new algorithm. Cloud types at different altitudes in the TP exhibit strong seasonality. The dominant cloud types in winter and summer are high and mid‐level, respectively. In spring, low clouds dominate during the day (2:00–10:00 UTC), transitioning to high clouds at night (10:00–18:00 UTC), with mid‐level clouds prevailing at other times. In autumn, low clouds dominate during the day, transitioning to mid‐level clouds at other times, with fewer occurrences of high clouds. Key Points Employing cloud‐top temperature instead of pressure resolves classification‐phase inconsistencies for clouds in the Tibetan Plateau (TP) Lidar validation shows new algorithm's low cloud detection outperforms the conventional International Satellite Cloud Climatology Project algorithm for both TP and plains The study reveals significant diurnal and seasonal variations in low clouds over the TP

The cloud type product 2B-CLDCLASS-LIDAR based on CloudSat and CALIPSO from June 2006 to May 2017 is used to examine the temporal and spatial distribution characteristics and interannual variability of eight cloud types (high cloud, altostratus, altocumulus, stratus, stratocumulus, cumulus, nimbostratus, and deep convection) and three phases (ice, mixed, and water) in the Arctic. Possible reasons for the observed interannual variability are also discussed. The main conclusions are as follows: (1) More water clouds occur on the Atlantic side, and more ice clouds occur over continents. (2) The average spatial and seasonal distributions of cloud types show three patterns: high clouds and most cumuliform clouds are concentrated in low-latitude locations and peak in summer; altostratus and nimbostratus are concentrated over and around continents and are less abundant in summer; stratocumulus and stratus are concentrated near the inner Arctic and peak during spring and autumn. (3) Regional averaged interannual frequencies of ice clouds and altostratus clouds significantly decrease, while those of water clouds, altocumulus, and cumulus clouds increase significantly. (4) Significant features of the linear trends of cloud frequencies are mainly located over ocean areas. (5) The monthly water cloud frequency anomalies are positively correlated with air temperature in most of the troposphere, while those for ice clouds are negatively correlated. (6) The decrease in altostratus clouds is associated with the weakening of the Arctic front due to Arctic warming, while increased water vapor transport into the Arctic and higher atmospheric instability lead to more cumulus and altocumulus clouds.

A cloud clustering and classification algorithm is developed for a ground-based Ka-band radar system in the vertically pointing mode. Cloud profiles are grouped based on the combination of a time-height clustering method and the k -means clustering method. The cloud classification algorithm, developed using a fuzzy logic method, uses nine physical parameters to classify clouds into nine types: cirrostratus, cirrocumulus, altocumulus, altostratus, stratus, stratocumulus, nimbostratus, cumulus or cumulonimbus. The performance of the clustering and classification algorithm is presented by comparison with all-sky images taken from January to June 2014. Overall, 92% of the cloud profiles are clustered successfully and the agreement in classification between the radar system and the all-sky imager is 87%. The distribution of cloud types in Beijing from January 2014 to December 2017 is studied based on the clustering and classification algorithm. The statistics show that cirrostratus clouds have the highest occurrence frequency (24%) among the nine cloud types. High-level clouds have the maximum occurrence frequency and low-level clouds the minimum occurrence frequency.

Cloud classification is not only important for weather forecasts, but also for radiation budget studies. Although cloud mask and classification procedures have been proposed for Himawari-8 Advanced Himawari Imager (AHI), their applicability is still limited to daytime imagery. The split window algorithm (SWA), which is a mature algorithm that has long been exploited in the cloud analysis of satellite images, is based on the scatter diagram between the brightness temperature (BT) and BT difference (BTD). The purpose of this research is to examine the usefulness of the SWA for the cloud classification of both daytime and nighttime images from AHI. We apply SWA also to the image data from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra to highlight the capability of AHI. We implement the cloud analysis around Japan by employing band 3 (0.469 μm) of MODIS and band 1 (0.47 μm) of AHI for extracting the cloud-covered regions in daytime. In the nighttime case, the bands that are centered at 3.9, 11, 12, and 13 µm are utilized for both MODIS and Himawari-8, with somewhat different combinations for land and sea areas. Thus, different thresholds are used for analyzing summer and winter images. Optimum values for BT and BTD thresholds are determined for the band pairs of band 31 (11.03 µm) and 32 (12.02 µm) of MODIS (SWA31-32) and band 13 (10.4 µm) and 15 (12.4 µm) of AHI (SWA13-15) in the implementation of SWA. The resulting cloud mask and classification are verified while using MODIS standard product (MYD35) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data. It is found that MODIS and AHI results both capture the essential characteristics of clouds reasonably well in spite of the relatively simple scheme of SWA based on four threshold values, although a broader spread of BTD obtained with Himawari-8 AHI (SWA13-15) could possibly lead to more consistent results for cloud-type classification than SWA31-32 based on the MODIS sensors.

