Search Results (301)

This study investigates the effects of key atmospheric features on mineral dust emissions and transport in the Sahara–Sahel region, focusing on the Bodélé Depression, during June 2006 and 2011. We use a combination of high-resolution atmospheric simulations (AROME model), satellite observations (MODIS), and reanalysis data (ERA5, ECMWF) to examine the roles of the low-level jet (LLJ), Saharan heat low (SHL), Intertropical Discontinuity (ITD), and African Easterly Jet (AEJ) in modulating dust activity. Our results reveal significant interannual variability in aerosol optical depth (AOD) between the two periods, with a marked decrease in June 2011 compared to June 2006. The LLJ emerges as a dominant factor in dust uplift over Bodélé, with its intensity strongly influenced by local topography, particularly the Tibesti Massif. The position and intensity of the SHL also play crucial roles, affecting the configuration of monsoon flow and Harmattan winds. Analysis of wind patterns shows a strong negative correlation between AOD and meridional wind in the Bodélé region, while zonal wind analysis emphasizes the importance of the AEJ and Tropical Easterly Jet (TEJ) in dust transport. Surprisingly, we observe no significant correlation between ITD position and AOD measurements, highlighting the complexity of dust emission processes. This study is the first to combine climatological context and case studies to demonstrate the effects of African monsoon variability on dust uplift at intra-seasonal timescales, associated with the modulation of ITD latitude position, SHL, LLJ, and AEJ. Our findings contribute to understanding the complex relationships between large-scale atmospheric features and dust dynamics in this key source region, with implications for improving dust forecasting and climate modeling efforts. Full article

Aerosols play an important role in global meteorology and climate, as well as in air transport and human health, but there are still many unknowns on their effects and importance, in particular for the coarser (giant and ultragiant) aerosol particles. In this study, we aim to exploit the synergy between Raman lidar and Ka-band cloud radar to enlarge the size range in which aerosols can be observed and characterized. To this end, we developed an inversion technique that retrieves the aerosol microphysical properties based on cloud radar reflectivity and linear depolarization ratio. We applied this technique to a 6-year-long dataset, which was created using a recently developed methodology for the identification of giant aerosols in cloud radar measurements, with measurements from Potenza in Italy. Similarly, using collocated and concurrent lidar profiles, a dataset of aerosol microphysical properties using a widely used inversion technique complements the radar-retrieved dataset. Hence, we demonstrate that the combined use of lidar- and radar-derived aerosol properties enables the inclusion of particles with radii up to 12 µm, which is twice the size typically observed using atmospheric lidar alone. Full article

Climate change is significantly affecting water availability, emphasising the need for sustainable strategies such as wastewater reuse. While this represents a promising alternative resource, insufficiently treated wastewater may pose health risks, particularly through aerosol formation during irrigation, which can facilitate Legionella transmission. This study aimed to evaluate the presence of Legionella across various stages in a wastewater treatment plant (WWTP) that reuses effluent for agricultural purposes. Samples from the influent, four treatment phases, and the final effluent were analysed using both culture-based methods and quantitative PCR (qPCR) for Legionella spp. and L. pneumophila. qPCR detected Legionella spp. in all samples and L. pneumophila in 66% of them. In contrast, the culture-based analysis showed much lower detection levels, with only one positive sample at the influent stage—likely due to microbial interference or growth inhibition. Although contamination decreased in the final effluent, Legionella was still detected in water designated for reuse (Legionella spp. in 100% and L. pneumophila in 17% of samples). No treatment stage appeared to promote Legionella proliferation, likely due to WWTP characteristics, in addition to wastewater temperature and COD. These findings underscore the importance of monitoring Legionella in reclaimed water and developing effective control strategies to ensure the safe reuse of treated wastewater in agriculture. Full article

