Volcanic Ash Dispersion Research Articles

Analysis of Japanese Volcanic Ash Dispersion on the Korean Peninsula using Satellite Imagery

A volcanic eruption is a kind of global natural disaster that can occur suddenly and cause great damage to humankind. During the eruption, the magma causes fatal damage to life and property in areas near the volcano, and nearby countries are affected by the spread of volcanic ash, causing secondary damage due to air and soil pollution. Near the Korean peninsula, there exists an active volcano that can spread volcanic ash over long distances by erupting above Volcanic Explosivity Index (VEI) 4. Volcanoes in Japan have been known to cause considerable volcanic ash damage on the Korean Peninsula during eruption. Accordingly, the Korea Meteorological Administration is developing technology to predict and monitor volcanic ash spread using satellite images. However, despite the fact that empirical models for volcanic ash diffusion range prediction are used during volcanic eruptions, continuous improvement is needed for accurate information prediction. In this study, satellite images were analyzed not for the predicted distance of volcanic ash clouds, but for the actual distance of volcanic ash dispersion in cases where the volcanic ashes dispersed in the direction of the Korean peninsula. Of the 3,880 volcanoes that erupted in Japan over the last four years, 111 cases were identified where the height and spread distance of the volcanic ash that erupted toward the Korean Peninsula can be confirmed. In addition, the actual volcanic eruption cases and modeling results were analyzed to determine the extent of volcanic ash spread, and a hypothetical scenario was tested to quantify the direct damage of the volcanic ash. From the analysis of the volcanic ash spread through the virtual simulations, it was found that the height of the volcanic ash, the direction of the wind, and wind speed during volcanic eruption were important factors.

Sensitivity of Volcanic Ash Dispersion Modelling to Input Grain Size Distribution Based on Hydromagmatic and Magmatic Deposits

The size distribution of volcanic ash is rarely measured in real time and Volcanic Ash Advisory Centres (VAACs) often rely on a default particle size distribution (PSD) to initialise their dispersion models when forecasting the movement of ash clouds. We conducted a sensitivity study to investigate the impact of PSD on model output and consider how best to apply default PSDs in operational dispersion modelling. Compiled grain size data confirm that, when considering particles likely to be in the distal ash cloud (< 125 µm diameter), magma composition and eruption size are the dominant controls on grain size distribution. Constraining the PSD is challenging but we find that the grain size of deposits from large hydromagmatic eruptions remains relatively constant with distance, suggesting that total (whole-deposit) grain size distributions (TGSDs) for these eruptions could be estimated from a few samples. We investigated the sensitivity of modelled ash mass loadings (in the air and on the ground) to input PSDs based on coarse to fine TGSDs from our dataset. We found clear differences between modelled mass loadings and the extent of the plume. Comparing TGSDs based on ground-only and ground-plus-satellite data for the Eyjafjallajökull 2010 eruption, we found that basing input PSDs on TGSDs from deposits alone (likely missing the finest particles) led to lower modelled peak ash concentrations and a smaller plume.

Volcanic ash forecast using ensemble-based data assimilation: an ensemble transform Kalman filter coupled with the FALL3D-7.2 model (ETKF–FALL3D version 1.0)

Abstract. Quantitative volcanic ash cloud forecasts are prone to uncertainties coming from the source term quantification (e.g., the eruption strength or vertical distribution of the emitted particles), with consequent implications for an operational ash impact assessment. We present an ensemble-based data assimilation and forecast system for volcanic ash dispersal and deposition aimed at reducing uncertainties related to eruption source parameters. The FALL3D atmospheric dispersal model is coupled with the ensemble transform Kalman filter (ETKF) data assimilation technique by combining ash mass loading observations with ash dispersal simulations in order to obtain a better joint estimation of the 3-D ash concentration and source parameters. The ETKF–FALL3D data assimilation system is evaluated by performing observing system simulation experiments (OSSEs) in which synthetic observations of fine ash mass loadings are assimilated. The evaluation of the ETKF–FALL3D system, considering reference states of steady and time-varying eruption source parameters, shows that the assimilation process gives both better estimations of ash concentration and time-dependent optimized values of eruption source parameters. The joint estimation of concentrations and source parameters leads to a better analysis and forecast of the 3-D ash concentrations. The results show the potential of the methodology to improve volcanic ash cloud forecasts in operational contexts.

