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The Copernicus Atmosphere Monitoring Service (CAMS) provides daily analyses and forecasts of the composition of the atmosphere, including the reactive gases such as O3, CO, NO2, HCHO and SO2; aerosol species; and greenhouse gases. The global CAMS analysis system (IFS-COMPO) is based on the ECMWF Integrated Forecasting System (IFS) for numerical weather prediction (NWP) and assimilates a large number of composition satellite products on top of the meteorological observations ingested in IFS. The CAMS system receives regular upgrades, following the upgrades of IFS. The last upgrade, Cy48R1, operational since 27 June 2023, was major with a large number of code changes, both for IFS-COMPO and for NWP. The main IFS-COMPO innovations include the introduction of full stratospheric chemistry; a major update of the emissions; a major update of the aerosol model, including the representation of secondary organic aerosol; several updates of the dust life cycle and optics; updates to the inorganic chemistry in the troposphere; and the assimilation of Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol optical depth (AOD) and TROPOspheric Monitoring Instrument (TROPOMI) CO. The CAMS Cy48R1 upgrade was validated using a large number of independent measurement datasets, including surface in situ, surface remote sensing, routine aircraft, and balloon and satellite observations. In this paper we present the validation results for Cy48R1 by comparing them with the skill of the previous operational system (Cy47R3), with the independent observations as reference, for the period October 2022 to June 2023, during which daily forecasts from both cycles are available. Major improvements in skill are found for the ozone profile in the lower–middle stratosphere and for stratospheric NO2 due to the inclusion of full stratospheric chemistry. Stratospheric trace gases compare well with the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) observations between 10 and 200 hPa, with larger deviations between 1 and 10 hPa. The impact of the updated emissions is especially visible over East Asia and is beneficial for the trace gases O3, NO2 and SO2. The CO column assimilation is now anchored by the Infrared Atmospheric Sounding Interferometer (IASI) instead of the Measurements Of Pollution in The Troposphere (MOPITT) instrument, which is beneficial for most of the CO comparisons, and the assimilation of TROPOMI CO data improves the model CO field in the troposphere. In general the aerosol optical depth has improved globally, but the dust evaluation shows more mixed results. The results of the 47 comparisons are summarised in a scorecard, which shows that 83 % of the evaluation datasets show a neutral or improved performance of Cy48R1 compared to the previous operational CAMS system, while 17 % indicate a (slight) degradation. This demonstrates the overall success of this upgrade.

Global monitoring of aerosols is required to analyse the impacts of aerosols on air quality and to understand their role in modulating the climate variability. The Copernicus Atmosphere Monitoring Service (CAMS) provides near-real-time forecasts and reanalyses of aerosols using the ECMWF Integrated Forecasting System (IFS), constrained by the assimilation of MODIS and Polar Multi-Sensor Aerosol Optical Properties (PMAp) aerosol optical depth (AOD). Given the potential end of lifetime of MODIS AOD, implementing new AOD observations in the CAMS operational suite is a priority to ensure the continuity of the CAMS forecast performances. The objective of this work is to test the assimilation of the NOAA VIIRS AOD product from S-NPP and NOAA20 satellites in the IFS model. Simulation experiments assimilating VIIRS on top or in place of MODIS were carried out from June 2021 to November 2021 to evaluate the impacts on the AOD analysis. For maritime aerosol background, the assimilation of VIIRS and the use of VIIRS from NOAA20 as an anchor reduce the analysis AOD values compared to MODIS-based experiments, in which the analysis values were too high due to the positive bias of MODIS/Terra over ocean. Over land, the assimilation of VIIRS induces a large increase in the analysis over biomass burning regions where VIIRS shows larger AOD than MODIS due to differences in the aerosol models and cloud filtering between MODIS and VIIRS retrieval algorithms. For dust source regions, the analysis is reduced when VIIRS is assimilated on top of or in place of MODIS, particularly over the Sahara, the Arabian Peninsula and a few places in Asia in the July–August period. The assimilation of VIIRS leads to an overall reduction of the bias in AOD analysis evaluated against AERONET measurements, with the largest bias reduction over Europe and desert and maritime sites.

