Search Results (287)

Supercooled liquid water (SLW) in mixed-phase clouds significantly influences precipitation efficiency and aviation safety. However, a comprehensive understanding of its vertical structure has been hampered by a lack of sustained, vertically resolved observations over the North China Plain. This study presents the first systematic analysis of SLW vertical distribution and microphysics in this region, utilizing a year-long dataset (2022) from synergistic ground-based instruments in Beijing. Our retrieval approach integrates Ka-band cloud radar, microwave radiometer, ceilometer, and radiosonde data, combining fuzzy-logic phase classification with a liquid water content inversion constrained by column liquid water path. Key findings reveal a distinct bimodal seasonality: SLW primarily occurs at mid-to-upper levels (4–7.5 km) during spring and summer, driven by convective lofting, while winter SLW is confined to lower altitudes (1–2 km) under stable atmospheric conditions. The temperature-dependent occurrence probability of SLW clouds has an annual maximum at −12 °C. The diurnal variation in SLW in summer shows peaks in the afternoon and at night, corresponding to convective cloud activity. Spring, autumn, and winter do not exhibit strong diurnal variations. Retrieved microphysical properties, including liquid water content and droplet effective radius, are consistent with in situ aircraft measurements, validating our methodology. This analysis provides a critical observational benchmark and offers actionable insights for improving cloud microphysics parameterizations in models and optimizing weather modification strategies, such as seeding altitude and timing, in this water-stressed region. Full article

Low clouds significantly impact weather, climate, and multiple environmental and economic sectors such as agriculture, fire risk management, aviation, and renewable energy. Accurate knowledge of cloud base height (CBH) is critical for optimizing crop yields, improving fire danger forecasts, enhancing flight safety, and increasing solar energy efficiency. This study evaluates a shadow-based CBH retrieval method using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite visible imagery and compares the results against collocated lidar measurements from the Micro-Pulse Lidar Network (MPLNET) ground stations. The shadow method leverages sun–sensor geometry to estimate CBH from the displacement of cloud shadows on the surface, offering a practical and high-resolution passive remote sensing technique, especially useful where active sensors are unavailable. The validation results show strong agreement, with a correlation coefficient (R) = 0.96 between shadow-based and lidar-derived CBH estimates, confirming the robustness of the approach for shallow, isolated cumulus clouds. The method’s advantages include direct physical height estimation without reliance on cloud top heights or stereo imaging, applicability across archived datasets, and suitability for diurnal studies. This work highlights the potential of shadow-based retrievals as a reliable, cost-effective tool for global low cloud monitoring, with important implications for atmospheric research and operational forecasting. Full article

The thermal forcing of the Tibetan Plateau (TP) significantly influences the Asian summer monsoon. However, its interaction with cloud feedbacks remains unclear due to the limitations of synchronous analysis and traditional cloud classification over the TP. By applying an improved cloud-classification algorithm—which integrates cloud microphysical properties to improve low-cloud detection—to CERES data (2001–2023), we generated a long-term cloud-type dataset. Combined with ERA5 reanalysis data, we systematically analyzed the trends and lead–lag relationships among cloud vertical structure, surface radiation, cloud radiative forcing (CRF), heat fluxes, snowfall, and the TP Monsoon Index (TPMI). Results indicate a vertical cloud redistribution over the TP, with high cloud cover (HCC) decreasing and low cloud cover (LCC) increasing. HCC is strongly synchronized with snowfall and significantly affects surface radiation, while net CRF and sensible heat flux show delayed responses, peaking when HCC leads by about one month. A composite analysis of winter low-HCC events reveals that reduced HCC suppresses snowfall, weakens net CRF, and reduces sensible heat flux after approximately 1–2 months, while the TPMI shows a significant response around month zero. These findings highlight the key role of cloud–radiation–snowfall interactions in modulating TP thermal forcing. Full article