Stratocumulus occur in closed- or open-cell states, which tend to be associated with high or low cloud cover and the absence or presence of precipitation, respectively. Thus, the transition between these states has substantial implications for the role of this cloud type in Earth’s radiation budget. In this study, we analyze transitions between these states using an ensemble of 127 large-eddy simulations, covering a wide range of conditions. Our analysis is focused on the behavior of these clouds in a cloud fraction ( f c ) scene albedo ( A ) phase space, which has been shown in previous studies to be a useful framework for interpreting system behavior. For the transition from closed to open cells, we find that precipitation creates narrower clouds and scavenges cloud droplets for all f c . However, precipitation decreases the cloud depth for f c > 0.8 only, causing a rapid decrease in A . For f c < 0.8, the cloud depth actually increases due to mesoscale organization of the cloud field. As the cloud deepening balances the effects of cloud droplet scavenging in terms of influence on A , changes in A are determined by the decreasing f c only, causing a linear decrease in A for f c < 0.8. For the transition from open to closed cells, we find that longwave radiative cooling drives the cloud development, with cloud widening dominating for f c < 0.5. For f c > 0.5, clouds begin to deepen gradually due to the decreasing efficiency of lateral expansion. The smooth switch between cloud widening and deepening leads to a more gentle change in A compared to the transitions under precipitating conditions.

Cloud is an important factor that affects weather and climate, and the vertical distribution of cloud determines its role in the atmospheric radiation transfer process. In this paper, the characteristics of different cloud types and their vertical cloud base height distributions over Eastern China are investigated with a four-year 2B-CLDCLASS-LIDAR product. The intercomparison of cloud base height distribution from ground-based lidar, CloudSat and CALIPSO measurements was studied with observations over the Hefei and Jinhua areas. The 2B-CLDCLASS-LIDAR product has the potential to uncover geographical and seasonal changes in cloud base height distribution over the Hefei area and Jinhua area, which may be beneficial for local climate models, although the CPR on CloudSat suffers from surface clutter or blind-zones. The results show that for non-precipitation cloud over the defined region (Eastern China), the occurrence frequencies of altocumulus, stratocumulus, and cirrus clouds are 29.4%, 21.0%, and 18.9%, respectively. The vertical occurrence frequencies of their cloud base heights are 0.5–8.5 km, below 3.5 km, and 5.5–17.0 km. The precipitation clouds are dominated by nimbostratus (48.4%), cumulus (17.9%), and deep convective clouds (24.2%), and their cloud base heights are all below 3.0 km. The cloud base height distributions have large differences below 3 km between the satellite measurement and ground-based measurement over Hefei site. Between the Hefei site and Jinhua site, the difference in cloud base height distribution measured by ground-based lidar is in good agreement with that measured by satellite over their matched grid boxes. Over the Hefei site, the vertical occurrence frequencies of cloud base height measured by ground-based lidar are higher than the satellite measurement within 0–0.5 km during all the seasons. It is suggested that more cloudy days may result from the sufficient water vapor environment in Hefei. In summer, the occurrence frequency of the cloud base height distribution at a height of 0–2.0 km is lower than other seasons over Jinhua city, which may be associated with the local weather system. Over the Jinhua site, the difference in seasonal cloud base height distribution based on satellite is in good agreement with that based on ground-based lidar. However, it does not appear over Hefei site. Thus, a multi-platform observation of cloud base height seems to be one of the essential ways for improvement in the observation of cloud macroscopic properties.