Localized ionization enhancements (LIEs) in altitude range corresponding to the D-region ionosphere, disrupting Very-Low-Frequency (VLF) signal propagation. This case study focuses on Lightning-induced Electron Precipitation (LEP), analyzing amplitude and phase variations in VLF signals recorded in Belgrade, Serbia, from worldwide transmitters. Due to the localized, transient nature of Energetic Electron Precipitation (EEP) events and the path-dependence of VLF responses, research relies on event-specific case studies to model reflection height and sharpness via numerical simulations. Findings show LIEs are typically under 1000 × 500 km, with varying internal structure. Accumulated case studies and corresponding data across diverse conditions contribute to a broader understanding of ionospheric dynamics and space weather effects. These findings enhance regional modeling, support aerosol–electricity climate research, and underscore the value of VLF-based ionospheric monitoring and collaboration in Europe. Full article

Ammonia (NH₃), as a vital component of the nitrogen cycle, exerts significant influence on the biosphere, air quality, and climate by contributing to secondary aerosol formation through its reactions with sulfur dioxide (SO₂) and nitrogen oxides (NO_x). Despite its critical environmental role, NH₃’s transient atmospheric lifetime and the variability in spatial and temporal distributions pose challenges for effective global monitoring and comprehensive impact assessment. Recognizing the inadequacies in current in situ measurement capabilities, this study embarked on an extensive analysis of NH₃’s temporal and spatial characteristics over East Asia, using the Infrared Atmospheric Sounding Interferometer (IASI) onboard the MetOp-B satellite from 2013 to 2024. The atmospheric NH₃ concentrations exhibit clear seasonality, beginning to rise in spring, peaking in summer, and then decreasing in winter. Overall, atmospheric NH₃ shows an annual increasing trend, with significant increases particularly evident in Eastern China, especially in June. The regional NH₃ trends within China have varied, with steady increases across most regions, while the Northeastern China Plain remained stable until a recent rapid rise. South Korea continues to show consistent and accelerating growth. East Asia demonstrates similar NH₃ emission characteristics, driven by farmland and livestock. The spatial and temporal inconsistencies between satellite data and global chemical transport models underscore the importance of establishing accurate NH₃ emission inventories in East Asia. Full article

Precise photovoltaic (PV) performance modeling is essential for optimizing system design, operational monitoring, and reliable power forecasting—yet spectral correction is often overlooked, despite its significant impact on energy yield uncertainty. This study employs the FARMS-NIT model to assess the impact of spectral irradiance on eight PV technologies across 79 European sites, grouped by Köppen–Geiger climate classification. Unlike previous studies limited to clear-sky or single-site analysis, this work integrates satellite-derived spectral data for both all-sky and clear-sky scenarios, enabling hourly, tilt-optimized simulations that reflect real-world operating conditions. Spectral analyses reveal European climates exhibit blue-shifted spectra versus AM1.5 reference, only 2–5% resembling standard conditions. Thin-film technologies demonstrate superior spectral gains under all-sky conditions, though the underlying drivers vary significantly across climatic regions—a distinction that becomes particularly evident in the clear-sky analysis. Crystalline silicon exhibits minimal spectral sensitivity (<1.6% variations), with PERC/PERT providing highest stability. CZTSSe shows latitude-dependent performance with ≤0.7% variation: small gains at high latitudes and losses at low latitudes. Atmospheric parameters were analyzed in detail, revealing that air mass (AM), clearness index (K_t), precipitable water (W), and aerosol optical depth (AOD) play key roles in shaping spectral effects, with different parameters dominating in distinct climate groups. Full article

Atmospheric corrosion, especially in coastal environments, presents a major challenge for the long-term durability of metallic and concrete infrastructure due to chloride deposition from marine aerosols. With a significant portion of the global population residing in coastal zones—often associated with intense industrial activity—there is growing demand for accurate and early corrosion prediction methods. Traditional standards for assessing atmospheric corrosivity depend on long-term empirical data, limiting their usefulness during the design stage of infrastructure projects. To address this limitation, this study develops predictive models using machine-learning techniques, namely gradient boosting, support vector machine, and neural networks, to estimate chloride deposition levels based on easily accessible climatic and geographical parameters. Our models were trained on a comprehensive dataset that included variables such as land coverage, wind speed, and orientation. Among the models tested, tree-based algorithms, particularly gradient boosting, provided the highest prediction accuracy (F1 score: 0.8673). This approach not only highlights the most influential environmental variables driving chloride deposition but also offers a scalable and cost-effective solution to support corrosion monitoring and structural life assessment in coastal infrastructure. Full article