The Copernicus Atmosphere Monitoring Service (CAMS) provides near-real-time forecast and reanalysis of aerosols using the ECMWF Integrated Forecasting System with atmospheric composition extension, constrained by the assimilation of MODIS and the Polar Multi-Sensor Aerosol Optical Properties (PMAp) aerosol optical depth (AOD). The objective of this work is to evaluate two new near-real-time AOD products to prepare for their assimilation into CAMS, namely the Copernicus AOD (collection 1) from the Sea and Land Surface Temperature Radiometer (SLSTR) on board Sentinel 3-A/B over ocean and the NOAA EPS AOD (v2.r1) from VIIRS on board S-NPP and NOAA20 over both land and ocean. The differences between MODIS (C6.1), PMAp (v2.1), VIIRS (v2.r1), and SLSTR (C1) AOD as well as their departure from the modeled AOD were assessed at the model grid resolution (i.e., level-3) using the 3-month AOD average (December 2019–February 2020 and March–May 2020). VIIRS and MODIS show the best consistency across the products, which is explained by instrument and retrieval algorithm similarities. VIIRS AOD is frequently lower over the ocean background and higher over biomass burning and dust source land regions compared to MODIS. VIIRS shows larger spatial coverage over land and resolves finer spatial structures such as the transport of Australian biomass burning smoke over the Pacific, which can be explained by the use of a heavy aerosol detection test in the retrieval algorithm. Our results confirm the positive offset over ocean (i) between Terra/MODIS and Aqua/MODIS due to the non-corrected radiometric calibration degradation of Terra/MODIS in the Dark Target algorithm and (ii) between SNPP/VIIRS and NOAA20/VIIRS due to the positive bias in the solar reflective bands of SNPP/VIIRS. SLSTR AOD shows much smaller level-3 values than the rest of the products, which is mainly related to differences in spatial representativity at the IFS grid spatial resolution due to the stringent cloud filtering applied to the SLSTR radiances. Finally, the geometry characteristics of the instrument, which drive the range of scattering angles sampled by the instrument, can explain a large part of the differences between retrievals such as the positive offset between PMAp datasets from MetOp-B and MetOp-A.

Ammonium nitrate is a major aerosol constituent over many land regions and contributes to air pollution episodes, ecosystem destruction, regional haze, and aerosol-induced climate forcing. Many climate models that represent ammonium nitrate assume that the ammonium–sulfate–nitrate chemistry reaches thermodynamic equilibrium instantaneously without considering kinetic limitations on condensation rates. The Met Office's Unified Model (UM) is employed to investigate the sensitivity of ammonium nitrate concentrations to the nitric acid uptake coefficient (γ) in a newly developed nitrate scheme in which first-order condensation theory is utilised to limit the rate at which thermodynamic equilibrium is attained. Two values of γ representing fast (γ=0.193) and slow (γ=0.001) uptake rates are tested in 20-year global UM integrations. The global burden of nitrate associated with ammonium in the “fast” simulation (0.11 Tg[N]) is twice as great as in the “slow” simulation (0.05 Tg[N]), while the top-of-the-atmosphere radiative impact of representing nitrate is −0.19 W m−2 in the fast simulation and −0.07 W m−2 in the slow simulation. In general, the fast simulation exhibits better spatial correlation with observed nitrate concentrations, while the slow simulation better resolves the magnitude of concentrations. Local near-surface nitrate concentrations are found to be highly correlated with seasonal ammonia emissions, suggesting that ammonia is the predominant limiting factor controlling nitrate prevalence. This study highlights the high sensitivity of ammonium nitrate concentrations to nitric acid uptake rates and provides a novel mechanism for reducing nitrate concentration biases in climate model simulations. The new UM nitrate scheme represents a step change in aerosol modelling capability in the UK across weather and climate timescales.