New model for dispersion of volcanic ash and dust in the troposphere.

Dispersion of volcanic ash and dust is traditionally modeled as advection and Gaussian diffusion. This is the tradition in treating smoke stack plumes. About 100 meters above earth the velocity profile may disintegrate, diffusion coefficients become rather unpredictable and stratified flow occur. It is suggested that gravitational flattening may be the main cause of dispersion in dust plumes above the turbulent boundary layer. A dust plume in between two layers of small temperature difference has a certain carrying capacity of dust. The corresponding mass loading can be estimated from the temperature difference between the layers above and beneath the plume. Such dust plumes will be forced to jettison a load they may have in excess of this carrying capacity; this may be seen as streak fallout from the plume. In the same time, the plume will be subjected to gravitational flattening to the sides, in addition to any diffusion if there is any. The plume width resulting from the flattening may be estimated from the temperature difference. This can explain the behavior of plumes like the plume from the Eyjafjallaj?kull 2010 in absence of diffusion. In the long run diffusion and gravitational flattening will cause different developments of the plume width. Gravitational flattening and streak fallouts are important elements from plume physics not included in most plume models. It is concluded that modelling dust plumes with diffusion and ordinary fallout only; can cause serious errors in the model, the simulated plumes will become too big. To avoid them, the new model should be included in dust models in the same manner as the turbulent diffusion, i.e. as a sub grid model. Then, the plume model only needs to include horizontal turbulent diffusion of the same order of magnitude as the vertical one.

Mixed local and ultra-distal volcanic ash deposition within the Upper Cretaceous Kanguk Formation, Sverdrup Basin, Canadian Arctic Islands

AbstractThe Upper Cretaceous Kanguk Formation of the Sverdrup Basin, Canadian Arctic Islands, contains numerous diagenetically altered volcanic ash layers (bentonites). Eleven bentonites were sampled from an outcrop section on Ellesmere Island for U–Pb zircon secondary ion mass spectrometry dating and whole-rock geochemical analysis. Two distinct types of bentonite are identified from the geochemical data. Relatively thick (0.1 to 5 m) peralkaline rhyolitic to trachytic bentonites erupted in an intraplate tectonic setting. These occur throughout the upper Turonian to lower Campanian (c. 92–83 Ma) outcrop section and are likely associated with the alkaline phase of the High Arctic Large Igneous Province. Two thinner (<5 cm) subalkaline dacitic to rhyolitic bentonites of late Turonian to early Coniacian age (c. 90–88 Ma) are also identified. The geochemistry of these bentonites is consistent with derivation from volcanoes within an active continental margin tectonic setting. The lack of nearby potential sources of subalkaline magmatism, together with the thinner bed thickness of the subalkaline bentonites and the small size of zircon phenocrysts therein (typically 50–80 μm in length) are consistent with a more distal source area. The zircon U–Pb age and whole-rock geochemistry of these two subalkaline bentonites correlate with an interval of intense volcanism in the Okhotsk–Chukotka Volcanic Belt, Russia. It is proposed that during late Turonian to early Coniacian times intense volcanism within the Okhotsk–Chukotka Volcanic Belt resulted in widespread volcanic ash dispersal across Arctic Alaska and Canada, reaching as far east as the Sverdrup Basin, more than 3000 km away.

