Mount Kelut (Indonesia) erupted explosively around 15:50 UT on 13 February 2014 sending ash and gases into the stratosphere. Satellite ash retrievals and dispersion transport modeling are combined ...within an inversion framework to estimate the volcanic ash source term and to study ash transport. The estimated source term suggests that most of the ash was injected to altitudes of 16–17 km, in agreement with space‐based lidar data. Modeled ash concentrations along the flight track of a commercial aircraft that encountered the ash cloud indicate that it flew under the main ash cloud and encountered maximum ash concentrations of 9 ± 3 mg m−3, mean concentrations of 2 ± 1 mg m−3over a period of 10–11 min of the flight, giving a dosage of 1.2 ± 0.3 g s m−3. Satellite data could not be used directly to observe the ash cloud encountered by the aircraft, whereas inverse modeling revealed its presence.
Key Points
Combining satellite retrievals and modeling gives valuable information
Volcanic ash source term is constrained by satellite observations
Estimates of modeled ash concentrations encountered by a commercial aircraft
The April–May, 2010 volcanic eruptions of Eyjafjallajökull, Iceland caused significant economic and social disruption in Europe whilst state of the art measurements and ash dispersion forecasts were ...heavily criticized by the aviation industry. Here we demonstrate for the first time that large improvements can be made in quantitative predictions of the fate of volcanic ash emissions, by using an inversion scheme that couples a priori source information and the output of a Lagrangian dispersion model with satellite data to estimate the volcanic ash source strength as a function of altitude and time. From the inversion, we obtain a total fine ash emission of the eruption of 8.3 ± 4.2 Tg for particles in the size range of 2.8–28 μm diameter. We evaluate the results of our model results with a posteriori ash emissions using independent ground-based, airborne and space-borne measurements both in case studies and statistically. Subsequently, we estimate the area over Europe affected by volcanic ash above certain concentration thresholds relevant for the aviation industry. We find that during three episodes in April and May, volcanic ash concentrations at some altitude in the atmosphere exceeded the limits for the "Normal" flying zone in up to 14 % (6–16 %), 2 % (1–3 %) and 7 % (4–11 %), respectively, of the European area. For a limit of 2 mg m−3 only two episodes with fractions of 1.5 % (0.2–2.8 %) and 0.9 % (0.1–1.6 %) occurred, while the current "No-Fly" zone criterion of 4 mg m−3 was rarely exceeded. Our results have important ramifications for determining air space closures and for real-time quantitative estimations of ash concentrations. Furthermore, the general nature of our method yields better constraints on the distribution and fate of volcanic ash in the Earth system.
Aerosols have important impacts on air quality and climate, but the processes affecting their removal from the atmosphere are not fully understood and are poorly constrained by observations. This ...makes modelled aerosol lifetimes uncertain. In this study, we make use of an observational constraint on aerosol lifetimes provided by radionuclide measurements and investigate the causes of differences within a set of global models. During the Fukushima Dai-Ichi nuclear power plant accident of March 2011, the radioactive isotopes cesium-137 (137Cs) and xenon-133 (133Xe) were released in large quantities. Cesium attached to particles in the ambient air, approximately according to their available aerosol surface area. 137Cs size distribution measurements taken close to the power plant suggested that accumulation-mode (AM) sulfate aerosols were the main carriers of cesium. Hence, 137Cs can be used as a proxy tracer for the AM sulfate aerosol's fate in the atmosphere. In contrast, the noble gas 133Xe behaves almost like a passive transport tracer. Global surface measurements of the two radioactive isotopes taken over several months after the release allow the derivation of a lifetime of the carrier aerosol. We compare this to the lifetimes simulated by 19 different atmospheric transport models initialized with identical emissions of 137Cs that were assigned to an aerosol tracer with each model's default properties of AM sulfate, and 133Xe emissions that were assigned to a passive tracer. We investigate to what extent the modelled sulfate tracer can reproduce the measurements, especially with respect to the observed loss of aerosol mass with time. Modelled 137Cs and 133Xe concentrations sampled at the same location and times as station measurements allow a direct comparison between measured and modelled aerosol lifetime. The e-folding lifetime τe, calculated from station measurement data taken between 2 and 9 weeks after the start of the emissions, is 14.3 days (95 % confidence interval 13.1–15.7 days). The equivalent modelled τe lifetimes have a large spread, varying between 4.8 and 26.7 days with a model median of 9.4 ± 2.3 days, indicating too fast a removal in most models. Because sufficient measurement data were only available from about 2 weeks after the release, the estimated lifetimes apply to aerosols that have undergone long-range transport, i.e. not for freshly emitted aerosol. However, modelled instantaneous lifetimes show that the initial removal in the first 2 weeks was quicker (lifetimes between 1 and 5 days) due to the emissions occurring at low altitudes and co-location of the fresh plume with strong precipitation. Deviations between measured and modelled aerosol lifetimes are largest for the northernmost stations and at later time periods, suggesting that models do not transport enough of the aerosol towards the Arctic. The models underestimate passive tracer (133Xe) concentrations in the Arctic as well but to a smaller extent than for the aerosol (137Cs) tracer. This indicates that in addition to too fast an aerosol removal in the models, errors in simulated atmospheric transport towards the Arctic in most models also contribute to the underestimation of the Arctic aerosol concentrations.