This study presents a global aerosol climatology derived from six years (October 2018–October 2024) of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observations, using a U-Net Convolutional Neural Network (CNN) machine learning algorithm for Cloud–Aerosol Discrimination (CAD). Despite ICESat-2’s design primarily as an altimetry mission with a single-wavelength, low-power, high-repetition-rate laser, ICESat-2 effectively captures global aerosol distribution patterns and can provide valuable insights to bridge the observational gap between the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) missions to support future spaceborne lidar mission design. The machine learning approach outperforms traditional thresholding methods, particularly in complex conditions of cloud embedded in aerosol, owing to a finer spatiotemporal resolution. Our results show that annually, between 60°S and 60°N, 78.4%, 17.0%, and 4.5% of aerosols are located within the 0–2 km, 2–4 km, and 4–6 km altitude ranges, respectively. Regional analyses cover the Arabian Sea (ARS), Arabian Peninsula (ARP), South Asia (SAS), East Asia (EAS), Southeast Asia (SEA), the Americas, and tropical oceans. Vertical aerosol structures reveal strong trans-Atlantic dust transport from the Sahara in summer and biomass burning smoke transport from the Savanna during dry seasons. Marine aerosol belts are most prominent in the tropics, contrasting with earlier reports of the Southern Ocean maxima. This work highlights the importance of vertical aerosol distributions needed for more accurate quantification of the aerosol–cloud interaction influence on radiative forcing for improving global climate models. Full article

This study utilized Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite level-2 data with high-confidence cloud–aerosol discrimination (|CAD| > 70) to investigate the optical properties, vertical distributions, seasonal variations, and aerosol interactions of near-surface cloud layers (cloud base height < 2.5 km) over Australia from 2006 to 2021. This definition encompasses both traditional low clouds and part of mid-level clouds that extend into the lower troposphere, enabling a comprehensive view of cloud systems that interact most directly with boundary-layer aerosols. The results showed that the optical depth of low clouds (CODL) exhibited significant spatial heterogeneity, with higher values in central and eastern regions (often exceeding 6.0) and lower values in western plateau regions (typically 4.0–5.0). CODL values demonstrated clear seasonal patterns with spring peaks across all regions, contrasting with traditional summer-maximum expectations. Pronounced diurnal variations were observed, with nighttime CODL showing systematic enhancement effects (up to 19.29 maximum values compared to daytime 11.43), primarily attributed to surface radiative cooling processes. Cloud base heights (CBL) exhibited counterintuitive nighttime increases (41% on average), reflecting fundamental differences in cloud formation mechanisms between day and night. The geometric thickness of low clouds (CTL) showed significant diurnal contrasts, decreasing by nearly 50% at night due to enhanced atmospheric stability. Cloud layer number (CN) displayed systematic nighttime reductions (18% decrease), indicating dominance of single stratiform cloud systems during nighttime. Regional analysis revealed that the central plains consistently exhibited higher CODL values, while eastern mountains showed elevated cloud heights due to orographic effects. Correlation analysis between cloud and aerosol layer properties revealed moderate but statistically significant relationships (|R| = 0.4–0.6), with the strongest correlations appearing between cloud layer heights and aerosol layer heights. However, these correlations represent only partial influences among multiple factors controlling cloud development, suggesting measurable but modest aerosol effects on cloud properties. This study provides comprehensive observational evidence for cloud optical property variations and aerosol–cloud interactions over Australia, contributing to an improved understanding of Southern Hemisphere cloud systems and their climatic implications. Full article

