In recent years, methane (CH4) has attracted increasing scientific attention as the second most abundant anthropogenic greenhouse gas (GHG) in the atmosphere. Due to the high reduction potential and the relatively short atmospheric lifetime of around 9 years, mitigation measures can become effective within a relatively short period of time. However, the current estimates of CH4 fluxes from emission inventories are still subject to uncertainties at both global and regional scale. An effort to reduce uncertainties from those bottom-up flux estimates is given by inverse modelling, which provides a robust tool to verify GHG emissions by combining GHG observations as well as atmospheric transport modelling and statistical optimization. In this study, we use an inverse modelling approach to estimate CH4 fluxes at European scale for the year 2022. Additionally, we use the European in-situ observation network to explore the feasibility of reducing uncertainties in CH4 fluxes in Austria, a European country with a limited availability of stationary observations. This work is part of the Austrian ASAP18 flagship project “GHG-KIT: Keep it traceable”. Hereby, the inverse modelling tool FLEXINVERT is used, which is based on the backward simulations of the Lagrangian particle dispersion model FLEXPART (FLEXible PARTicle). In particular, we investigate to what extent prolonged backward trajectories of 50 to 100 days contribute to better constrain the CH4 fluxes. In an attempt to estimate background concentrations as accurately as possible, we use global CH4 concentration fields obtained with the chemical transport model FLEXPART (CTM).

Atmospheric carbon dioxide (CO2) is a significant greenhouse gas, and its concentration has increased by 51% compared to the pre-industrial value. Concerning its impact on the earth’s climate system, there is an urge to reduce CO2 emissions, hence mitigating global warming and climate change. This requires adequate knowledge of its source-sink distribution and quantification of the CO2 budget. Inverse modeling has emerged as an effective tool to constrain greenhouse gas (GHG) fluxes using the spatiotemporal pattern of atmospheric concentration measurements. In this regard, this study focuses on estimating CO2 fluxes over Europe using the Bayesian inverse modelling framework FLEXINVERT during the year 2021. In-situ CO2 concentrations were taken from various locations across Europe (World Data Centre for Greenhouse Gases, WDCGG) and data were averaged every 3 hours. The Lagrangian Particle Dispersion Model FLEXPART (FLEXible PARTicle) is employed to calculate the source-receptor relationship (SRR). The FLEXPART model has been run backward in time to trace back the particles (released from the locations of observation sites) for 10 days. Background CO2 concentrations are calculated using the sensitivity of concentration at the termination points from FLEXPART and the global 3D concentration from the FLEXible PARTicle-chemical transport model (FLEXPART-CTM). The uncertainty reduction, calculated from posterior and prior flux uncertainties, indicates how well the prior fluxes are optimized. In addition, longer backward simulations can be carried out to assess the impact of transport on background CO2 concentrations and the uncertainty reduction.

Airborne mineral dust poses a safety challenge for aviation. Several fatal accidents have happened in dust-laden air due to reduced visibility, strong gusty winds, and wind shear. Dust-induced icing also contributed at least to two fatal accidents. Furthermore, atmospheric dust has long- and short-term effects on aircraft operating conditions due to corrosion and abrasion on the aircraft surfaces and molten ingress deterioration of engine hot section components. The combined impact can increase operating and maintenance costs and increase the overall cost of ownership. While the scientific community has started preparing and providing products based on atmospheric dust modeling and observation, there are still important data and information gaps in the fundamental science. These include (i) insufficient data which could be used to better understand the effects of dust on aircraft as well as on ground systems and operations (e.g., four-dimensional information of dust mineralogy, cost-benefit analysis of the impact of dust on aviation along flight routes), (ii) the identification of airborne dust monitoring and modeling products and services that could enable the flow of relevant information in commercial aviation and in decision-making workflows, and (iii) the underdeveloped, unclear, or absent role of dust hazards in regulations and operational procedures as well as in the training, skill set, and knowledge base of pilots. This review is aimed at both academic and aviation stakeholders and presents the current state-of-the-art knowledge at the intersection of dust hazards, aviation safety, and impacts on flight operations and aircraft maintenance.

