Search Results (124)

Marine boundary layer (MBL) clouds strongly influence radiation and precipitation over the Southern Ocean (SO), yet their vertical structures and microphysical properties remain poorly constrained across observational platforms. This study compares macrophysical and microphysical properties of single-layer, liquid-dominant MBL clouds below 3 km using aircraft observations from the SO Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES), ship-based observations from Measurements of Aerosols, Radiation, and Clouds over the SO (MARCUS), and satellite observations from CloudSat. An empirical reflectivity–microphysics retrieval framework developed from in situ droplet size distributions (DSDs) measured during SOCRATES was applied to MARCUS M-WACR and CloudSat CPR reflectivity observations to retrieve vertical profiles of number concentration (N), effective radius (r_e), and liquid water content (LWC) for cloud and drizzle particles. Cloud boundary heights and retrieved microphysical properties show broad agreement across the three platforms within the limitations imposed by instrumental sensitivity, sampling differences, and retrieval uncertainties. However, CloudSat CPR observations exhibit larger deviations because of their coarser vertical resolution and lower reflectivity sensitivity, including limited detection of low clouds below ~500 m. The observed vertical structures are consistent with condensational growth, entrainment, and collision–coalescence processes. Overall, the results demonstrate broad consistency in cloud and drizzle properties across the three platforms, while highlighting the impacts of instrumental sensitivity, vertical resolution, and sampling differences on cloud boundary detection and microphysical retrievals. Full article

Recently, numerous studies have exploited satellite Aerosol Optical Depth (AOD) to estimate near-surface particulate matter (PM) concentrations, with the aim of overcoming the limited spatial and temporal coverage of ground-based air quality monitoring networks. Despite significant progress, the relationship between AOD and PM remains highly uncertain, mainly due to the inadequate representation of local aerosol microphysical properties and of hygroscopic growth effects. In particular, satellite AOD is retrieved at ambient relative humidity, whereas standard PM measurements are performed under dry conditions. This study proposes a physics-informed, semi-empirical approach that overcomes these limitations by directly relating satellite AOD to PM measured at ambient humidity. Co-located measurements, from a Light Optical Aerosol Counter (LOAC) in the urban area of Bologna (Po Valley, Italy) during 2023, are used. This study is designed as a pilot application to evaluate the physical consistency of the proposed framework under well-characterised observational conditions, including spatial co-location, temporal matching to satellite overpasses, and exclusion of precipitation and desert dust events. The LOAC provides particle number size distribution and particle-type classification, which are used to estimate key aerosol properties controlling the AOD–PM theoretical relationship, including the Effective Radius, Extinction Efficiency, and aerosol Mass Density. These quantities, together with Mixing Layer Height, are combined within a theoretical framework linking PM and AOD, allowing for the derivation of a physically based scaling coefficient without relying on empirical hygroscopic growth corrections. The results show that using ambient PM_2.5 alone already yields a moderate linear correlation with AOD normalized by Mixing Layer Height (Pearson’s R = 0.56) whereas no meaningful correlation is found when using standard dry PM_2.5. When aerosol microphysical properties derived from LOAC measurements are incorporated, the correlation substantially improves (R = 0.76), with regression slopes close to unity and reduced errors, independently of the season. These results demonstrate that explicitly accounting for aerosol size and optical properties enhances the physical consistency and robustness of satellite-based PM estimates. The proposed framework also provides a pathway to indirectly derive aerosol hygroscopic growth factors by coupling ambient PM estimates from satellite observations with conventional dry PM measurements. This opens new perspectives for characterizing aerosol–humidity interactions from space and for improving air quality monitoring in regions lacking of dense in situ networks. Full article