The crystallization of soluble salts is one of the most significant agents of deterioration affecting porous building materials in historical architecture. This process not only compromises the physical integrity of the materials but also results in considerable aesthetic, structural, and economic consequences. Soluble salts involved in these processes may originate from geogenic sources—including soil leachate, marine aerosols, and the natural weathering of parent rocks—or from anthropogenic factors such as air pollution, wastewater infiltration, and the use of incompatible restoration materials. This study examines the role of capillary rise as a primary mechanism responsible for the vertical migration of saline solutions from the soil profile into historic masonry structures, especially those constructed with calcareous stones. It describes how water retained or sustained within the soil matrix ascends via capillarity, carrying dissolved salts that eventually crystallize within the pore network of the stone. This phenomenon leads to a variety of damage types, ranging from superficial staining and efflorescence to more severe forms such as subflorescence, microfracturing, and progressive mass loss. By adopting a multidisciplinary approach that integrates concepts and methods from soil physics, hydrology, petrophysics, and conservation science, this paper examines the mechanisms that govern saline water movement, salt precipitation patterns, and their cumulative effects on stone durability. It highlights the influence of key variables such as soil texture and structure, matric potential, hydraulic conductivity, climatic conditions, and stone porosity on the severity and progression of deterioration. This paper also addresses regional considerations by focusing on the context of Spain, which holds one of the highest concentrations of World Heritage Sites globally and where many monuments are constructed from vulnerable calcareous materials such as fossiliferous calcarenites and marly limestones. Special attention is given to the types of salts most commonly encountered in Spanish soils—particularly chlorides and sulfates—and their thermodynamic behavior under fluctuating environmental conditions. Ultimately, this study underscores the pressing need for integrated, preventive conservation strategies. These include the implementation of drainage systems, capillary barriers, and the use of compatible materials in restoration, as well as the application of non-destructive diagnostic techniques such as electrical resistivity tomography and hyperspectral imaging. Understanding the interplay between soil moisture dynamics, salt crystallization, and material degradation is essential for safeguarding the cultural and structural value of historic buildings in the face of ongoing environmental challenges and climate variability. Full article

This study addresses the influence of clouds and aerosols on solar radiation and energy over Greece from September 2004 to August 2024. By leveraging Earth Observation data and radiative transfer models, the largest to date time series was constructed, in order to investigate the fluctuations in global horizontal irradiance, its rate of change, and the natural and anthropogenic factors that drive them. By incorporating simulation tools and appropriate calibration, the solar potential per region and the rate of change of the produced photovoltaic energy for 1 kWp were also quantified, highlighting the climatic effects on the production of solar energy. Additionally, two energy planning scenarios were explored: the first regarding the energy adequacy that each region can achieve, if a surface equal to 1% of its total area is covered with photovoltaics; and the latter estimating the necessary area covered with photovoltaics to fully meet each region’s energy demand. Finally, to ensure a solid and holistic approach, the research converted energy data into economic gains and avoided CO₂ emissions. The study is innovative, particularly for the Greek standards, in terms of the volume and type of information it provides. It is able to offer stakeholders and decision and policymakers, both in Greece and worldwide thanks to the use of open access data, invaluable insights regarding the impact of climate change on photovoltaic energy production, the optimization of photovoltaic installations and investments and the resulting financial and environmental benefits from proper and methodical energy planning. Full article

Blowing snow is a common phenomenon over the Antarctic ice sheet and sea ice regions, playing a crucial role in the Antarctic climate system. Previous research developed an optimized machine learning (ML) model to diagnose blowing snow occurrence using meteorological fields from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). This paper extends that work by optimizing an ML model to estimate blowing snow height and optical depth for operational data production. Observations from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) serve as ground truth for training. The optimization process involves selecting relevant input features and identifying the most effective ML regressor. As a result, 21 MERRA-2 fields were identified as key input features, and Extreme Gradient Boosting emerged as the most effective regressor. Feature importance analysis highlights wind components and surface pressure as the most significant predictors for blowing snow height and optical depth. Individual models were developed for each month. Using 10 years of CALIPSO data (2007–2016) for training, these optimized models can be applied across the full MERRA-2 dataset, spanning from 1980 to the present. This enables the generation of hourly blowing snow height and optical depth data on the MERRA-2 grid for the entire MERRA-2 time span. Full article