Aerosol-induced absorption of shortwave radiation can modify the climate through local atmospheric heating, which affects lapse rates, precipitation, and cloud formation. Presently, the total amount of aerosol absorption is poorly constrained, and the main absorbing aerosol species (black carbon (BC), organic aerosols (OA), and mineral dust) are diversely quantified in global climate models. As part of the third phase of the Aerosol Comparisons between Observations and Models (AeroCom) intercomparison initiative (AeroCom phase III), we here document the distribution and magnitude of aerosol absorption in current global aerosol models and quantify the sources of intermodel spread, highlighting the difficulties of attributing absorption to different species. In total, 15 models have provided total present-day absorption at 550 nm (using year 2010 emissions), 11 of which have provided absorption per absorbing species. The multi-model global annual mean total absorption aerosol optical depth (AAOD) is 0.0054 (0.0020 to 0.0098; 550 nm), with the range given as the minimum and maximum model values. This is 28 % higher compared to the 0.0042 (0.0021 to 0.0076) multi-model mean in AeroCom phase II (using year 2000 emissions), but the difference is within 1 standard deviation, which, in this study, is 0.0023 (0.0019 in Phase II). Of the summed component AAOD, 60 % (range 36 %–84 %) is estimated to be due to BC, 31 % (12 %–49 %) is due to dust, and 11 % (0 %–24 %) is due to OA; however, the components are not independent in terms of their absorbing efficiency. In models with internal mixtures of absorbing aerosols, a major challenge is the lack of a common and simple method to attribute absorption to the different absorbing species. Therefore, when possible, the models with internally mixed aerosols in the present study have performed simulations using the same method for estimating absorption due to BC, OA, and dust, namely by removing it and comparing runs with and without the absorbing species. We discuss the challenges of attributing absorption to different species; we compare burden, refractive indices, and density; and we contrast models with internal mixing to models with external mixing. The model mean BC mass absorption coefficient (MAC) value is 10.1 (3.1 to 17.7) m2 g−1 (550 nm), and the model mean BC AAOD is 0.0030 (0.0007 to 0.0077). The difference in lifetime (and burden) in the models explains as much of the BC AAOD spread as the difference in BC MAC values. The difference in the spectral dependency between the models is striking. Several models have an absorption Ångstrøm exponent (AAE) close to 1, which likely is too low given current knowledge of spectral aerosol optical properties. Most models do not account for brown carbon and underestimate the spectral dependency for OA.

The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis is the latest global reanalysis dataset of atmospheric composition produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), consisting of three-dimensional time-consistent atmospheric composition fields, including aerosols and chemical species. The dataset currently covers the period 2003–2016 and will be extended in the future by adding 1 year each year. A reanalysis for greenhouse gases is being produced separately. The CAMS reanalysis builds on the experience gained during the production of the earlier Monitoring Atmospheric Composition and Climate (MACC) reanalysis and CAMS interim reanalysis. Satellite retrievals of total column CO; tropospheric column NO2; aerosol optical depth (AOD); and total column, partial column and profile ozone retrievals were assimilated for the CAMS reanalysis with ECMWF's Integrated Forecasting System. The new reanalysis has an increased horizontal resolution of about 80 km and provides more chemical species at a better temporal resolution (3-hourly analysis fields, 3-hourly forecast fields and hourly surface forecast fields) than the previously produced CAMS interim reanalysis. The CAMS reanalysis has smaller biases compared with most of the independent ozone, carbon monoxide, nitrogen dioxide and aerosol optical depth observations used for validation in this paper than the previous two reanalyses and is much improved and more consistent in time, especially compared to the MACC reanalysis. The CAMS reanalysis is a dataset that can be used to compute climatologies, study trends, evaluate models, benchmark other reanalyses or serve as boundary conditions for regional models for past periods.