The sudden turn of tropical cyclones (TCs) can rapidly alter the affected disaster-prone regions and associated rainfall distributions, posing severe threats to coastal areas and creating major challenges for operational forecasting. However, most of these events occur over the open ocean, where the scarcity of in situ observations limits our understanding of how precipitation and cloud microphysical processes evolve during the sudden turning. In this study, we analyzed the precipitation evolution and associated microphysical characteristics during the sudden turn of Super Typhoon Vongfong (2014) using the latest GPM satellite observations. The main findings are as follows: (1) During the sudden-turning period, the precipitation coverage expanded significantly. Strong convective precipitation was distributed from the inner eyewall to the outer eyewall and spiral rainbands and weakened in intensity, whereas stratiform precipitation broadened in coverage and intensified. (2) The increase in stratiform precipitation was attributed primarily to increased cloud water content, which strengthened collision–coalescence processes, promoted the formation of larger and more numerous raindrops, and consequently increased precipitation efficiency and intensity. (3) The weakening of convective precipitation was related to the reduction in eyewall updrafts, which suppressed ice-phase processes and limited the development of deep convection. Full article

Stratocumulus clouds are cloud systems composed of stratiform clouds with embedded convective clouds, possessing strong catalytic potential and serving as key target cloud systems for weather modification operations. In this study, the parameterization of ice nucleation for silver iodide (AgI) particles was applied to the Thompson microphysics scheme in the WRF model. Numerical experiments were designed for a stratocumulus cloud that occurred over the Hulunbuir region, northeastern China, on 31 May 2021, to investigate how the structure and evolution of cloud macro- and microphysical properties and precipitation formation respond to glaciogenic seeding. The simulation results indicate that AgI nucleation increased ice concentrations at 4–5 km altitude, enhancing ice crystal formation through condensation–freezing and deposition nucleation and the growth of ice particles through auto-conversion and riming, leading to increased precipitation. The results also show that owing to the non-uniform distribution of supercooled water within this stratocumulus cloud system, the consumption of AgI and the enhanced ice nucleation release latent heat more strongly in regions with higher supercooled water content. This leads to more pronounced isolated updrafts, altering the structure of shear lines and subsequently influencing regional precipitation distribution after silver iodide seeding concludes. These findings reveal that seeding influences both the microphysical and dynamic structures within clouds and highlight the non-uniform seeding effects within cloud systems. This study contributes to a deeper understanding of the effects of artificial seeding on stratocumulus clouds in high-latitude regions and holds significant reference value for artificial weather modification efforts in mixed-phase stratiform clouds. Full article

On the morning of 4 April 2025, a rare formation of Cirrostratus undulatus homomutatus was observed over Barcelona. This variety of the homomutatus form of the Cirrostratus cloud genus—originating from the transformation of persistent aircraft contrails—has not previously been documented in the International Cloud Atlas or in any scientific publication, making this observation unique within the current literature. The event was visually recorded and meteorologically analyzed using upper-air data from the Barcelona radiosonde and the ECMWF ERA5 reanalysis at 300 and 500 hPa geopotential heights. Synoptic and thermodynamic analyses revealed a localized region of enhanced wind shear activity coinciding with a thin, moist layer near the tropopause. These conditions likely facilitated the transformation of persistent contrails into cirriform layers exhibiting undulated patterns characteristic of the undulatus variety. This case provides new insight into the microphysical and dynamic mechanisms underlying the evolution of anthropogenic cirriform clouds, contributing to the growing body of knowledge on homomutatus phenomena and their interaction with upper-tropospheric processes. It thus represents the first formal documentation and meteorological interpretation of Cirrostratus undulatus homomutatus, offering a valuable reference for future observational and classification efforts within the WMO framework. Full article