Aviation is a vulnerable infrastructure. This is not only true during economic crises such as the COVID-19 pandemic but also during natural hazards, especially airborne ones. Airborne hazards can be detected and observed by Earth Observation (EO) instruments. Most EO sensors that observe air pollutants fly on Sun-synchronous satellites. The strength of these satellites is global coverage and provision of high spatial resolution measurements. Their disadvantage is, that they do not observe high temporal variations of air pollutants. This is a severe limitation for the detection of natural hazards. One solution is to combine observations from Sun-synchronous (e.g., OMI, GOME2, TROPOMI, IASI, OMPS, AIRS) and geostationary (e.g., MSG-SEVIRI for Europe and Africa) instruments. The Volcanic Ash Advisory Centers (VAACs) are responsible for predicting the dispersion of the volcanic plumes for the aviation sector. In addition, GeoSphere Austria supports the Austrian aviation authority (Austro Control) with information on volcanic ash and SO2 dispersion generated by the GeoSphere Austria emergency response Volcano Tool (GeoSphere Austria-VT). Since dispersion information needs to be available shortly after the eruption, VAACs and GeoSphere Austria forecasts are both based on rough estimates of source term parameters. The usage of near-real-time observations can significantly improve the source terms, and reduce the dispersion uncertainty. The GeoSphere Austria-VT is extended with a dynamic inverse modelling system that provides sequentially updated source terms using satellite data. Apart from flying through volcanic plumes resulting possibly in a significant hazard, flying through regions with elevated air pollution levels has an impact on engine lifetime, maintenance costs, and fuel consumption. Here, the relevant factor is the cumulative air pollution intake over time. Additional costs associated with maintenance and repair are currently not considered in flight planning. Continuous monitoring, forecasting, and optimizing data integration of air pollutants into atmospheric models has thus two significant advantages: on the one hand, the obvious risk associated with flying through highly polluted air (including volcanic ash and SO2) is reduced, on the other hand, the impact on engine maintenance and fuel costs for airline operators and engine manufacturers is minimized. From the wide range of air pollutants, we will focus on aerosols (volcanic, dust and salt) and SO2, because these are the most relevant air pollutants for the aviation sector. The aim is to implement a flexible, holistic system that is able to consider both hazardous and non-hazardous air pollution levels. The emphasis is on making extensive use of existing services (SACS, CAMS, VAACs) as well as air quality observations from satellites to extend and improve model applications (GeoSphere Austria-VT). To provide end-users with one comprehensive tool for the analysis and comparison of the air pollutants distribution from selected sources, these products will be available and visualized in one new data platform, exploiting a datacube-like approach to deal with the different spatial and temporal resolutions of the data.

The Austrian Flagship Project “GHG-KIT: Keep it traceable” aims to prototype an Austrian, Earth Observation-Integrated GHG measurement and modelling system, which can support national GHG emission monitoring. Among other aims, the project is working toward an Austrian inverse modelling framework to produce top-down estimates of national and subnational CO2 and CH4 fluxes that are independent of the current bottom-up system of the Austrian GHG inventory.This conference contribution will present the GHG-KIT progress on inverse modelling of subnational CO2 and CH4 emissions, using Vienna as a case study. Vienna is the most populated city in Austria with around 2 million inhabitants, corresponding to slightly more than a fifth of the total Austrian population. To estimate the GHG emissions in Vienna the inversion framework FLEXINVERT is used. The atmospheric back trajectories for GHG measurements carried out in Vienna are calculated using the Lagrangian dispersion model FLEXPART-WRF, which is driven by WRF meteorology. The a priori fluxes of GHGs, used in FLEXINVERT, are prepared using WRF-GHG and are based on data from the Copernicus Atmosphere Monitoring Service (CAMS), among others. WRF-GHG is a Weather Research and Forecasting (WRF) model version that is coupled with chemistry modules as well as the GHG flux module and considers urban building features. Atmospheric ground-based observations of CO2 and CH4 mole fractions from the Vienna Urban Carbon Laboratory are included in the inverse modelling. These include in-situ observations from a tall-tower, as well as total column measurements at four locations from a 2022 summer campaign performed by the Technical University of Munich. Furthermore, the usability of satellite measurements over Vienna as an additional observation constraint will be investigated. This includes GHGSat measurements from Vienna that are carried out as part of the GHG-KIT project, as well as synthetic or possibly authentic observations from satellite missions (such as CO2M and MethaneSAT) that will be launched during the course of this project or shortly thereafter.