A unique 4-week ship cruise from Guadeloupe to Cabo Verde in April–May 2013 see part 1, Rittmeister et al. (2017) is used for an in-depth comparison of dust profiles observed with a polarization/Raman lidar aboard the German research vessel Meteor over the remote tropical Atlantic and respective dust forecasts of a regional (SKIRON) and two global atmospheric (dust) transport models (NMMB/BSC-Dust, MACC/CAMS). New options of model–observation comparisons are presented. We analyze how well the modeled fine dust (submicrometer particles) and coarse dust contributions to light extinction and mass concentration match respective lidar observations, and to what extent models, adjusted to aerosol optical thickness observations, are able to reproduce the observed layering and mixing of dust and non-dust (mostly marine) aerosol components over the remote tropical Atlantic. Based on the coherent set of dust profiles at well-defined distances from Africa (without any disturbance by anthropogenic aerosol sources over the ocean), we investigate how accurately the models handle dust removal at distances of 1500 km to more than 5000 km west of the Saharan dust source regions. It was found that (a) dust predictions are of acceptable quality for the first several days after dust emission up to 2000 km west of the African continent, (b) the removal of dust from the atmosphere is too strong for large transport paths in the global models, and (c) the simulated fine-to-coarse dust ratio (in terms of mass concentration and light extinction) is too high in the models compared to the observations. This deviation occurs initially close to the dust sources and then increases with distance from Africa and thus points to an overestimation of fine dust emission in the models.

A new global reanalysis data set of atmospheric composition (AC) for the period 2003–2015 has been produced by the Copernicus Atmosphere Monitoring Service (CAMS). Satellite observations of total column (TC) carbon monoxide (CO) and aerosol optical depth (AOD), as well as several TC and profile observations of ozone, have been assimilated with the Integrated Forecasting System for Composition (C-IFS) of the European Centre for Medium-Range Weather Forecasting. Compared to the previous Monitoring Atmospheric Composition and Climate (MACC) reanalysis (MACCRA), the new CAMS interim reanalysis (CAMSiRA) is of a coarser horizontal resolution of about 110 km, compared to 80 km, but covers a longer period with the intent to be continued to present day. This paper compares CAMSiRA with MACCRA and a control run experiment (CR) without assimilation of AC retrievals. CAMSiRA has smaller biases than the CR with respect to independent observations of CO, AOD and stratospheric ozone. However, ozone at the surface could not be improved by the assimilation because of the strong impact of surface processes such as dry deposition and titration with nitrogen monoxide (NO), which were both unchanged by the assimilation. The assimilation of AOD led to a global reduction of sea salt and desert dust as well as an exaggerated increase in sulfate. Compared to MACCRA, CAMSiRA had smaller biases for AOD, surface CO and TC ozone as well as for upper stratospheric and tropospheric ozone. Finally, the temporal consistency of CAMSiRA was better than the one of MACCRA. This was achieved by using a revised emission data set as well as by applying careful selection and bias correction to the assimilated retrievals. CAMSiRA is therefore better suited than MACCRA for the study of interannual variability, as demonstrated for trends in surface CO.

In the framework of the World Meteorological Organisation's Sand and Dust Storm Warning Advisory and Assessment System, we evaluated the predictions of five state-of-the-art dust forecast models during an intense Saharan dust outbreak affecting western and northern Europe in April 2011. We assessed the capacity of the models to predict the evolution of the dust cloud with lead times of up to 72 h using observations of aerosol optical depth (AOD) from the AErosol RObotic NETwork (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS) and dust surface concentrations from a ground-based measurement network. In addition, the predicted vertical dust distribution was evaluated with vertical extinction profiles from the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP). To assess the diversity in forecast capability among the models, the analysis was extended to wind field (both surface and profile), synoptic conditions, emissions and deposition fluxes. Models predict the onset and evolution of the AOD for all analysed lead times. On average, differences among the models are larger than differences among lead times for each individual model. In spite of large differences in emission and deposition, the models present comparable skill for AOD. In general, models are better in predicting AOD than near-surface dust concentration over the Iberian Peninsula. Models tend to underestimate the long-range transport towards northern Europe. Our analysis suggests that this is partly due to difficulties in simulating the vertical distribution dust and horizontal wind. Differences in the size distribution and wet scavenging efficiency may also account for model diversity in long-range transport.