We present the results of applications of ATLAS2.0 to experimental data in this final part of our series of publications. ATLAS2.0 retrieves particle microphysical parameters from multiwavelength Raman and high-spectral-resolution lidar measurements of backscatter (β) coefficients at three wavelengths, i.e., λ = 355, 532, and 1064 nm, extinction (α) coefficients at two wavelengths, i.e., 355 and 532 nm, and particle linear depolarization ratios (PLDR, δ) at three wavelengths, i.e., 355, 532, and 1064 nm, so-called 3β + 2α + 3δ datasets. The explicit use of PLDRs is a novel feature compared to all previously developed lidar data retrieval algorithms. For the tests of ATLAS2.0, we use data that were taken with NASA Langley Research Center’s airborne high-spectral-resolution lidar 2 (HSRL-2). We show the results of two case studies. We compare the particle microphysical parameters and single-scattering albedo (SSA) retrieved with ATLAS2.0 to results obtained with the first version of ATLAS, our Tikhonov regularization algorithm (TiARA), and in situ observations carried out aboard an aircraft that followed the airborne HSRL-2 instrument. The solutions converge within the retrieval uncertainties of these techniques. The discrepancy between the measured and backcalculated, i.e., retrieved 3β + 2α + 3δ data on average stays below 10%. The difference between the retrieved and measured PLDRs is, on average, even less. This comparably good convergence of the optical datasets (experimental versus backcalculated) of both measurement cases can only be achieved if the investigated aerosol particles are analyzed on the basis of a sphere-spheroid mixture. Full article

Volcanic super-eruptions can perturb atmospheric composition and climate-relevant radiative properties in ways that are not captured by simple scaling from Pinatubo-like events. This study presents a reduced-order regime theory for the coupled evolution of stratospheric sulfur, sulfate aerosol burden, reactive halogens, ozone loss, stratospheric thermal adjustment, and aerosol residence time. The analysis is intended as an interpretive tool for organizing sulfur-rich volcanic scenarios, comparing literature-based benchmark classes, and designing chemistry–climate model experiments, rather than as an event-specific calibration or a substitute for three-dimensional models. Four control parameters structure the response: sulfur loading relative to microphysical saturation, effective halogen strength, ash-uptake efficiency, and dynamical lifetime sensitivity, with hemispheric asymmetry treated diagnostically. An external consistency check against published Pinatubo-like, idealized 10–40 teragrams of sulfur (Tg S), Toba-like, and Los Chocoyos-like responses is used to evaluate whether the reduced theory reproduces the expected rank ordering of aerosol saturation, forcing-efficiency decline, ozone-loss amplification, ash-driven sulfur suppression, and residence-time sensitivity. This comparison does not assign pointwise error margins against three-dimensional model output; it evaluates regime membership, sign of response, rank ordering, and broad magnitude behavior. The main conclusion is that volcanic super-eruption impacts are governed by interacting regime transitions rather than by sulfur mass alone. Microphysical saturation can limit forcing efficiency, halogens can shift the system toward chemically amplified ozone depletion, ash uptake can reduce the effective sulfur burden during the early phase, and dynamical state can control persistence and hemispheric expression. By separating these mechanisms, the study provides a compact basis for interpreting large volcanic perturbations to atmospheric chemistry and for designing targeted model experiments on extreme eruption scenarios. Full article