A data insertion method, where a dispersion model is initialized from ash properties derived from a series of satellite observations, is used to model the 8 May 2010 Eyjafjallajökull volcanic ash ...cloud which extended from Iceland to northern Spain. We also briefly discuss the application of this method to the April 2010 phase of the Eyjafjallajökull eruption and the May 2011 Grímsvötn eruption. An advantage of this method is that very little knowledge about the eruption itself is required because some of the usual eruption source parameters are not used. The method may therefore be useful for remote volcanoes where good satellite observations of the erupted material are available, but little is known about the properties of the actual eruption. It does, however, have a number of limitations related to the quality and availability of the observations. We demonstrate that, using certain configurations, the data insertion method is able to capture the structure of a thin filament of ash extending over northern Spain that is not fully captured by other modeling methods. It also verifies well against the satellite observations according to the quantitative object‐based quality metric, SAL—structure, amplitude, location, and the spatial coverage metric, Figure of Merit in Space.
Key Points
Eyjafjallajökull ash cloud is simulated using data insertion
Captures regional‐scale ash cloud structure
Results evaluated using spatial metrics
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.
Modeling the transport of volcanic ash and gases released during volcanic eruptions is crucially dependent on knowledge of the source term of the eruption, that is, the source strength as a function ...of altitude and time. For the first time, an inversion method is used to estimate the source terms of both volcanic sulfur dioxide (SO2) and ash. It was applied to the explosive volcanic eruption of Grímsvötn, Iceland, in May 2011. The method uses input from the particle dispersion model, FLEXPART (flexible particle dispersion model), a priori source estimates, and satellite observations of SO2 or ash total columns from Infrared Atmospheric Sounding Interferometer to separately obtain the source terms for volcanic SO2 and fine ash. The estimated source terms show that SO2 was emitted mostly to high altitudes (5 to 13 km) during about 18 h (22 May, 00–18 UTC) while fine ash was emitted mostly to low altitudes (below 4 km) during roughly 24 h (22 May 06 UTC to 23 May 06 UTC). FLEXPART simulations using the estimated source terms show a clear separation of SO2 (transported mostly northwestward) and the fine ash (transported mostly southeastward). This corresponds well with independent satellite observations and measured aerosol mass concentrations and lidar measurements at surface stations in Scandinavia. Aircraft measurements above Iceland and Germany confirmed that the ash was located in the lower atmosphere. This demonstrates that the inversion method, in this case, is able to distinguish between emission heights of SO2 and ash and can capture resulting differences in transport patterns.