Lidar observations of smoke aerosols have been analysed from six flights of the Facility for Airborne Atmospheric Measurements BAe-146 research aircraft over Brazil during the biomass burning season (September 2012). A large aerosol optical depth (AOD) was observed, typically ranging 0.4–0.9, along with a typical aerosol extinction coefficient of 100–400 Mm−1. The data highlight the persistent and widespread nature of the Amazonian haze, which had a consistent vertical structure, observed over a large distance ( ∼ 2200 km) during a period of 14 days. Aerosols were found near the surface; but the larger aerosol load was typically found in elevated layers that extended from 1–1.5 to 4–6 km. The measurements have been compared to model predictions with the Met Office Unified Model (MetUM) and the ECMWF-MACC model. The MetUM generally reproduced the vertical structure of the Amazonian haze observed with the lidar. The ECMWF-MACC model was also able to reproduce the general features of smoke plumes albeit with a small overestimation of the AOD. The models did not always capture localised features such as (i) smoke plumes originating from individual fires, and (ii) aerosols in the vicinity of clouds. In both these circumstances, peak extinction coefficients of the order of 1000–1500 Mm−1 and AODs as large as 1–1.8 were encountered, but these features were either underestimated or not captured in the model predictions. Smoke injection heights derived from the Global Fire Assimilation System (GFAS) for the region are compatible with the general height of the aerosol layers.

The El Niño–Southern Oscillation (ENSO) not only affects meteorological fields but also has a large impact on atmospheric composition. Atmospheric composition fields from the Monitoring Atmospheric Composition and Climate (MACC) reanalysis are used to identify the ENSO signal in tropospheric ozone, carbon monoxide, nitrogen oxide and smoke aerosols, concentrating on the months October to December. During El Niño years, all of these fields have increased concentrations over maritime South East Asia in October. The MACC Composition Integrated Forecasting System (C-IFS) model is used to quantify the relative magnitude of dynamically induced and emission-driven changes in the atmospheric composition fields. While changes in tropospheric ozone are a combination of dynamically induced and emission-driven changes, the changes in carbon monoxide, nitrogen oxides and smoke aerosols are almost entirely emission-driven in the MACC model. The ozone changes continue into December, i.e. after the end of the Indonesian fire season while changes in the other fields are confined to the fire season.

We use the global circulation model ECHAM6 extended by the aerosol module HAM2 to simulate global patterns in wildfire emission heights. Prescribed plume heights in ECHAM6 are replaced by an implementation of a simple, semi-empirical plume height parametrization. In a first step, the global performance of the plume height parametrization is evaluated for plumes reported in the Multiangle Imaging Spectroradiometer (MISR) Plume Height Project (MPHP) data set. Our results show that the parametrization simulates a largely reasonable global distribution of plume heights. While the modeled global mean plume height (1411 ± 646 m) is in good agreement with the observed mean (1382 ± 702 m), the upper and lower tails of the plume height distribution tend to be slightly underrepresented. Furthermore, we compare plume heights simulated by the simple parametrization to a more complex, analytical plume model. Major differences in global plume height distributions are found for the lowest 1.5 km, but reasonable agreement is observed for higher plumes. In a second step, fire radiative power (FRP) as reported in the global fire assimilation system (GFAS) is used to simulate plume heights for observed fires globally for the period 2005–2011. The global fraction of simulated daytime plumes injecting emissions into the free troposphere (FT) ranges from 3.7 ± 0.7 to 5.2 ± 1.0 %. This range is comparable to results from observational studies, but it is much lower than results for prescribed plume heights in the ECHAM6-HAM2 standard setup. Nevertheless, occasionally deep emission injections exceeding 5–7 km in height are simulated for intense fires and favorable meteorological conditions. The application of a prescribed diurnal cycle in FRP turns out to be of minor importance. For a hypothetical doubling in FRP, moderate changes in plume heights of 100–400 m are simulated. These small changes indicate that a potential future increase in fire intensity will only slightly impact the emission heights on a global scale.