The Austrian Flagship Project “GHG-KIT: Keep it traceable” aims to prototype an Austrian, Earth Observation-Integrated GHG measurement and modelling system, which can support national GHG emission monitoring. Among other aims, the project is working toward an Austrian inverse modelling framework to produce top-down estimates of national and subnational CO2 and CH4 fluxes that are independent of the current bottom-up system of the Austrian GHG inventory.This conference contribution will present the GHG-KIT progress on inverse modelling of subnational CO2 and CH4 emissions, using Vienna as a case study. Vienna is the most populated city in Austria with around 2 million inhabitants, corresponding to slightly more than a fifth of the total Austrian population. To estimate the GHG emissions in Vienna the inversion framework FLEXINVERT is used. The atmospheric back trajectories for GHG measurements carried out in Vienna are calculated using the Lagrangian dispersion model FLEXPART-WRF, which is driven by WRF meteorology. The a priori fluxes of GHGs, used in FLEXINVERT, are prepared using WRF-GHG and are based on data from the Copernicus Atmosphere Monitoring Service (CAMS), among others. WRF-GHG is a Weather Research and Forecasting (WRF) model version that is coupled with chemistry modules as well as the GHG flux module and considers urban building features. Atmospheric ground-based observations of CO2 and CH4 mole fractions from the Vienna Urban Carbon Laboratory are included in the inverse modelling. These include in-situ observations from a tall-tower, as well as total column measurements at four locations from a 2022 summer campaign performed by the Technical University of Munich. Furthermore, the usability of satellite measurements over Vienna as an additional observation constraint will be investigated. This includes GHGSat measurements from Vienna that are carried out as part of the GHG-KIT project, as well as synthetic or possibly authentic observations from satellite missions (such as CO2M and MethaneSAT) that will be launched during the course of this project or shortly thereafter.

In late March 2018, a large part of the Eastern Mediterranean experienced an extraordinary episode of African dust, one of the most intense in recent years, here referred to as the "Minoan Red" event. The episode mainly affected the Greek island of Crete, where the highest aerosol concentrations over the past 15 yeas were recorded, although impacts were also felt well beyond this core area. Our study fills a gap in dust research by assessing the multi-sectoral impacts of sand and dust storms and their socioeconomic implications. Specifically, we provide a multi-sectoral impact assessment of Crete during the occurrence of this exceptional African dust event. During the day of the occurrence of the maximum dust concentration in Crete, i.e. March 22nd, 2018, we identified impacts on meteorological conditions, agriculture, transport, energy, society (including closing of schools and cancellation of social events), and emergency response systems. As a result, the event led to a 3-fold increase in daily emergency responses compare to previous days associated with urban emergencies and wildfires, a 3.5-fold increase in hospital visits and admissions for Chronic Obstructive Pulmonary Disease (COPD) exacerbations and dyspnoea, a reduction of visibility causing aircraft traffic disruptions (eleven cancellations and seven delays), and a reduction of solar energy production. We estimate the cost of direct and indirect effects of the dust episode, considering the most affected socio-economic sectors (e.g. civil protection, aviation, health and solar energy production), to be between 3.4 and 3.8 million EUR for Crete. Since such desert dust transport episodes are natural, meteorology-driven and thus to a large extent unavoidable, we argue that the efficiency of actions to mitigate dust impacts depends on the accuracy of operational dust forecasting and the implementation of relevant early warning systems for social awareness.

High-quality volcanic ash forecasts are crucial to minimize the economic impact of volcanic hazards on air traffic. Decision-making is usually based on numerical dispersion modelling with only one model realization. Given the inherent uncertainty of such an approach, a multi-model multi-source term ensemble has been designed and evaluated for the Eyjafjallajökull eruption in May 2010. Its use for flight planning is discussed. Two multi-model ensembles were built: the first is based on the output of four dispersion models and their own implementation of ash ejection. All a priori model source terms were constrained by observational evidence of the volcanic ash cloud top as a function of time. The second ensemble is based on the same four dispersion models, which were run with three additional source terms: (i) a source term obtained from a model background constrained with satellite data (a posteriori source term), (ii) its lower-bound estimate and (iii) its upper-bound estimate. The a priori ensemble gives valuable information about the probability of ash dispersion during the early phase of the eruption, when observational evidence is limited. However, its evaluation with observational data reveals lower quality compared to the second ensemble. While the second ensemble ash column load and ash horizontal location compare well to satellite observations, 3D ash concentrations are negatively biased. This might be caused by the vertical distribution of ash, which is too much diluted in all model runs, probably due to defaults in the a posteriori source term and vertical transport and/or diffusion processes in all models. Relevant products for the air traffic management are horizontal maps of ash concentration quantiles (median, 75 %, 99 %) at a finely resolved flight level grid as well as cross sections. These maps enable cost-optimized consideration of volcanic hazards and could result in much fewer flight cancellations, reroutings and traffic flow congestions. In addition, they could be used for route optimization in the areas where ash does not pose a direct and urgent threat to aviation, including the aspect of aeroplane maintenance.