Application of C-band Doppler Weather Radar (CDR) for Detecting Volcanic Ash Dispersion of Sinabung Eruption 19 February 2018

The weather radar is generally employed to forecast the potential of hydrometeorological hazard. In the present paper, we focus on the application of C-band Doppler Weather Radar (CDR) to identify February 19th, 2018 Sinabung volcanic eruption and its dispersion by using a direct product in the form of CMAX (Column Max) retrieved from reflectivity factor (dBz). The distribution of reflectivity factor varies from the center of the eruption to the farther location indicating the dispersion of material size erupted by the Sinabung. Lapili which is larger material size with 48dBz tends to locate in the nearby volcanic area. The medium size of the material like coarse volcanic ash with 33 dBz of reflectivity was floating in the air and carried away by the wind. Meanwhile fine volcanic ash with 10 dBz of reflectivity flies a great distance along with the wind.

Special Issue on Integrated Study on Mitigation of Multimodal Disasters Caused by Ejection of Volcanic Products: Part 2

Our research project titled “Integrated study on mitigation of multimodal disasters caused by ejection of volcanic products” began in 2014 under SATREPS (Science and Technology Research Partnership for Sustainable Development) and is now coming to an end in 2019. Indonesia has 127 active volcanoes distributed along its archipelago making it a high risk location for volcano-related disasters. The target volcanoes in our study are Guntur, Galunggung, Merapi, Kelud, and Semeru in Java, and Sinabung in North Sumatra. Guntur and Galunggung are currently dormant and are potentially high-risk volcanoes. Merapi generated pyroclastic flows along the Gendol River in 2010, which resulted in over 300 casualties and induced frequent lahars. New eruptive activity of Merapi began in 2018. The 2014 eruption of Kelud formed a gigantic ash plume over 17 km high, dispersing ash widely over the island of Java. Semeru continued minor eruptive activity, accompanying a risk of a dome collapse. The aim of our research includes disaster mitigation of the Sinabung volcano, whose eruption began to form a lava dome at its summit at the end of 2013, followed by frequent pyroclastic flows for approximately 4 years, and the deposits became the source of rain-triggered lahars. Our goal is to implement SSDM (Support System for Decision-Making), which would allow us to forecast volcano-related hazards based on scales and types of eruptions inferred from monitoring data. This special issue collects fundamental scientific knowledge and technology for the SSDM as output from our project. The SSDM is an integrated system of monitoring, constructed scenarios, forecasting scale of eruption, simulation of sediment movement and volcanic ash dispersion in the atmosphere. X-band radars newly installed by our project in Indonesia were well utilized for estimation of spatial distribution not only of rain fall in catchments but also of volcanic ash clouds. Finally, we hope the SSDM will continue to be utilized under a consortium in Merapi, which was newly established in collaboration with our projects, and extended to other volcanoes.

Numerical Simulations of Volcanic Ash Plume Dispersal for Sakura-Jima Using Real-Time Emission Rate Estimation

In this study, a real-time volcanic ash dispersion model called PUFF is applied to the Sakura-jima volcano erupted on 16 June 2018 to assess the performance of the new system connected with a real-time emission rate estimation. The emission rate of the ash mass from the vent is estimated based on an empirical formula developed for the Sakura-jima volcano using seismic monitoring and ground deformation data. According to the time series of the estimated emission rate, a major eruption occurred at 7:20 JST indicating an emission rate of 1000 t/min and continued for 15 min showing a plume height of 4500 m. It is observed that we need to introduce an adjusting constant to fit the model prediction of the ash fallout with the ground observation. Once the particle mass is calibrated, the distributions of ash fallout are compared with other eruption events to confirm the model performance. According to the PUFF model simulations, an airborne ash concentration of 100 mg/m3extends to a wide area around the volcano within one hour after the eruption. The simulation result quantitatively indicates the location of the danger zone for commercial airliners. The PUFF model system combined with the real-time emission rate estimation is useful for aviation safety purposes as well as for ground transportation and human health around active volcanoes.