Warm-cloud hygroscopic seeding is widely used in precipitation enhancement, but the conditions under which seeding amplifies or suppresses rainfall remain unclear. Here, we use a two-dimensional slab-symmetric spectral bin microphysics model from Tel Aviv University to simulate a warm convective cloud that occurred over Hainan, China, on 11 May 2024, and design three sets of sensitivity experiments in which hygroscopic particles of different characteristic diameters are introduced under a fixed-mass injection constraint. We find that seeding with submicrometer particles (0.1–0.9 µm) systematically suppresses precipitation, with the strongest reduction for 0.1 µm particles. When super-micrometer particles (1–9 µm) are used, the precipitation response transitions from suppression to enhancement as particle size increases, and this transition occurs at about 2 µm. Seeding with ultra-giant particles (>10 µm) generally enhances rainfall and also advances its onset, with the enhancement strengthening up to ~60 µm before weakening for even larger particles. We further show that the transitional particle size at which the seeding effect changes sign decreases with increasing background aerosol loading, from maritime to polluted urban conditions. These results identify an environment-dependent critical particle size that governs the sign and efficiency of hygroscopic seeding in warm convective clouds. Full article

Marine low clouds have a strong impact on Earth’s system but remain a major source of uncertainty in anthropogenic radiative forcing simulated by general circulation models. This uncertainty arises from incomplete understanding of the many processes controlling their evolution and interactions. A key feature of these clouds is their diverse mesoscale morphologies, which are closely tied to their microphysical and radiative properties but remain difficult to characterize with satellite retrievals and numerical models. Here, we develop and apply a vision–language model (VLM) to classify marine low cloud morphologies using two independent datasets based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery: (1) mesoscale cellular convection types of sugar, gravel, fish, and flower (SGFF; 8800 total samples) and (2) marine stratocumulus (Sc) types of stratus, closed cells, open cells, and other cells (260 total samples). By conditioning frozen image encoders on descriptive prompts, the VLM leverages multimodal priors learned from large-scale image–text training, making it less sensitive to limited sample size. Results show that the k-fold cross-validation of VLM achieves an overall accuracy of 0.84 for SGFF, comparable to prior deep learning benchmarks for the same cloud types, and retains robust performance under the reduction in SGFF training size. For the Sc dataset, the VLM attains 0.86 accuracy, whereas the image-only model is unreliable under such a limited training set. These findings highlight the potential of VLMs as efficient and accurate tools for cloud classification under very low samples, offering new opportunities for satellite remote sensing and climate model evaluation. Full article

Aerosol–cloud interaction remains one of the largest sources of uncertainty in weather and climate modeling. This study investigates the impacts of aerosols on the macro- and microphysical properties of different cloud types over East Asia, based on nine years of joint satellite observations from CloudSat, CALIPSO, and MODIS, combined with ERA5 reanalysis data. Results reveal pronounced cloud-type dependence in aerosol effects on cloud fraction, cloud top height, and cloud thickness. Aerosols enhance the development of convective clouds while suppressing the vertical extent of stable stratiform clouds. For ice-phase structures, ice cloud fraction and ice water path significantly increase with aerosol optical depth (AOD) in deep convective and high-level clouds, whereas mid- to low-level clouds exhibit reduced ice crystal effective radius and ice water content, indicating an “ice crystal suppression effect.” Even after controlling for 14 meteorological variables, partial correlations between AOD and cloud properties remain significant, suggesting a degree of aerosol influence independent of meteorological conditions. Humidity and wind speed at different altitudes are identified as key modulating factors. These findings highlight the importance of accounting for cloud-type differences, moisture conditions, and dynamic processes when assessing aerosol–cloud–climate interactions and provide observational insights to improve the parameterization of aerosol indirect effects in climate models. Full article