The purpose of the EUNADICS-AV (European Natural Airborne Disaster Information and Coordination System for Aviation) prototype early warning system (EWS) is to develop the combined use of harmonised data products from satellite, ground-based and in situ instruments to produce alerts of airborne hazards (volcanic, dust, smoke and radionuclide clouds), satisfying the requirement of aviation air traffic management (ATM) stakeholders (https://cordis.europa.eu/project/id/723986, last access: 5 November 2021). The alert products developed by the EUNADICS-AV EWS, i.e. near-real-time (NRT) observations, email notifications and netCDF (Network Common Data Form) alert data products (called NCAP files), have shown significant interest in using selective detection of natural airborne hazards from polar-orbiting satellites. The combination of several sensors inside a single global system demonstrates the advantage of using a triggered approach to obtain selective detection from observations, which cannot initially discriminate the different aerosol types. Satellite products from hyperspectral ultraviolet–visible (UV–vis) and infrared (IR) sensors (e.g. TROPOMI – TROPOspheric Monitoring Instrument – and IASI – Infrared Atmospheric Sounding Interferometer) and a broadband geostationary imager (Spinning Enhanced Visible and InfraRed Imager; SEVIRI) and retrievals from ground-based networks (e.g. EARLINET – European Aerosol Research Lidar Network, E-PROFILE and the regional network from volcano observatories) are combined by our system to create tailored alert products (e.g. selective ash detection, SO2 column and plume height, dust cloud, and smoke from wildfires). A total of 23 different alert products are implemented, using 1 geostationary and 13 polar-orbiting satellite platforms, 3 external existing service, and 2 EU and 2 regional ground-based networks. This allows for the identification and the tracking of extreme events. The EUNADICS-AV EWS has also shown the need to implement a future relay of radiological data (gamma dose rate and radionuclides concentrations in ground-level air) in the case of a nuclear accident. This highlights the interest of operating early warnings with the use of a homogenised dataset. For the four types of airborne hazard, the EUNADICS-AV EWS has demonstrated its capability to provide NRT alert data products to trigger data assimilation and dispersion modelling providing forecasts and inverse modelling for source term estimate. Not all of our alert data products (NCAP files) are publicly disseminated. Access to our alert products is currently restricted to key users (i.e. Volcanic Ash Advisory Centres, national meteorological services, the World Meteorological Organization, governments, volcano observatories and research collaborators), as these are considered pre-decisional products. On the other hand, thanks to the EUNADICS-AV–SACS (Support to Aviation Control Service) web interface (https://sacs.aeronomie.be, last access: 5 November 2021), the main part of the satellite observations used by the EUNADICS-AV EWS is shown in NRT, with public email notification of volcanic emission and delivery of tailored images and NCAP files. All of the ATM stakeholders (e.g. pilots, airlines and passengers) can access these alert products through this free channel.

This paper demonstrates the environmental impacts of the wildfires occurring at the beginning of April 2020 in and around the highly contaminated Chernobyl Exclusion Zone (CEZ). Due to the critical fire location, concerns arose about secondary radioactive contamination potentially spreading over Europe. The impact of the fire was assessed through the evaluation of fire plume dispersion and re-suspension of the radionuclide Cs-137, whereas, to assess the smoke plume effect, a WRF-Chem simulation was performed and compared to Tropospheric Monitoring Instrument (TROPOMI) satellite columns. The results show agreement of the simulated black carbon and carbon monoxide plumes with the plumes as observed by TROPOMI, where pollutants were also transported to Belarus. From an air quality and health perspective, the wildfires caused extremely bad air quality over Kiev, where the WRF-Chem model simulated mean values of PM2.5 up to 300 µg/m3 (during the first fire outbreak) over CEZ. The re-suspension of Cs-137 was assessed by a Bayesian inverse modelling approach using FLEXPART as the atmospheric transport model and Ukraine observations, yielding a total release of 600 ± 200 GBq. The increase in both smoke and Cs-137 emissions was only well correlated on the 9 April, likely related to a shift of the focus area of the fires. From a radiological point of view even the highest Cs-137 values (average measured or modelled air concentrations and modelled deposition) at the measurement site closest to the Chernobyl Nuclear Power Plant, i.e., Kiev, posed no health risk.