Fires are important emitters of aerosol and trace gases and as such need to be taken into account in any atmospheric composition modelling enterprise. One method to estimate these emissions is to convert fire radiative power (FRP) analysis into dry matter burnt and emissions of smoke constituents using land-cover-dependent conversion factors. Inventories like the Global Fire Assimilation System (GFAS) follow this approach by calculating daily global smoke emissions from FRP observed by the MODIS instruments onboard the Terra and Aqua satellites. Observations with different overpass times systematically sample fires at different stages in the strong diurnal fire cycle. For some time periods, observations are available from only one instrument, which leads to a bias in the observed average FRP. We develop a method to correct this bias in daily FRP observations from any low Earth orbit (LEO) satellite, so that the budget of daily smoke emissions remains independent of the number of satellites from which FRP observations are taken into account. This ensures the possibility of running, for example, GFAS in case of failure of one of the MODIS instruments. It also enables the extension GFAS to 2000–2002 and the inclusion of FRP observations from upcoming satellite missions. The correction combines linear and non-linear regressions and uses an adaptive regionalization algorithm. It decreases the bias in daily average FRP from Terra and Aqua by more than 95%, and RMSE by 75% for Aqua and 55% for Terra. The correction algorithm is applied to Terra observations from 25 February 2000 to 31 December 2002, when Aqua observations were not available. The database of fire emissions GFASv1.0 is extended correspondingly.

The atmospheric composition forecasting system used to produce the Copernicus Atmosphere Monitoring Service (CAMS) forecasts of global aerosol and trace gas distributions, the Integrated Forecasting System (IFS-COMPO), undergoes periodic upgrades. In this study we describe the development of the future operational cycle 49R1 and focus on the implementation of the thermodynamical model EQSAM4Clim version 12, which represents gas–aerosol partitioning processes for the nitric acid–nitrate and ammonia–ammonium couples and computes diagnostic aerosol, cloud, and precipitation pH values at the global scale. This information on aerosol acidity influences the simulated tropospheric chemistry processes associated with aqueous-phase chemistry and wet deposition. The other updates of cycle 49R1 concern wet deposition, sea-salt aerosol emissions, dust optics, and size distribution used for the calculation of sulfate aerosol optics. The implementation of EQSAM4Clim significantly improves the partitioning of reactive nitrogen compounds, decreasing surface concentrations of both nitrate and ammonium in the particulate phase, which reduces PM2.5 biases for Europe, the US, and China, especially during summertime. For aerosol optical depth there is generally a decrease in the simulated wintertime biases and for some regions an increase in the summertime bias. Improvements in the simulated Ångström exponent are noted for almost all regions, resulting in generally good agreement with observations. The diagnostic aerosol and precipitation pH calculated by EQSAM4Clim have been compared to ground observations and published simulation results. For precipitation pH, the annual mean values show relatively good agreement with the regional observational datasets, while for aerosol pH the simulated values over continents are quite close to those simulated by ISORROPIA II. The use of aerosol acidity has a relatively smaller impact on the aqueous-phase production of sulfate compared to the changes in gas-to-particle partitioning induced by the use of EQSAM4Clim.

Atmospheric mineral dust aerosol constitutes a threat to aircraft engines from deterioration of internal components. Here we fulfil an overdue need to quantify engine dust ingestion at airports worldwide. The vertical distribution of dust is of key importance since ascent/descent rates and engine power both vary with altitude and affect dust ingestion. We use representative jet engine power profile information combined with vertically and seasonally varying dust concentrations to calculate the “dust dose” ingested by an engine over a single ascent or descent. Using the Copernicus Atmosphere Monitoring Service (CAMS) model reanalysis, we calculate climatological and seasonal dust dose at 10 airports for 2003–2019. Dust doses are mostly largest in Northern Hemisphere summer for descent, with the largest at Delhi in June–August (JJA; 6.6 g) followed by Niamey in March–May (MAM; 4.7 g) and Dubai in JJA (4.3 g). Holding patterns at altitudes coincident with peak dust concentrations can lead to substantial quantities of dust ingestion, resulting in a larger dose than the take-off, climb, and taxi phases. We compare dust dose calculated from CAMS to spaceborne lidar observations from two dust datasets derived from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). In general, seasonal and spatial patterns are similar between CAMS and CALIOP, though large variations in dose magnitude are found, with CAMS producing lower doses by a factor of 1.9 to 2.8, particularly when peak dust concentration is very close to the surface. We show that mitigating action to reduce engine dust damage could be achieved, firstly by moving arrivals and departures to after sunset and secondly by altering the altitude of the holding pattern away from that of the local dust peak altitude, reducing dust dose by up to 44 % and 41 % respectively. We suggest that a likely low bias of dust concentration in the CAMS reanalysis should be considered by aviation stakeholders when estimating dust-induced engine wear.