This paper presents Rain-Cloud Condensation Optimizer (RCCO), a nature-inspired metaheuristic that maps cloud microphysics to population-based search. Candidate solutions (“droplets”) evolve under a dual-attractor dynamic toward both a global leader and a rank-weighted cloud core, with time-decaying coefficients that progressively shift emphasis from exploration to exploitation. Diversity is preserved via domain-aware coalescence and opposition-based mirroring sampled within the coordinate-wise band defined by two parents. Rare heavy-tailed “turbulence gusts” (Cauchy perturbations) enable long jumps, while a wrap-and-reflect scheme enforces feasibility near the bounds. A sine-map initializer improves early coverage with negligible overhead. RCCO exposes a small hyperparameter set, and its per-iteration time and memory scale linearly with population size and problem dimension. RCOO has been compared with 21 state-of-the-art optimizers, over the CEC 2022 benchmark suite, where it achieves competitive to superior accuracy and stability, and achieves the top results over eight functions, including in high-dimensional regimes. We further demonstrate constrained, real-world effectiveness on five structural engineering problems—cantilever stepped beam, pressure vessel, planetary gear train, ten-bar planar truss, and three-bar truss. These results suggest that a hydrology-inspired search framework, coupled with simple state-dependent schedules, yields a robust, low-tuning optimizer for black-box, nonconvex problems. Full article

The Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) was an aircraft-based campaign (15 January–26 February 2018) that deployed in situ probes and remote sensors to investigate low-level clouds over the Southern Ocean (SO). A novel methodology was developed to identify cloud boundaries and classify cloud phases in single-layer, low-level marine boundary layer (MBL) clouds below 3 km using the HIAPER Cloud Radar (HCR) and in situ measurements. The cloud base and top heights derived from HCR reflectivity, Doppler velocity, and spectrum width measurements agreed well with corresponding lidar-based and in situ estimates of cloud boundaries, with mean differences below 100 m. A liquid water content–reflectivity (LWC-Z) relationship, LWC = 0.70Z^0.29, was derived to retrieve the LWC and liquid water path (LWP) from HCR profiles. The cloud phase was classified using HCR measurements, temperature, and LWP, yielding 40.6% liquid, 18.3% mixed-phase, and 5.1% ice samples, along with drizzle (29.1%), rain (3.2%), and snow (3.7%) for drizzling cloud cases. The classification algorithm demonstrates good consistency with established methods. This study provides a framework for the boundary and phase detection of MBL clouds, offering insights into SO cloud microphysics and supporting future efforts in satellite retrievals and climate model evaluation. Full article

Rainfalls are the result of complex cloud microphysical processes. Trying to estimate their intensity and duration is a key task necessary for assessing precipitation magnitude. Across mountains, extreme rainfalls may cause several side effects on the ground, triggering severe geo-hydrological issues (floods and landslides) which impact people, human activities, buildings, and infrastructure. Therefore, having a tool able to reconstruct rainfall processes easily and understandably is advisable for non-expert stakeholders and researchers who deal with rainfall management. In this work, an evolution of the LUME (Linear Upslope Model Experiment), designed to simplify the study of the rainfall process, is presented. The main novelties of the new version, called LUME 2D, regard (1) the 2D domain extension, (2) the inclusion of warm-rain and cold-rain bulk-microphysical schemes (with snow and hail categories), and (3) the simulation of convective precipitations. The model was completely rewritten using Python (version 3.11) and was tested on a heavy rainfall event that occurred in Piedmont in April 2025. Using a 2D spatial and temporal interpolation of the radiosonde data, the model was able to reconstruct a realistic rainfall field of the event, reproducing rather accurately the rainfall intensity pattern. Applying the cold microphysics schemes, the snow and hail amounts were evaluated, while the rainfall intensity amplification due to the moist convection activation was detected within the results. The LUME 2D model has revealed itself to be an easy tool for carrying out further studies on intense rainfall events, improving understanding and highlighting their peculiarity in a straightforward way suitable for non-expert users. Full article