We present a novel algorithm for the retrieval of non-spherical particle microphysical parameters (PMP) from 3β + 2α + 3δ optical data taken with multiwavelength lidar. The 3β + 2α + 3δ optical datasets describe particle backscatter coefficients (β) at three wavelengths, λ = 355, 532, and 1064 nm, particle extinction coefficients (α) at two wavelengths, λ = 355 and 532 nm, and particle linear depolarization ratios (PLDR, δ) at three wavelengths, λ = 355, 532, and 1064 nm. The algorithm can be used for retrieving bimodal particle size distributions (PSDs). The PSDs can comprise mixtures of spheres and spheroids (SS). One or both modes can comprise spheroid-shaped particles or spherically shaped particles. The spheroids are used for approximating an arbitrary ensemble of non-spherical particles. The algorithm works on the basis of a combination of direct and analytical inversion methods. The algorithm uses the spheroid reference look-up table (RLUT) we developed and presented in part 1 of our research work. The algorithm uses constraints regarding the particle complex refractive index (CRI) and information on relative humidity (RH) in the atmosphere (in the case of aerosol lidar observation) for suppressing retrieval uncertainties. We carried out a numerical simulation study to evaluate the algorithm’s performance. In these numerical simulations, we considered perturbed synthetic 3β + 2α + 3δ optical data that mimic different organic carbon (OC)–dust (D) mixtures. Such mixtures are suitable examples for describing bimodal PSDs that consist of a fine mode of spherical particles and a coarse mode of non-spherical particles. The results of the numerical simulation show that (1) the PMPs of each mode of these particle mixtures can be found separately, (2) the mean retrieval errors of the effective radius, number, surface-area, and volume concentrations of these mixtures are 25%, 52%, 9%, and 28%, respectively, and (3) the mean retrieval error of single-scattering albedo (SSA) at 355 nm of these mixtures is as low as ±0.02. SSA retrieval accuracies at 532 and 1064 nm degrade because the complex refractive index (CRI) of OC and D particles depends on the measurement wavelength. In future studies, we will upgrade the algorithm such that it takes into account a spectrally dependent CRI. We also compare the results of our novel algorithm with our TiARA2.1 algorithm. The errors obtained from the TiARA2.1 algorithm are approximately three times larger compared to the errors we obtain with our novel ATLAS algorithm for the case of the OC-D mixtures considered in the present study. We explain the higher accuracy of the PMP retrievals by the use of three PLDRs and the extra constraints placed on CRI and RH. Full article

We developed a reference look-up table (RLUT) of particles of spheroidal shape. This RLUT will be used in our lidar-data inversion algorithm we have developed in the past 25 years for the retrieval of microphysical parameters of non-spherical particles from 3β + 2α + 3δ optical datasets measured with Raman/HSRL lidar. The optical datasets are described by particle backscatter coefficients (β) at three wavelengths λ = 355, 532, and 1064 nm, particle extinction coefficients (α) at two wavelengths λ = 355 and 532 nm, and particle linear depolarization ratios (PLDRs, δ) at three wavelengths λ = 355, 532, and 1064 nm. The RLUT contains 64,032 synthetic 3β + 2α + 3δ—datasets calculated on the basis of a light-scattering model of randomly oriented spheroids and spheroid particle size distributions described by different particle complex refractive indices (CRIs) and lognormal functions with different Gauss parameters such as mean radius (μ) and standard deviation (σ). We investigate major features of the RLUT such as information content encoded in the 3β + 2α + 3δ datasets, conditionality, determinacy and the sensitivity of the retrievals to the underlying measurement errors. We find that major features of the sphere and spheroid RLUTs are similar; however, extra information is encoded in the PLDRs. The PLDR spectrum on the domain λ ∈ [355; 1064] μm contains significant information about the size of spheroid particles. The analysis of the information content is more productive if we use the cross-polarized backscatter-related Ångström exponent (CrPBAE) at the wavelength pairs 355 and 532 nm [${\dot{\beta }}^{\perp }$(355/532)] and the wavelength pairs 532 and 1064 nm [${\dot{\beta }}^{\perp }$(532/1064)]. In particular, the cycloid-like behavior of the interdependency ${\dot{\beta }}^{\perp }$(355/532) versus ${\dot{\beta }}^{\perp }$(532/1064), i.e., hysteresis, means that non-spherical particle size changes. Full article

Accurate validation of spaceborne lidar data is fundamental for reliable quantification of aerosol vertical distributions, which strongly influence air quality and climate effects. This study presents a comparative analysis of aerosol profiles from the 532 nm High-Spectral-Resolution Lidar (HSRL) onboard China’s DQ-1 satellite (ACDL) and ground-based observations from the Asian Dust and Aerosol Lidar Observation Network (AD-Net). Using one year of measurements under minimized spatiotemporal mismatches at three representative coastal stations (Matsue, Tokyo, Hedo), we quantify the sources of observational differences. Results show that discrepancies in detection targets (aerosols/clouds) dominate the total variance (>75%), while instrumental differences contribute 10–25%. Horizontal wind speed, particularly its north–south component, correlates more strongly with discrepancies than vertical wind speed, except in high-concentration aerosol layers where vertical motions become influential. Furthermore, larger differences are associated with increased aerosol extinction coefficients (α) and particle depolarization ratios (δ). This work demonstrates that integrated applications of multi-platform lidar data must account for both meteorological controls on aerosol transport and particle microphysical properties. These findings provide a quantitative validation framework for current and future spaceborne HSRL missions and support the integrated application of multi-platform lidar observations in regional aerosol monitoring, air quality assessment, and climate effect research. Full article

Aerosol–cloud interactions (ACIs) remain a long-standing uncertainty in quantifying cloud microphysical properties, convection, and precipitation. There are fewer investigations into the effects of ACIs on the southwest vortex (a mesoscale circulation with a spatial scale of 300–500 km). Satellite-retrieved MODIS data (2002–2022) reveals a decreasing trend in the June–August (JJA) seasonal mean ice droplet effective radius (DER_Ice) over the Sichuan Basin (SCB) since 2013, corresponding to China’s emission reduction efforts. Concurrently, post-2013 trends exhibit a positive shift in cloud-top height (CTH) and a negative trend in cloud-top pressure (CTP), collectively indicative of intensified convective activity. This contradicts the conventional conclusion that increased anthropogenic emissions reduce droplet effective radius (DER) and intensify convection under constant cloud water content. To address this discrepancy, we simulated the precipitation event caused by the southwest vortex (SWV) during 11–14 August 2020, under distinct initial aerosol loading (clean vs. polluted), using the fully coupled WRF-ACI-Full cloud-resolving model (incorporating sophisticated aerosol parameterizations). Results show that increased aerosols reduce basin-averaged precipitation by 0.54% and updraft speed by 0.37% in the polluted case compared to the clean case, which is negligible. These findings differ from previous studies on ACI-related cloud and precipitation responses. Full article

Numerical simulations of (1) two aerosol types such as organic carbon (i.e., spherical) and dust (i.e., non-spherical) particles, and (2) their mixtures are carried out. Optical and microphysical parameters of these aerosols in our simulations are provided by MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2). The inversion routine is performed with TiARA (Tikhonov Advanced Regularization Algorithm) using the Lorenz–Mie (i.e., spherical) light-scattering model in unsupervised and automated, i.e., autonomous mode. The results of our numerical simulations show that the accuracy of the inversion results for the aerosol mixtures from synthetic optical data perturbed by ±10% random error is comparable to the accuracy observed for the inversion results of the “pure” spherical particles. In particular, the retrieval uncertainties of effective radius, and number, surface-area, and volume concentrations of these mixtures are ±30%, ±10%, between −50% and +100% and ±30%, respectively. However, we need to apply a modified version of the gradient correlation method (GCM) to stabilize the inversion results. The results of this study will form the baseline for future work, where we plan to apply TiARA to optical data products obtained from real lidar observations in the framework of the SCC (Single Calculus Chain) of EARLINET (European Aerosol Research Lidar Network). Full article

Mixed-phase clouds, in which liquid droplets and ice crystals coexist at temperatures between $-38 °$C and $0 °$C, play a critical role in Earth’s radiation budget. Here, we assess the ability of climate and storm-resolving models to represent mixed-phase cloud properties and their hemispheric contrasts as inferred from satellite observations. We compare observations from the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) with one global climate model, the Community Atmosphere Model version 6, Oslo configuration (CAM6-Oslo), and three storm-resolving models: the ICOsahedral Non-hydrostatic model (ICON), the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM), and the Goddard Earth Observing System model (GEOS). Our results show that all models reproduce the geographic distribution of mixed-phase clouds but differ significantly in detail. CAM6-Oslo yields the closest agreement in hemispheric contrasts of supercooled liquid fraction and its relationship with the liquid effective radius. Our results highlight the role of aerosol–cloud interactions and microphysics schemes in determining model performance and demonstrate that storm-resolving models still do not overcome the challenge of representing mixed-phase clouds at global scales. Full article

