With the increasing usage of renewable energy systems to meet the climate agreement aims accurate predictions of the possible amount of energy production stemming from renewable energy systems are needed. The need for such predictions and their uncertainty is manifold: to estimate the load on the power grid, to take measures in case of too much/not enough renewable energy with reduced nuclear energy availability, rescheduling/adjusting of energy production, maintenance, trading, and more. Furthermore, TSOs and energy providers need the information as finegrained, spatially and temporarily, as possible, on third level hub or even on solar farm / wind turbine level for a comparatively large area. These needs pose a challenge to numerical weather prediction (NWP) post-processing methods. Typically, one uses selected NWP fields aswell as observations, if available, as input in post-processing methods. Here, we combine two post-processing methods namely a neural network and random forest approach with the Flex_extract algorithm. Flex_extract is the pre-processing algorithm for the langrangian particle dispersion model FLEXPART and the trajectory model FLEXTRA. Flex_extract uses the three-dimensional wind fields of the NWP model and calculates additionally the instantaneous surfaces fluxes. Thus, coupling Flex_extract with a machine learning post-processing algorithm enables the usage of native NWP fields with a higher vertical accuracy than pressure levels. To generate an ensmeble in post-processing from deterministic sources different tools are available. Here, we will apply the Schaake Shuffle. In this study a neural network and random forest approach for probabilistic forecasting with a high horizontal grid resolution (1 km ) as well as a high temporal forecasting frequency of wind speed and global horizontal irradiance for Austria will be presented. Evaluation will be carried out against gridded analysis fields and observations.

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.

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.

The Lagrangian particle dispersion model FLEXPART in its original version in the mid-1990s was designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as those released after an accident in a nuclear power plant. Over the past decades, the model has evolved into a comprehensive tool for multi-scale atmospheric transport modeling and analysis and has attracted a global user community. Its application fields have been extended to a large range of atmospheric gases and aerosols, e.g., greenhouse gases, short-lived climate forcers like black carbon and volcanic ash, and it has also been used to study the atmospheric branch of the water cycle. Given suitable meteorological input data, it can be used for scales from dozens of meters to global. In particular, inverse modeling based on source–receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10.4, which works with meteorological input data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) and data from the United States National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEXPART description (version 6.2), the model has been improved in different aspects such as performance, physicochemical parameterizations, input/output formats, and available preprocessing and post-processing software. The model code has also been parallelized using the Message Passing Interface (MPI). We demonstrate that the model scales well up to using 256 processors, with a parallel efficiency greater than 75 % for up to 64 processes on multiple nodes in runs with very large numbers of particles. The deviation from 100 % efficiency is almost entirely due to the remaining nonparallelized parts of the code, suggesting large potential for further speedup. A new turbulence scheme for the convective boundary layer has been developed that considers the skewness in the vertical velocity distribution (updrafts and downdrafts) and vertical gradients in air density. FLEXPART is the only model available considering both effects, making it highly accurate for small-scale applications, e.g., to quantify dispersion in the vicinity of a point source. The wet deposition scheme for aerosols has been completely rewritten and a new, more detailed gravitational settling parameterization for aerosols has also been implemented. FLEXPART has had the option of running backward in time from atmospheric concentrations at receptor locations for many years, but this has now been extended to also work for deposition values and may become useful, for instance, for the interpretation of ice core measurements. To our knowledge, to date FLEXPART is the only model with that capability. Furthermore, the temporal variation and temperature dependence of chemical reactions with the OH radical have been included, allowing for more accurate simulations for species with intermediate lifetimes against the reaction with OH, such as ethane. Finally, user settings can now be specified in a more flexible namelist format, and output files can be produced in NetCDF format instead of FLEXPART's customary binary format. In this paper, we describe these new developments. Moreover, we present some tools for the preparation of the meteorological input data and for processing FLEXPART output data, and we briefly report on alternative FLEXPART versions.

There is little information to decision support in air traffic management in case of nuclear releases into the atmosphere. In this paper, the dose estimation due to both, external exposure (i.e. cloud immersion, deposition inside and outside the aircraft), and due to internal exposure (i.e, inhalation of radionuclides inside the aircraft) to passengers and crew is calculated for a worst-case emergency scenario. The doses are calculated for different radionuclides and activities. Calculations are mainly considered according to International Commission on Radiological Protection (ICRP) recommendations and Monte Carlo simulations. In addition, a discussion on potential detectors installed inside the aircraft for monitoring the aerosol concentration and the ambient dose equivalent rate, H*(10), for during-flight monitoring and early warning is provided together with the evaluation of a response of a generic detector. The results show that the probability that a catastrophic nuclear accident would produce significant radiological doses to the passengers and crew of an aircraft is very low. In the worst-case scenarios studied, the maximum estimated effective dose was about 1 mSv during take-off or landing operations, which is the recommended yearly threshold for the public. However, in order to follow the ALARA (As Low As Reasonably Achievable) criteria and to avoid aircraft contamination, the installation of radiological detectors is considered. This would, on one hand help the pilot or corresponding decision maker to decide about the potential change of the route and, on the other, allow for gathering of 4D data for future studies.