Abstract. High quality volcanic ash forecasts are crucial to minimize the economic impact of volcanic hazards on air traffic. Decision-making is usually based on numerical dispersion modeling with only one model realization. Given the inherent uncertainty of such approach, a multi-model multi-source term ensemble has been designed and evaluated for the Eyjafjallajökull eruption in May 2010. Its use for air traffic management is discussed. Two multi-model ensembles were built: the first is based on the output of four dispersion models and their own implementation of ash ejection. All a priori model source terms were constrained by observational evidence of the volcanic ash cloud top as a function of time. The second ensemble is based on the same four dispersion models, which were run with three additional source terms: (i) a source term obtained with background modeling constrained with satellite data (a posteriori source term), (ii) its lower bound estimate, and (iii) its upper bound estimate. The a priori ensemble gives valuable information about the probability of ash dispersion during the early phase of the eruption, when observational evidence is limited. However, its evaluation with observational data reveals lower quality compared to the second ensemble. While the second ensemble ash column load and ash horizontal location compare well to satellite observations, 3D ash concentrations are negatively biased. This might be caused by the vertical distribution of ash, which is too much diluted in all model runs, probably due to defaults in the a posteriori source term and vertical transport and/or diffusion processes in all models. Relevant products for the air traffic management are horizontal maps of ash concentration quantiles (median, 75 %, 99 %) at a fine-resolved flight level grid. These maps can be used for route optimization in the areas where ash does not pose a direct and urgent threat to aviation. Cost-optimized consideration of such hazards will result in much less impact on flight cancellations, reroutings, and traffic flow congestions.

The scientific and industrial communities are handling continuously increasing amounts of data from Earth Observation (EO) satellite missions and related instruments. This is in particular the case for the atmospheric sciences communities, with the recently launched Copernicus Sentinel-5 Precursor, the upcoming Sentinel-4, -5, and ESA’s Earth Explorers scientific satellites ADM-Aeolus and EarthCARE, but also heritage missions such as ENVISAT, MetOp and OMI Aura. However, the challenge is not only to manage the large volume of data generated by each mission / sensor, but also to manage the data variety. Tools are needed to be able to rapidly and trustfully identify, from all available datasets of a specific region for a specific timeframe, all available products for a selected field (e.g. ozone, trace gases) and prepare these data into a format that is ready to be extracted and used /analyzed (Analysis-Ready Data, ARD). Exploiting potential synergies to maximise the use of data from various sources will be key to harness the full potential of the available information. In summary, there is a need of an “intelligent” packaging of subsets of the available data tailored to the users’ needs. The scope of the “Atmospheric Mission Data Packaging” (AMiDA) project is to design, implement, and demonstrate the functionalities of an infrastructure for access and distribution of a wide variety of EO data in the field of atmospheric sciences: heritage, current, and future missions will be managed by the platform, to allow the users accessing, visualizing, and downloading a meaningful subset of this growing data stream. AMiDA (https://amida.adamplatform.eu/en/) makes use of the baseline functionalities provided by the TOP platform (http://top-platform.eu/) that already allows accessing and manipulating a large variety of satellite, model, and non-satellite remotely sensed data. TOP is empowered with spatial and temporal homogenization and packaging capabilities to create, from heterogeneous data sources (e.g., SO2 total column data from different satellites and numerical models) a single data structure (local data cube) for simultaneous exploitation of various data sources. The data cube can be exploited through the TOP tools (web application, Jupyter notebook and APIs) and downloaded by the user. A comprehensive demonstration campaign will be performed through five main use cases to demonstrate the capability of AMiDA. AMiDA is currently in its final development phase, thus the scope of the contribution is to present the initiative, preliminary results, and stimulate the discussion with potential users, analyzing their needs and see if and how they can use AMiDA to facilitate their everyday professional life.