Aerosol microphysical and optical properties play a crucial role in cloud microphysics, precipitation physics, and flood formation over areas characterized by complex monsoon regimes. This research presents a multi-source data integration approach to analyzing the spatio-temporal interaction between precipitation, aerosols, and flooding in the state of Kerala, incorporating an air mass trajectory analysis to examine its potential contribution to flooding. The results show that the Aerosol Optical Depth (AOD) values were high in the coastal districts (>0.8) in the La Niña year (2021) but low in the El Niño year (2015). On the precipitation side, 2018 and 2021 were both years with a high degree of anomalies, resulting in heavy rainfall that led to widespread flooding in the Thrissur district, among others. The trajectory analysis revealed that the Indian Ocean controls the precipitation during the southwest monsoon and the pre-monsoon. The post-monsoon precipitation is mainly sourced from the Arabian Peninsula and Arabian Sea, transferring marine aerosols along with desert aerosols. The overall study shows that the variability in aerosols and precipitation is more subject to change by the meteorological dynamics, as well as influenced by the regional changes in land use and land cover, causing fluxes in the land–atmosphere interactions. In conclusion, the present study highlights the possible interactive functions of atmospheric dynamics and anthropogenic land use modifications in generating a flood hazard. It provides essential information for land management policies and disaster risk reduction. Full article

This study, based on global AERONET observation data from 2023, employs a synergistic inversion algorithm that integrates aerosol optical, microphysical, and chemical properties to retrieve the global distribution of aerosol parameters. We find that the global annual mean aerosol optical depth (AOD), fine-mode AOD (AOD_f), coarse-mode AOD (AOD_c), absorbing aerosol optical depth (AAOD), single scattering albedo (SSA) are 0.20, 0.15, 0.04, 0.024, and 0.87, respectively. From the perspective of spatial distribution, in densely populated urban areas, AOD is mainly determined by AOD_f, while in the areas dominated by natural sources, AOD_c contributes more. Combined with the optical and microphysical properties, fine-mode aerosols dominate optical contributions, whereas coarse-mode aerosols dominate volume contributions. In terms of chemical components, fine-mode aerosols at most global sites are primarily carbonaceous. The mass concentrations of black carbon (BC) exceed 10 mg m⁻² in parts of South Asia, Southeast Asia, and the Arabian Peninsula, while the mass fraction of brown carbon (BrC) accounts for more than 16% in regions such as the Sahara, Western Africa, and the North Atlantic Ocean reference areas. The dust (DU) dominates in coarse mode, with the annual mean DU fraction reaching 86.07% in the Sahara. In coastal and humid regions, the sea salt (SS) and water content (AW_c) contribute significantly to the aerosol mass, with fractions reaching 13.13% and 34.39%. The comparison of aerosol properties in the hemispheres reveals that the aerosol loading in the Northern Hemisphere caused by human activities is higher than in the Southern Hemisphere, and the absorption properties are also stronger. We also find that the uneven distribution of global observation sites leads to a significant underestimation of aerosol absorption and coarse-mode features in global mean values, highlighting the adverse impact of observational imbalance on the assessment of global aerosol properties. By combining analyses of aerosol optical, microphysical, and chemical properties, our study offers a quantitative foundation for understanding the spatiotemporal distribution of global aerosols and their emission contributions, providing valuable insights for climate change assessment and air quality research. 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

An analysis was conducted using AERONET Inversion Data at Thessaloniki and Leipzig stations. Aerosol type plays a vital role in determining their ability to act as CCN or INP, as properties such as chemical composition, morphology, and particle size influence their hygroscopic and ice-nucleating behavior. The CALIPSO mission provides global aerosol classification with vertical resolution by using backscatter intensity and depolarization ratio measurements. Aerosol typing from CALIPSO overpasses within 100 km of each selected AERONET station was used. Only pure aerosol cases (dust, polluted continental, smoke) were selected. This study combines AERONET-derived microphysical properties with CALIPSO aerosol classification to estimate particle number concentrations relevant for CCN and INP formation. The aim is to derive improved conversion factors for each aerosol type, enabling their application in future CCN and INP concentration profiles. 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