Cirrus clouds cover 25% of the Earth at any given time. However, significant uncertainties remain in our understanding of cirrus cloud formation, in particular, how it is impacted by aerosols. This study investigates the formation and properties of dust-induced cirrus clouds using long-term observational datasets, focusing on Central Asia’s Aral Sea region and the Iberian Peninsula. We identify cirrus events influenced by mineral dust using an algorithm that uses CALIPSO satellite data through spatial and temporal proximity analysis. Results indicate significant seasonal and regional variations in the prevalence of dust-induced cirrus clouds, with spring emerging as the peak season for the Aral Sea and high-altitude Saharan dust transport influencing the Iberian Peninsula. With the help of DARDAR-Nice data, we characterize dust-induced cirrus clouds as being thicker, forming at higher altitudes, and exhibiting distinct microphysical properties, including reduced ice crystal concentrations and smaller frozen water content. Furthermore, a statistical test using a non-parametric Mann–Whitney U test is employed and confirms the robustness of the study. These findings enhance our understanding of the interactions between mineral dust and cloud microphysics, with implications for global climate modeling and weather forecasting. This study provides methodological advancements for dust-induced cloud detection and highlights the need for integrating a dust–cloud feedback mechanism in weather and climate models. Full article

Cloud seeding is a targeted weather modification strategy aimed at enhancing precipitation, particularly in regions facing water scarcity. This study evaluates the impacts of wintertime cloud seeding events in the western United States, focusing on three regions: the Lake Tahoe area, the Santa Rosa Range, and the Ruby Mountains, using an integrated remote sensing approach. Ground-based AgI generators were deployed to initiate seeding, and the atmospheric responses were assessed using multispectral observations from the Advanced Baseline Imager (ABI) aboard the GOES-R series satellites and regional radar reflectivity mosaics derived from NEXRAD data. Satellite-derived cloud microphysical properties, including cloud top brightness temperatures, optical thickness, and phase indicators, were analyzed in conjunction with radar reflectivity to evaluate microphysical changes associated with seeding. The analysis revealed significant regional variability: Tahoe events consistently exhibited strong seeding signatures, such as droplet-to-ice phase transitions, cloud top cooling, and thickened cloud structures, often followed by increased radar reflectivity. These outcomes were linked to favorable atmospheric conditions, including colder temperatures, elevated mid-to-upper tropospheric moisture, and sufficient supercooled liquid water. In contrast, events in the Santa Rosa Range generally showed weaker responses due to warmer, drier conditions and limited cloud development, while the Ruby Mountains presented mixed outcomes. This study improves the detection of seeding impacts by characterizing microphysical changes and precipitation development, capturing the progression from initial cloud phase transitions to hydrometeor development. The results highlight the importance of aligning seeding strategies with local atmospheric conditions and demonstrate the practical value of satellite-based tools for evaluating seeding effectiveness, particularly in data-sparse regions. Overall, this work contributes to advancing both the scientific insight and operational practices of weather modification through remote sensing. Full article

The Third Pole region, particularly the Hindu–Kush–Himalaya (HKH), is highly prone to lightning, causing thousands of fatalities annually. Skillful prediction and timely communication are essential for mitigating lightning-related losses in such observationally data-sparse regions. Therefore, this study evaluates kilometer-scale ICON-CLM-simulated atmospheric variables using six machine learning (ML) models to detect lightning activity over the Third Pole. Results from the ensemble boosting ML models show that ICON-CLM simulated variables such as relative humidity (RH), vorticity (vor), 2m temperature (t_2m), and surface pressure (sfc_pres) among a total of 25 variables allow better spatial and temporal prediction of lightning activities, achieving a Probability of Detection (POD) of ∼0.65. The Lightning Potential Index (LPI) and the product of convective available potential energy (CAPE) and precipitation (prec_con), referred to as CP (i.e., CP = CAPE × precipitation), serve as key physics aware predictors, maintaining a high Probability of Detection (POD) of ∼0.62 with a 1–2 h lead time. Sensitivity analyses additionally using climatological lightning data showed that while ML models maintain comparable accuracy and POD, climatology primarily supports broad spatial patterns rather than fine-scale prediction improvements. As LPI and CP reflect cloud microphysics and atmospheric stability, their inclusion, along with spatiotemporal averaging and climatology, offers slightly lower, yet comparable, predictive skill to that achieved by aggregating 25 atmospheric predictors. Model evaluation using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) highlights XGBoost as the best-performing diagnostic classification (yes/no lightning) model across all six ML tested configurations. Full article