The Equilibrium Simplified Aerosol Model for Climate version 12 (EQSAM4Clim-v12) has recently been revised to provide an accurate and efficient method for calculating the acidity of atmospheric particles. EQSAM4Clim is based on an analytical concept that is not only sufficiently fast for chemical weather prediction applications but also free of numerical noise, which also makes it attractive for air quality forecasting. EQSAM4Clim allows the calculation of aerosol composition based on the gas–liquid–solid and the reduced gas–liquid partitioning with the associated water uptake for both cases and can therefore provide important information about the acidity of the aerosols. Here we provide a comprehensive description of the recent changes made to the aerosol acidity parameterization (referred to as a version 12) which builds on the original EQSAM4Clim. We evaluate the pH improvements using a detailed box model and compare them against previous model calculations and both ground-based and aircraft observations from the USA and China, covering different seasons and scenarios. We show that, in most cases, the simulated pH is within reasonable agreement with the reference results of the Extended Aerosol Inorganics Model (E-AIM) and of satisfactory accuracy.

The emergence of aerosol reanalyses in recent years has facilitated a comprehensive and systematic evaluation of aerosol optical depth (AOD) trends and attribution over multi-decadal timescales. Notable multi-year aerosol reanalyses currently available include NAAPS-RA from the US Naval Research Laboratory, the NASA MERRA-2, JRAero from the Japan Meteorological Agency (JMA), and CAMSRA from Copernicus/ECMWF. These aerosol reanalyses are based on differing underlying meteorology models, representations of aerosol processes, as well as data assimilation methods and treatment of AOD observations. This study presents the basic verification characteristics of these four reanalyses versus both AERONET and MODIS retrievals in monthly AOD properties and identifies the strength of each reanalysis and the regions where divergence and challenges are prominent. Regions with high pollution and often mixed fine-mode and coarse-mode aerosol environments, such as South Asia, East Asia, Southeast Asia, and the Maritime Continent, pose significant challenges, as indicated by higher monthly AOD root mean square error. Moreover, regions that are distant from major aerosol source areas, including the polar regions and remote oceans, exhibit large relative differences in speciated AODs and fine-mode versus coarse-mode AODs among the four reanalyses. To ensure consistency across the globe, a multi-reanalysis consensus (MRC, i.e., ensemble mean) approach was developed similarly to the International Cooperative for Aerosol Prediction Multi-Model Ensemble (ICAP-MME). Like the ICAP-MME, while the MRC does not consistently rank first among the reanalyses for individual regions, it performs well by ranking first or second globally in AOD correlation and RMSE, making it a suitable candidate for climate studies that require robust and consistent assessments.

In the realm of sensor networks, the substantial rise in multimedia data production, covering audio, video, and acoustic measurements, has expanded the scale of big data. Multimedia Sensor Networks (MSN) excel in managing diverse sensor outputs, representations, and encoding across domains. Existing models for event detection in sensor networks fall short in handling the sheer volume and speed of these measurements from a Big Data perspective. This research work introduces “Saltus,” a model that aligns multimedia data from sensor networks to a standardized feature space. Saltus employs a machine learning-centric architecture to enhance data analysis possibilities. Crucially, the model integrates federated learning to address the evolving landscape of sensor networks. This approach optimizes the collaborative learning capabilities by allowing distributed nodes to train machine learning models locally, preserving data privacy. Saltus emerges as a solution that not only streamlines multimedia data processing but also establishes a more secure and privacy-preserving analytics framework in large-scale sensor networks. The model signifies a step forward in integrating multimedia data into an easily analyzable format, leveraging the advantages of federated learning in big data analytics.