Volcanic eruptions eject ash and gases into the atmosphere that can contribute to significant hazards to aviation, public and environment health, and the economy. Several volcanic ash transport and dispersion (VATD) models are in use to simulate volcanic ash transport operationally, but none include a treatment of volcanic ash aggregation processes. Volcanic ash aggregation can greatly reduce the atmospheric budget, dispersion and lifetime of ash particles, and therefore its impacts. To enhance our understanding and modeling capabilities of the ash aggregation process, a volcanic ash aggregation scheme was integrated into the Weather Research Forecasting with online Chemistry (WRFChem) model. Aggregation rates and ash mass loss in this modified code are calculated in line with the meteorological conditions, providing a fully coupled treatment of aggregation processes. The updated-model results were compared to field measurements of tephra fallout and in situ airborne measurements of ash particles from the April–May 2010 eruptions of Eyjafjallajökull volcano, Iceland. WRF-Chem, coupled with the newly added aggregation code, modeled ash clouds that agreed spatially and temporally with these in situ and field measurements. A sensitivity study provided insights into the mechanics of the aggregation code by analyzing each aggregation process (collision kernel) independently, as well as by varying the fractal dimension of the newly formed aggregates. In addition, the airborne lifetime (e-folding) of total domain ash mass was analyzed for a range of fractal dimensions, and a maximum reduction of 79.5 % of the airborne ash lifetime was noted.

Air pollution is becoming a stringent issue, especially in large urban agglomerations like Bucharest, Romania. National and European air quality regulations are focusing on enforcing emission and immission limit values, but there is a poor correlation between air quality policies and land use policies, both critical to urban development. The objective of this study is to develop a methodological framework for including air quality data in policies concerning urban development and land use change. The novelty is given by the use of high-resolution regional air quality modelling data for key pollutants like PM10, SO2 and NO2 with land use datasets. The study proposes a methodological framework based on the Urban Atlas Project's land use classes and the WRF-Chem air quality model. Through overlay analysis of the air quality model output and land use datasets, it is possible to derive two major types of information: (1) the land use area and classes exposed to certain pollutant concentration intervals and (2) the variability of the exposed land use classes according to the air pollutants source - receptor dynamics. Results are presented in the form of applications of the methodological framework in two different case studies. The methodological framework could be used as a tool for practitioners to include and use the evidence from air pollution information in the spatial planning policies and practice. In the first case study, three PM10, classes were set up in order to demonstrate the possibility to identify land use classes exposed to certain intervals of PM10, concentrations and their extent. The second case study focuses on the land use classes and their extent exposed to SO2 and NO2 concentrations in two different meteorological scenarios during two days in September 2014. This approach could provide support for decision makers in the policy development process, resulting in more informed decisions regarding future destinations of different land categories taking into account air pollution data and indicators. This study also opens the way for future air quality satellite data (ESA Sentinel 5-P and 5), with resolutions similar to the air quality model outputs to be used in land use planning and policy development purposes.

Volcanic eruptions may generate volcanic ash and sulfur dioxide (SO2) plumes with strong temporal and vertical variations. When simulating these changing volcanic plumes and the afar dispersion of emissions, it is important to provide the best available information on the temporal and vertical emission distribution during the eruption. The volcanic emission preprocessor of the chemical transport model WRF-Chem has been extended to allow the integration of detailed temporally and vertically resolved input data from volcanic eruptions. The new emission preprocessor is tested and evaluated for the eruption of the Grimsvötn volcano in Iceland 2011. The initial ash plumes of the Grimsvötn eruption differed significantly from the SO2 plumes, posing challenges to simulate plume dynamics within existing modelling environments: observations of the Grimsvötn plumes revealed strong vertical wind shear that led to different transport directions of the respective ash and SO2 clouds. Three source terms, each of them based on different assumptions and observational data, are applied in the model simulations. The emission scenarios range from (i) a simple approach, which assumes constant emission fluxes and a predefined vertical emission profile, to (ii) a more complex approach, which integrates temporarily varying observed plume-top heights and estimated emissions based on them, to (iii) the most complex method that calculates temporal and vertical variability of the emission fluxes based on satellite observations and inversion techniques. Comparisons between model results and independent observations from satellites, lidar, and surface air quality measurements reveal the best performance of the most complex source term.