The impact of stratospheric aerosols on Earth’s climate, particularly through atmospheric heating and ozone depletion, remains a critical area of atmospheric research. While satellite data provide valuable insights, independent validation methods are necessary for ensuring accuracy. Twilight near-infrared (NIR) radiometry offers a promising approach for investigating aerosol properties, such as optical depth and layer height, at high altitudes. This study aims to evaluate the effectiveness of twilight radiometry in corroborating satellite data and assessing aerosol characteristics. Two methods based on twilight radiometry—the color ratio and the derivative method—are employed to derive the aerosol layer height and optical depth. Radiances at 450, 550, 762, 775, and 1050 nm wavelengths are analyzed at varying solar zenith angles, using zenith viewing geometry for consistency. Comparisons of aerosol optical depths (AODs) between Research Pandora (ResPan) and AErosol RObotic NETwork (AERONET) data (R = 0.99) and between ResPan and Modern-Era Retrospective analysis for Research and Applications (MERRA-2) data (R = 0.86) demonstrate a strong correlation. Twilight ResPan data are also used to estimate the aerosol layer height, with results in good agreement with SAGE and lidar measurements, particularly following the Hunga Tonga eruption in Lauder, New Zealand. The simulation database, created using the libRadtran DISORT and Monte Carlo packages for daylight and twilight calculations, is capable of detecting AODs as low as 10⁻³ using the derivative method. This work highlights the potential of twilight radiometry as a simple, cost-effective tool for atmospheric research and satellite data validation, offering valuable insights into aerosol dynamics at stratospheric altitudes. Full article

Based on the Clouds and the Earth’s Radiant Energy System (CERES) satellite data from 2001 to 2023 and the climate indices from the National Oceanic and Atmospheric Administration (NOAA), this study analyzes the solar irradiance over mainland China and the impacts of clouds and aerosols on it and constructs monthly forecasting models to analyze the influence of climate indices on irradiance forecasts. The irradiance over mainland China shows a spatial distribution of being higher in the west and lower in the east. The influence of clouds on irradiance decreases from south to north, and the influence of aerosols is prominent in the east. The average explained variance of clouds on irradiance is 86.72%, which is much higher than that of aerosols on irradiance, 15.62%. Singular Value Decomposition (SVD) analysis shows a high correlation between the respective time series of irradiance and cloud influence, with the two fields having similar spatial patterns of opposite signs. The variation in solar irradiance can be attributed mainly to the influence of clouds. Empirical Orthogonal Function (EOF) analysis indicates that the variation in the first mode of irradiance is consistent in most parts of China, and its time coefficient is selected for monthly forecasting. Both the traditional multiple linear regression method and the Long Short-Term Memory (LSTM) network are used to construct monthly forecast models, with the preceding time coefficient of the first EOF mode and different climate indices as input. Compared with the multiple linear regression method, LSTM has a better forecasting skill. When the input length increases, the forecasting skill decreases. The inclusion of climate indices, such as the Indian Ocean Basin (IOB), El Nino–Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD), can enhance the forecasting skill. Among these three indices, IOB has the most significant improvement effect. The research provides a basis for accurate forecasting of solar irradiance over China on monthly time scale. Full article

In this study, we evaluate aerosol, cloud, and radiation interactions in GFS.V17.p8 (Global Forecast System System Version 17 prototype 8). Two experiments were conducted for the summer of 2020. In the control experiment (EXP CTL), aerosols interact with radiation only, incorporating direct and semi-direct aerosol effects. The sensitivity experiment (EXP ACI) couples aerosols with both radiation and Thompson microphysics, accounting for aerosol indirect effects and fully interactive aerosol–cloud dynamics. Introducing aerosol and cloud interactions results in net cooling at the top of the atmosphere (TOA). Further analysis shows that the EXP ACI produces more liquid water at lower levels and less ice water at higher levels compared to the EXP CTL. The aerosol optical depth (AOD) shows a good linear relationship with cloud droplet number concentration, similar to other climate models, though with larger standard deviations. Including aerosol and cloud interactions generally enhances simulations of the Indian Summer Monsoon, stratocumulus, and diurnal cycles. Additionally, the study evaluates the impacts of aerosols on deep convection and cloud life cycles. Full article