Phishing websites, mimicking legitimate counterparts, pose significant threats by stealing user information through deceptive Uniform Resource Locators (URLs). Traditional blacklists struggle to identify dynamic URLs, necessitating advanced detection mechanisms. In this study, we propose an effective approach utilizing residual pipelining for phishing URL detection. Our method extracts common URL features and sentiments, employing a residual pipeline comprising convolutional and inverted residual blocks. These resultant features are then fed into a Multi-Layer Perceptron (MLP) for classification. We evaluate the efficacy of our approach against traditional algorithms using a Kaggle dataset. Our results demonstrate superior accuracy, precision, F1 Score, and recall, showcasing its effectiveness in mitigating phishing threats. Utilizing a residual pipeline made up of convolutional and inverted residual blocks, we start our method by identifying similar URL features and sentiments. We also use domain age research to figure out how long URLs have been around. Additionally, the lexical study of URL structure makes our method more useful, resulting in impressive accuracy. With an accuracy of 98.29%, this research highlights the importance of innovative techniques in combating evolving cyber threats. Future research directions could focus on enhancing the model’s robustness against adversarial attacks and integrating real-time monitoring for proactive defense strategies.

An extensive worldwide network known as the Internet of Things (IoT) links different electronic devices and facilitates easy communication and group work. This interdependency is especially apparent in Low Power and Lossy Networks (LLNs), where resource-constrained devices adhere to specified protocols for effective communication. Such systems frequently use Routing Protocol for LLNs (RPL). Nevertheless, due to its basic simplicity, there are numerous ways to exploit it, thereby compromising network security. It is also difficult to carry out complex computational operations on LLNs due to their resource constraints. A highly developed system called the Trust-Based Intrusion Detection System for RPL (TIDSRPL) is presented in this research study. Complex trust computations are offloaded to the root node by TIDSRPL, which assesses node trust based on network behavior. Reduce the possibility of resource depletion with this strategic transfer that preserves energy, storage, and computational resources at the node level. Comparative analysis with the default RPL Objective Function (OF), MRHOF-RPL, demonstrates TIDSRPL’s superior efficacy in detecting and isolating malicious nodes engaged in Sinkhole, Selective forwarding, and Sybil attacks. Notably, TIDSRPL exhibits a 20-35% reduction in average packet loss ratio and attains 33-45% greater energy efficiency compared to MRHOF-RPL, reinforcing its robustness in securing LLN operations.

Skin lesion classification is a pivotal process in dermatology, enabling the early detection and precise diagnosis of skin diseases, leading to improved patient outcomes. Deep learning has shown great potential for this task by leveraging its ability to learn complex patterns from images. However, diagnostic accuracy is compromised by exclusive reliance on single-modality images. This research work proposes an innovative framework that unifies a Vision Transformer model with transfer learning, channel attention mechanism, and ROI for the accurate detection of skin conditions, including skin cancer. The proposed approach blends computer vision and machine-learning techniques, leveraging a comprehensive dataset comprised of macroscopic dermoscopic images, appended with patient metadata. Compared with conventional techniques, the proposed methodology exhibits significant improvements in various parameters, including sensitivity, specificity, and precision. Moreover, it demonstrates outstanding performance in real-world datasets, reinforcing its potential for clinical implementation. With a remarkable accuracy of 99%, the method outperforms existing approaches. Overall, this investigation underscores the transformative impact of deep learning and multimodal data analysis in the dermoscopic domain, projecting substantial headway into the field of skin lesion analytic diagnosis.

Phishing is an electronic fraud through which an attacker can access user credentials. Phishing websites are the ones that mimic legitimate websites. Fraudsters can replace them within hours to evade their detection. The effects of phishing attacks exhibit the need for anti-phishing mechanisms. Several approaches were there to recognize the phishing websites, the white list approach, blacklist approach, machine learning, and heuristic-based approach. Earlier studies have shown that classifiers may be subject to evasion attacks although this point has only been explored on a small scale. As a result, the study covers evasion attacks and their detection within the context of website classifiers, which is rarely explored. In response to the inadequacies, the proposed technique includes extracting information from URLs and classifying webpages using various machine learning methods. The methodology involves crafting adversarial samples targeting classification features, with a focus on maintaining the functionality and appearance of phishing websites. The appearance is evaluated using image distortion metrics named mean squared error. Then a resemblance approach is utilized for the aim of detecting assaults that happened as a result of evasion attacks. This research introduces a novel defense mechanism against evasion attacks, marking a significant contribution to the field.