Key Points
Ash and SO2 source terms estimated using inverse techniques and satellite data
The transport and separation of ash and SO2 are modeled
Model simulations correspond well with a range of independent observations
Volcanic eruptions can cause significant disruption to society, and numerical models are crucial for forecasting the dispersion of erupted material. Here we assess the skill and limitations of the ...Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) in simulating the dispersion of the sulfur dioxide (SO.sub.2) cloud from the 21-22 June 2019 eruption of the Raikoke volcano (48.3.sup." N, 153.2.sup." E). The eruption emitted around 1.5±0.2 Tg of SO.sub.2, which represents the largest volcanic emission of SO.sub.2 into the stratosphere since the 2011 Nabro eruption. We simulate the temporal evolution of the volcanic SO.sub.2 cloud across the Northern Hemisphere (NH) and compare our model simulations to high-resolution SO.sub.2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite SO.sub.2 products.
The requirement to forecast volcanic ash concentrations was amplified as a response to the 2010 Eyjafjallajökull eruption when ash safety limits for aviation were introduced in the European area. The ...ability to provide accurate quantitative forecasts relies to a large extent on the source term which is the emissions of ash as a function of time and height. This study presents source term estimations of the ash emissions from the Eyjafjallajökull eruption derived with an inversion algorithm which constrains modeled ash emissions with satellite observations of volcanic ash. The algorithm is tested with input from two different dispersion models, run on three different meteorological input data sets. The results are robust to which dispersion model and meteorological data are used. Modeled ash concentrations are compared quantitatively to independent measurements from three different research aircraft and one surface measurement station. These comparisons show that the models perform reasonably well in simulating the ash concentrations, and simulations using the source term obtained from the inversion are in overall better agreement with the observations (rank correlation = 0.55, Figure of Merit in Time (FMT) = 25–46%) than simulations using simplified source terms (rank correlation = 0.21, FMT = 20–35%). The vertical structures of the modeled ash clouds mostly agree with lidar observations, and the modeled ash particle size distributions agree reasonably well with observed size distributions. There are occasionally large differences between simulations but the model mean usually outperforms any individual model. The results emphasize the benefits of using an ensemble‐based forecast for improved quantification of uncertainties in future ash crises.
Key Points
The source parameters of volcanic emissions are crucial for ash forecasts
The source parameters can be substantially improved by assimilating observations
The simulated ash transport is improved with new source term estimates
During the 2010 eruption of Eyjafjallajökull, improvements were made to the modeling procedure at the Met Office, UK, enabling peak ash concentrations within the volcanic cloud to be estimated. In ...this paper we describe the ash concentration forecasting method, its rationale and how it evolved over time in response to new information and user requirements. The change from solely forecasting regions of ash to also estimating peak ash concentrations required consideration of volcanic ash emission rates, the fraction of ash surviving near‐source fall‐out, and the relationship between predicted mean and local peak ash concentrations unresolved by the model. To validate the modeling procedure, predicted peak ash concentrations are compared against observations obtained by ground‐based and research aircraft instrumentation. This comparison between modeled and observed peak concentrations highlights the many sources of error and the uncertainties involved. Despite the challenges of predicting ash concentrations, the ash forecasting method employed here is found to give useful guidance on likely ash concentrations. Predicted peak ash concentrations lie within about one and a half orders of magnitude of the observed peak concentrations. A significant improvement in the agreement between modeled and observed values is seen if a buffer zone, accounting for positional errors in the predicted ash cloud, is used. Sensitivity of the predicted ash concentrations to the source properties (e.g., the plume height and the vertical distribution of ash at the source) is assessed and in some cases, seemingly minor uncertainties in the source specification have a large effect on predicted ash concentrations.
Key Points
A method for forecasting peak volcanic ash concentrations is described
Method validated using observations from the 2010 Eyjafjallajokull eruption
Uncertainties in modeled ash concentrations are numerous and large
Isolates of Escherichia coli from 31 Norwegian and 31 Russian females with significant bacteruria who presented with clinical signs of urinary tract infection (UTI) were tested for antimicrobial ...sensitivity, the presence of virulence genes, phylogroup distribution and clonal affinity. Twenty isolates, representing the full clonal diversity of a collection of 138 intestinal isolates of E. coli from healthy Norwegian females, served as a reference group. Russian UTI isolates belonged more often to phylogroup A and possessed fewer virulence genes than did Norwegian isolates. UTI isolates of E. coli were genetically heterogeneous and had a high degree of antimicrobial sensitivity.
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