Volcanic eruptions comprise an important airborne hazard for aviation. Although significant events are rare, e.g. compared to the threat of thunderstorms, they have a very high impact. The current state of tools and abilities to mitigate aviation hazards associated with an assumed volcanic cloud was tested within an international demonstration exercise. Experts in the field assembled at the Schwarzenberg barracks in Salzburg, Austria, in order to simulate the sequence of procedures for the volcanic case scenario of an artificial eruption of the Etna volcano in Italy. The scope of the exercise ranged from the detection (based on artificial observations) of the assumed event to the issuance of early warnings. Volcanic-emission-concentration charts were generated applying modern ensemble techniques. The exercise products provided an important basis for decision-making for aviation traffic management during a volcanic-eruption crisis. By integrating the available wealth of data, observations and modelling results directly into widely used flight-planning software, it was demonstrated that route optimization measures could be implemented effectively. With timely and rather precise warnings available, the new tools and processes tested during the exercise demonstrated vividly that a vast majority of flights could be conducted despite a volcanic plume being widely dispersed within a high-traffic airspace over Europe. The resulting number of flight cancellations was minimal.

ZAMG runs two models for air-quality forecasts operationally: ALARO-CAMx and WRF-Chem.ALARO-CAMx is a combination of the meteorological model ALARO and the photochemical dispersion model CAMx and is operated at ZAMG since 2005. The emphasis of this modeling system is to predict ozone peaks in the north-eastern Austrian flatlands. The outer model grid covers Central Europe with a resolution of 13.8 km, the inner domain is centered over Austria with a resolution of 4.6 km. The model runs twice per day for a period of 48 hours.The second operational air quality model at ZAMG is the on-line coupled model WRF-Chem. Meteorology is simulated simultaneously with the emission, turbulent mixing, transport, transformation as well as the fate of trace gases and aerosols. Two modeling domains are used for these simulations. The mother domain covers Europe with a resolution of 12 km. The inner, nested domain covers the Alpine region with a horizontal resolution of 4 km. The model runs two times per day for a period of 72 hours and is initialized with ECMWF forecasts.The evaluation of both models is conducted for the period from January to September 2019 with the focus on ozone. The summer 2019 was the 2nd warmest summer since the beginning of the meteorological measurements in Austria more than 200 years ago. Although this summer had favorable conditions for Ozone production (sunny and hot weather, less rain), only a few air quality stations in Eastern Austria have measured exceedances of the ozone information threshold (180 µg/m³) on overall 5 days. The measurements of the air-quality stations are compared with the area forecasts for every province of Austria. Besides the evaluation, air quality forecasts of ALARO-CAMx and WRF-Chem are compared.

The ongoing rise in missions to observe Earth from space, especially the various Copernicus’ Sentinel systems not only increases the volume of data daily, but also contributes to the variety of data, the velocity of data availability, and its veracity. In this scenario, Sentinel 5P has already changed the way in which chemical atmospheric components are monitored daily, providing data with global coverage and a very detailed spatial resolution. The discipline of atmospheric sciences poses an additional difficulty in efficiently accessing and analysing all available data: the variety is high as the source of atmospheric data is threefold with data coming from EO systems, models as well as in-situ measurements. The heterogeneity and multidimensionality of the so-called data triangle (EO, model, and in-situ data) make an efficient exploitation of the full potential of the available information even more challenging. Following the successful experience of the Technology and Atmospheric Mission Platform (TAMP), TOP (http://top-platform.eu/ ) implements the concept of operational Virtual Research Environment (VRE), allowing data users to access, visualize, process, and download heterogeneous, multidimensional data. Based on the ADAM datacube technology (https://adamplatform.eu), TOP allows exploiting the following datasets: Sentinel 5P Level 2 products (NO2 and O3 tropospheric columns, SO2, CO, and CH4 total columns, and aerosol index); Copernicus Atmosphere Monitoring Service (CAMS) global (surface PM10, total column NO2, SO2, and O3) and regional (surface PM10, NO2, SO2 and O3) analysis and forecast fields; European Environmental Agency (EEA) measurements (CO, NO2, PM10, SO2). Users can visualize and process all available data through a web application user interface (Data Analysis and Visualization Environment – DAVE), through a Jupyter notebook interface, and using the ADAM APIs and libraries to directly access available data. TOP is deployed on the Mundi DIAS infrastructure (https://mundiwebservices.com/). This allows accessing always most recent satellite products (reprocessed, offline, near real time), model output (analyses and forecasts – up to 5 days) and station measurements (full archive, updated daily). TOP is the first operational platform with the data triangle implemented. By creating an atmospheric multi-source data cube, it stimulates a multi-disciplinary scientific approach and significantly facilitates scientific professional life.