Abstract. The Lagrangian particle dispersion model FLEXPART was in its original version in the mid-1990s designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as released after an accident in a nuclear power plant. Over the past decades, the model has evolved into a comprehensive tool for multi-scale atmospheric transport modelling and analysis and has attracted a global user community. Its application fields have been extended to a large range of atmospheric gases and aerosols, e.g. greenhouse gases, short-lived climate forcers like black carbon, or volcanic emissions, and it has also been used to study the atmospheric branch of the water cycle. Given suitable meteorological input data, it can be used for scales from dozens of meters to the global scale. In particular, inverse modelling based on source-receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10.3, which works with meteorological input data from the European Centre for Medium-Range Weather Forecasts' (ECMWF) Integrated Forecast System (IFS), and data from the United States' National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEXPART description (version 6.2), the model has been improved in different aspects such as performance, physico-chemical parametrizations, input/output formats and available pre- and post-processing software. The model code has also been parallelized using the Message Passing Interface (MPI). We demonstrate that the model scales well up to using 256 processors, with a parallel efficiency greater than 75 % for up to 64 processes on multiple nodes. The deviation from 100 % efficiency is almost entirely due to remaining non-parallelized parts of the code, suggesting large potential for further speed-up. A new turbulence scheme for the convective boundary layer has been developed that considers the skewness in the vertical velocity distribution (updrafts and downdrafts) and vertical gradients in air density. FLEXPART is the only model available considering both effects, making it highly accurate for small-scale applications, e.g. to quantify dispersion in the vicinity of a point source. The wet deposition scheme for aerosols has been completely rewritten and a new, more detailed gravitational settling parameterization for aerosols has also been implemented. FLEXPART has had the option for running backward in time from atmospheric concentrations at receptor locations since many years, but this has now been extended to work also for deposition values and may become useful, for instance, for the interpretation of ice core measurements. To our knowledge, to date FLEXPART is the only model with that capability. Furthermore, temporal variation and temperature dependence of chemical reactions with the OH radical have been included, allowing more accurate simulations for species with intermediate lifetimes against the reaction with OH, such as ethane. Finally, user settings can now be specified in a more flexible namelist format, and output files can be produced in NetCDF format instead of FLEXPART's customary binary format. In this paper, we describe these new developments. Moreover, we present some tools for the preparation of the meteorological input data and for processing of FLEXPART output data and briefly report on alternative FLEXPART versions.

An operational framework is developed to provide timely and frequent source term updates for volcanic emissions (ash and SO2). The procedure includes running the Lagrangian particle dispersion model FLEXPART with an initial (a priori) source term, and combining the output with observations (from satellite, ground-based, etc. sources) to obtain an a posteriori source term. This work was part of the EUNADICS-AV (eunadics-av.eu), which is a continuation of the work developed in the VAST project (vast.nilu.no). The aim is to ensuring that at certain time intervals when new observational and meteorological data is available during an event, an updated source term is provided to analysis and forecasting groups. The system is tested with the Grimsvötn eruption of 2011. Based on a source term sensitivity test, one can find the optimum between a sufficiently detailed source term and computational resources. Because satellite and radar data from different sources is available at different times, the source term is generated with the data that is available the earliest after the eruption started and data that is available later is used for evaluation.

Explosive volcanic eruptions can inject large amounts of ash and gases into the atmosphere. Such volcanic aerosols can have a significant impact on the surrounding environment, and there is the need to closely investigate their effects on meteorology on local, regional, and even continental scale. This work presents a study of the 2010 Eyjafjallajokull volcanic eruption the resulting ash dispersion and its radiative feedback effects on the meteorological conditions with the Weather Research Forecasting model with on-line Chemistry (WRF-Chem). Two model runs, one meteorology-only simulation (without chemistry) and one that considers gas- and aerosol chemistry as well as direct- and semidirect aerosol feedbacks were performed and compared. Results for daily values show that aerosol radiative feedback effects can cool the atmosphere close to the surface on average by 1 degrees C with maximum cooling exceeding even 2 degrees C for the considered episode. Near-surface atmospheric wind speed changed on average by 0.5 m/s with maximum values above 2 m/s. Furthermore, the presence of ash aerosols affected the vertical shape of the profiles of wind speed and temperature and resulted in a better agreement with radiosonde measurements when radiative feedback effects were considered. Although the modeling of the dispersion of volcanic ash clouds is subject to large uncertainties, we have demonstrated that the WRF-Chem model can reproduce observations at surface levels and vertical profiles more realistically when radiative feedback effects are considered in the simulations.

Volcanic emissions of ash and SO2 can be harmful to both human health, infrastructure, and aviation. In assessing these hazards, satellite remote sensing and transport modeling are useful tools. Satellite observations can track the volcanic clouds in the atmosphere, their main limitation being little vertical information. Transport models are used to forecast the three-dimensional atmospheric dispersion of the volcanic emissions, but large uncertainties are involved. The main sources of uncertainties are lacking knowledge of the emissions (source term), errors in the meteorological data driving the dispersion model, and the model's description of physical processes such as loss mechanisms. One of the keys for achieving more reliable simulations is incorporating observation data into the models. In this chapter, we demonstrate three techniques useful for enhancing model simulations and for evaluating model uncertainties. First, the source term can be constrained by satellite observations via inverse modeling. Second, errors in the transport model and input data can be corrected for with data assimilation by updating the modeled fields with up-to-date observations. Third, uncertainties can be inferred from ensemble modeling. The Grimsvotn 2011 eruption is used to demonstrate how these three techniques together can improve the model simulations of volcanic clouds and determine model uncertainties.

(1) Central Institute for Meteorology and Geodynamics, Data, Methods and Modelling, Vienna, Austria (gerhard.wotawa@zamg.ac.at), (2) Brimatech Services GmbH, Vienna, Austria, (3) Consiglio Nazionale delle Ricerche (CNR), Italy, (4) Finish Meteorological Institute (FMI), Helsinki, Finland, (5) Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium, (6) Meteo France (MF), Toulouse, France, (7) Paris Lodron University of Salzburg, Austria, (8) Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands