Search Results (617)

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

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

Since dust aerosols are one of the major pollutants in Georgia, it is important to study the aeolian desert dust (ADD) invasion to Georgia from the neighboring deserts to find out its contribution to the dust pollution problem. Therefore, the main objective of this study is to investigate the history, frequency and routes of ADD invasions to the Caucasus (Georgia) using modern models and technologies for 1.5 years. Using WRF-Chem/dust, CAMS and HYSPLIT mathematical models; MODIS satellite images; and PM10 field data, 38 cases of not strong ADD invasions to Georgia were found, and two typical cases are presented and analyzed in this paper. The results of the modeling studies from 15 March 2023 to 15 September 2024 showed that the WRF-Chem/dust (GOCART) v.4.5.1 model simulated the ADD transport to Georgia from the surrounding deserts quite well. Daily monitoring of ADD migration routes showed that in the easternmost region of Georgia (the most vinicultural and agricultural region), the number of ADD invasions was approximately three times higher than in other regions of Georgia, which is a novelty of this study due to the lack of ground dust measurement stations in the easternmost region of Georgia. Full article

The Metropolitan Area of Queretaro (MAQ) is a significant industrial hub in central Mexico whose air quality, including high concentrations of particulate matter (PM), poses a risk to the population. However, there have not been many studies on the sources and processes that influence the concentration of atmospheric pollutants. We used aerosol chemical composition and meteorological data from 1 January to 15 May 2022, along with back-trajectory modeling, to investigate emission sources not previously described in the region and the impact of local and regional meteorology on the chemical composition of aerosols. Furthermore, this study presents the first quantitative analysis of nitroaromatic compounds (NACs) in particulate matter in the MAQ using ultra-performance liquid chromatography coupled with high-resolution mass spectrometry. The NAC concentrations ranged from 0.086 to 3.618 ng m⁻³, with the highest concentrations occurring during a period of atmospheric stability. The secondary inorganic and organic fractions of the PM were the most abundant (50%) of the PM concentration throughout the campaign. Local and regional meteorology played a significant role in the variability of PM chemical composition, as it influenced oxidation and transport processes. The results reveal that emissions from biomass burning are a recurrent PM source, and regional emissions significantly impact the organic fraction of the PM. These results underscore the importance of considering both local and regional sources in assessing air pollution in the region. Full article

We present coordinated observations from ozone Differential Absorption lidar (DIAL), aerosol lidar, and Doppler wind lidar at the City College of New York (CCNY) in northern Manhattan during the summer 2023 AGES+ campaigns across the New York City (NYC) region and Long Island Sound (LIS) areas. The results highlight significant ozone formation within the planetary boundary layer (PBL) and the concurrent transport of ozone/aerosol plumes aloft and mixing into the PBL during 26–28 July 2023. Especially, 26 July experienced the highest ozone concentration within the PBL during the three-day ozone episode despite having a lower temperature than the following two days. In addition, the onset of the afternoon sea breeze contributed to increased ozone levels in the PBL. A mobile ozone DIAL was also deployed at Columbia University’s Lamont–Doherty Earth Observatory (LDEO) in Palisades, NY, 29 km north of NYC, from 11 August to 8 September 2023. A notable high-ozone episode was observed by both ozone DIALs at the CCNY and the LDEO site during an unusual heatwave event in early September. On 7 September, the peak ozone concentration at the LDEO reached 120 ppb, exceeding the ozone levels observed in NYC. This enhancement was associated with urban plume transport, as indicated by wind lidar measurements, the HRRR (High-Resolution Rapid Refresh) model, and the Copernicus Sentinel-5 TROPOMI (TROPOspheric Monitoring Instrument) tropospheric column NO₂ product. The results also show that, during both heatwave events, those days with slow southeast to southwest winds experienced significantly higher ozone pollution. Full article

Aerosol optical depth (AOD) serves as a critical indicator for atmospheric aerosol monitoring and air quality assessment, and quantifies the radiative attenuation caused by airborne particulate matter. This study uses MODIS remote sensing imagery together with land use transition datasets (2000–2020) and road network density metrics (2014–2020), to investigate the spatiotemporal evolution of AOD in Zhejiang Province and its synergistic correlations with urbanization patterns and transportation infrastructure. By integrating MODIS_1KM AOD product, grid-based road network density mapping, land use dynamic degree modeling, and transfer matrix analysis, this study systematically evaluates the interdependencies among aerosol loading, impervious surface expansion, and transportation network intensification. The results indicate that during the study period (2000–2020), the provincial AOD level shows a significant declining trend, with obvious spatial heterogeneity: the AOD values in eastern coastal industrial zones and urban agglomerations continue to increase, with lower values dominating southwestern forested highlands. Meanwhile, statistical analyses confirm highly positive correlations between AOD, impervious surface coverage, and road network density, emphasizing the dominant role of anthropogenic activities in aerosol accumulation. These findings provide actionable insights for enhancing land-use zoning, minimizing vehicular emissions, and developing spatially targeted air quality management strategies in rapidly urbanizing regions. This study provides a solid scientific foundation for advancing environmental sustainability by supporting policy development that balances urban expansion and air quality. It contributes to building more sustainable and resilient cities in Zhejiang Province. Full article

Nitrogen oxides (NO_X) are important precursors of ozone and secondary aerosols. Accurate and timely NO_X emission estimates are essential for formulating measures to mitigate haze and ozone pollution. Bottom–up and satellite–constrained top–down methods are commonly used for emission inventory compilation; however, they have limitations of time lag and high computational demands. Here, we propose a machine learning model, WOA-XGBoost (Whale Optimization Algorithm–Extreme Gradient Boosting), to retrieve NO_X emissions. We constructed a dataset incorporating satellite observations and conducted model training and validation in the Shandong region with severe NO_X pollution to retrieve high spatiotemporal resolution of NO_X emission rates. The 10–fold cross–validation coefficient of determination (R²) for the NO_X emission retrieval model was 0.99, indicating that WOA-XGBoost has high accuracy. Validation of the model for the other year (2019) showed high agreement with MEIC (Multi–resolution Emission Inventory for China), confirming its strong robustness and good temporal transferability. The retrieved NO_X emissions for 2021–2022 revealed that emission rate hotspots were located in areas with heavy traffic flow. Among 16 prefecture–level cities in Shandong, Zibo exhibited the highest NO_X rate (>1 μg/m²/s), explaining its high NO₂ pollution levels. In the future, priority areas for emission reduction should focus on heavy industry clusters such as Zibo and high traffic urban centers. Full article

Accurate projection of future climate trends in arid regions critically depends on reliable precipitation simulations. However, most Coupled Model Intercomparison Project Phase 6 (CMIP6) models exhibit systematic overestimations of precipitation in Northwest China, a bias that undermines the credibility of climate projections for this vulnerable region. This persistent bias likely stems from the omission of key physical processes in traditional models. In this study, we incorporate a dust–ice-cloud interaction scheme into the Community Atmosphere Model version 5 (CAM5) model to investigate its role in regulating precipitation over dust-rich arid regions. This physical mechanism, which is rarely included in conventional models, is particularly relevant for Northwest China where dust aerosols are abundant. Our results show that accounting for dust-induced ice nucleation leads to a significant reduction in total precipitation, especially in the convective component, thereby alleviating the longstanding wet bias in the region. These findings underscore the critical importance of dust–ice-cloud interactions in simulating precipitation in arid environments. To improve the accuracy of future climate projections in Northwest China, climate models must incorporate realistic representations of dust-related microphysical processes. 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

Canopy chlorophyll content (CCC) is a key indicator for assessing the carbon sequestration capacity and material cycling efficiency of ecosystems, and its accurate retrieval holds significant importance for analyzing ecosystem functioning. Although numerous destructive and remote sensing methods have been developed to estimate CCC, the accurate estimation of CCC remains a significant challenge in mountainous regions with complex terrain and heterogeneous vegetation types. Through the synergistic analysis of ground hyperspectral and Sentinel-2 data, this study employed Pearson correlation analysis and spectral resampling techniques to identify Sentinel-2 blue band B1 (443 nm) and red band B4 (665 nm) as chlorophyll-sensitive bands through spectral matching with the hyperspectral reflectance of typical grassland vegetation. Based on this, we developed a new four-band vegetation index (VI), the Dual Red-edge and Coastal Aerosol Vegetation Index (DRECAVI), for estimating the CCC of heterogeneous grasslands in the middle section of the Tianshan Mountains. DRECAVI incorporates red-edge anti-saturation modules (bands B4 and B7) and aerosol correction modules (bands B1 and B8). In order to test the performance of the new index, we compared it with eight commonly used indices and a hybrid model, the Sentinel-2 Biophysical Processor (S2BP). The results indicated the following: (1) DRECAVI demonstrated the highest accuracy in CCC retrieval for mountainous vegetation (R² = 0.74, RMSE = 16.79, MAE = 12.50) compared to other VIs and hybrid methods, effectively mitigating saturation effects in high biomass areas and capturing a weak bimodal distribution pattern of CCC in the montane meadow. (2) The blue band B1 enhances atmospheric correction robustness by suppressing aerosol scattering, and the red-edge band B7 overcomes the sensitivity limitations of conventional red-edge indices (such as NDVI705, CIred-edge, and NDRE), demonstrating the potential application of the synergy mechanism between the blue band and the red-edge band. (3) Although the S2BP achieved high accuracy (R² = 0.73, RMSE = 19.83, MAE = 14.71) without saturation effects and detected a bimodal distribution of CCC in the montane meadow of the study area, its algorithmic complexity hindered large-scale operational applications. In contrast, DRECAVI maintained similar precision while reducing algorithmic complexity, making it more suitable for regional-scale grassland dynamic monitoring. This study confirms that the synergistic use of multi-source data effectively overcomes the limitations of the spectral–spatial resolution of a single data source, providing a novel methodology for the precision monitoring of mountain ecosystems. Full article

Aerosols considerably reduce the upwelling radiance in the thermal infrared (IR) window; thus, it is worthwhile to understand the effects and challenges of assimilating aerosol-affected (i.e., hazy-sky) IR observations for all-sky data assimilation (DA). This study introduces an aerosol-aware DA framework for the Infrared Atmospheric Sounder Interferometer (IASI) to exploit hazy-sky IR observations and investigate the impact of assimilating hazy-sky IR observations on analyses and subsequent forecasts. The DA framework consists of the detection of hazy-sky pixels and an observation error model as the function of the aerosol effect. Compared to the baseline experiment, the experiment utilized an aerosol-aware framework that reduces biases in the sea surface temperature in the tropical region, particularly over the areas affected by heavy dust plumes. There are no significant differences in the evaluation of the analyses and the 7-day forecasts between the experiments. To further improve the aerosol-aware framework, the enhancements in quality control (e.g., aerosol detection) and bias correction need to be addressed in the future. 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