As a basic property of cloud, accurate identification of cloud type is useful in forecasting the evolution of landfalling typhoons. Millimeter-wave cloud radar is an important means of identifying cloud type. Here, we develop a fuzzy logic algorithm that depends on radar range-height-indicator (RHI) data and takes into account the fundamental physical features of different cloud types. The algorithm is applied to a ground-based Ka-band millimeter-wave cloud radar. The input parameters of the algorithm include average reflectivity factor intensity, ellipse long axis orientation, cloud base height, cloud thickness, presence/absence of precipitation, ratio of horizontal extent to vertical extent, maximum echo intensity, and standard variance of intensities. The identified cloud types are stratus (St), stratocumulus (Sc), cumulus (Cu), cumulonimbus (Cb), nimbostratus (Ns), altostratus (As), altocumulus (Ac) and high cloud. The cloud types identified using the algorithm are in good agreement with those identified by a human observer. As a case study, the algorithm was applied to typhoon Khanun (1720), which made landfall in south-eastern China in October 2017. Sequential identification results from the algorithm clearly reflected changes in cloud type and provided indicative information for forecasting of the typhoon.

Changes in aerosol amount and size distribution significantly impact cloud droplet size distribution, as aerosols act as cloud condensation nuclei (CCNs) and influence the relative dispersion (ε) of cloud droplet spectra. Relative dispersion plays a key role in parameterizing cloud processes in general circulation models (GCMs) and microphysical schemes, affecting precipitation estimates and climate predictions. However, the effects of varying aerosol modes on cloud microphysics remain debated, depending on thermodynamic conditions and cloud type. This study simulates a warm stratiform cloud in Jiangxi, China, using the Weather Research and Forecasting (WRF) Spectra–Bin Microphysics scheme (SBM-FAST) from 18:00 on 24 December 2014 to 06:00 on 25 December 2014 (UTC). Satellite and aircraft observations were used to validate the simulation, showing good agreement in cloud structure. Sensitivity experiments were conducted by increasing nucleation, accumulation, and coarse-mode aerosols 5-fold and by reducing the total aerosol concentration to 1/5 of the control. Results show that higher aerosol concentrations enhance cloud formation and broaden droplet spectra, while lower concentrations suppress cloud development. Accumulation-mode aerosols increase small-droplet concentrations, while nucleation- and coarse-mode aerosols favor larger droplets. The correlation between ε and volume-weighted radius (Rv) shifts from positive to negative as Rv increases. This transition is driven by cloud droplet collision–coalescence, condensation, and activation. Increased accumulation-mode aerosol concentrations shift the ε–Rv correlation from negative to positive in the Rv range of 4.5–8 µm, while reduced aerosol concentrations strengthen the negative correlation. Regardless of different coalescence intensities, ε converges with the increase in number concentration of cloud droplets (Nc).

Marine cloud brightening (MCB) has been proposed as a viable way to counteract global warming by artificially increasing the albedo and lifetime of clouds via deliberate seeding of aerosol particles. Stratocumulus decks, which cover wide swaths of Earth’s surface, are considered the primary target for this geoengineering approach. The macroscale properties of this cloud type exhibit a high sensitivity to cloud microphysics, exposing the potential for undesired changes in cloud optical properties in response to MCB. In this study, we apply a highly detailed Lagrangian cloud model, coupled to an idealized parcel model as well as a full three-dimensional large-eddy simulation model, to show that the choice of seeded particle size distribution is crucial to the success of MCB, and that its efficacy can be significantly reduced by undesirable microphysical processes. The presence of even a small number of large particles in the seeded size spectrum may trigger significant precipitation, which will reduce cloud water and may even break up the cloud deck, reducing the scene albedo and hence counteracting MCB. On the other hand, a seeded spectrum comprising a large number of small particles reduces the fraction of activated cloud droplets and increases entrainment and evaporation of cloud water, which also reduces the efficiency of MCB. In between, there may exist an aerosol size distribution that minimizes undesirable microphysical processes and enables optimal MCB. This optimal size distribution is expected to be case dependent.

In this study,cloud base height（CBH） and cloud top height（CTH） observed by the Ka-band（33.44 GHz） cloud radar at the Boseong National Center for Intensive Observation of Severe Weather during fall 2013（September-November） were verified and corrected.For comparative verification,CBH and CTH were obtained using a ceilometer（CL51） and the Communication,Ocean and Meteorological Satellite（COMS）.During rainfall,the CBH and CTH observed by the cloud radar were lower than observed by the ceilometer and COMS because of signal attenuation due to raindrops,and this difference increased with rainfall intensity.During dry periods,however,the CBH and CTH observed by the cloud radar,ceilometer,and COMS were similar.Thin and low-density clouds were observed more effectively by the cloud radar compared with the ceilometer and COMS.In cases of rainfall or missing cloud radar data,the ceilometer and COMS data were proven effective in correcting or compensating the cloud radar data.These corrected cloud data were used to classify cloud types,which revealed that low clouds occurred most frequently.