Time evolution of transient volcanic plumes: Insights from fractal analysis

Transient volcanic plumes are time-dependent features generated by unsteady eruptive sources, having similar eruption duration and plume development timescales. Their morphological evolution reflects both the discharge history at the vent and air entrainment, crucial parameters controlling volcanic ash dispersal and impact on the environment and human activities. However, despite its importance, transient plume morphology has been scarcely quantified, due to both observational and analytical hindrances. In this study, we introduce new tools to quantify the initial morphological evolution of transient volcanic plumes by applying fractal analysis and plume's perimeter measurements to thermal high-speed and visible-light high-resolution videos of eruptions. Eruptive plumes from Sakurajima (Japan), Stromboli (Italy), and Fuego (Guatemala) volcanoes were recorded during several field campaigns in 2012–2016. The eruption dataset has been complemented by the fractal analysis of three 2D numerical gas-jet simulations at different Reynolds number (2 × 103, 5 × 103 and 10 × 103) in order to provide reference configurations to compare with the natural cases. The two shape analysis methods used show different sensitivities. The ratio between plume and bounding box perimeters appears to be more perceptive of punctual dynamical variations, while fractal analysis reflects the overall plume evolution. Both methods highlight that plume shape complexity increases over time and is related to the formation and development of smaller scale vortexes. The variability of the rate of fractal dimension increase over time (αD) effectively captures plume evolution. It also appears that αD correlates with the ash eruption rate (AER) evolution and the instability of the source. This study shows that discharge history and intensity at the vent are the first order control on plume's shape evolution and, by inference, on its air entrainment ability.

Scenario based volcanic hazard assessment from ash dispersion

Before the eruption of Eyjafjallajökull in 2010, the aviation disasters in such an enormous scale have not realistically been expected. Volcanic eruptions and earthquakes are not only rare but also very sudden events of which prediction and preparedness are often impossible. This study present a scenario based strategy to assess the hazard from the volcanic ash dispersion by combining computational simulation of the ash dispersions and ensemble method to reduce the uncertainty. Due to lack of information in advance, a scenario is designed to consider a range of parameters of the eruption such as particulate volume and eruption strength. The meteorological data are generated over past thousands days by numerical weather forecasting model in this study. With the given conditions in each scenario, the volcanic ash dispersion is simulated using Eulerian based scalar transport model. Once the large amount of the results from ash dispersion simulations are compiled, the meteorological conditions are classified to several groups according to meteorological similarity using K-mean method. Since tens of days are grouped to each similar weather condition, a set of ash dispersion results corresponding to a specified similarity group then is ensemble averaged to generate the representative hazard.

A cryptotephra record from the Lake Victoria sediment core record of Holocene palaeoenvironmental change

The sediment record from Lake Victoria is an important archive of regional environmental and climatic conditions, reaching back more than 15,000 cal. years before present (15 ka BP). As the largest lake by area in East Africa, its evolution is key to understanding regional palaeohydrological change during the late Pleistocene and Holocene, including controls on the Nile River flow. As well as important palaeoenvironmental proxies, the lake contains a unique record of explosive volcanism from the central Kenyan Rift, in the form of fine-grained volcanic ash (tephra) layers, interpreted as airfall deposits. In the V95-1P core, collected from the central northern basin of the lake, tephra layers vary in concentration from 10s to 10s of 1000s of glass shards per gram of sediment. None of the tephra are visible to the naked eye, and have only been revealed through careful laboratory processing. Compositional analyses of tephra glass shards has allowed the tephra layers to be correlated to previously unrecognized eruptions of Eburru volcano around 1.2 and 3.8 ka, and Olkaria volcano, prior to 15 ka. These volcanoes lie ~300 km east of the core site in the Kenyan Rift. Our results highlight the potential for developing cryptotephra analysis as a key tool in East African palaeolimnological research. Tephra layers offer opportunities for precise correlation of palaeoenvironmental sequences, as well as windows into the eruption frequency of regional volcanoes and the dispersal of volcanic ash.

The impact of particle shape on fall velocity: Implications for volcanic ash dispersion modelling

Modelling atmospheric volcanic ash dispersion is a critical tool in mitigating the impact of large explosive eruptions; it is also useful for understanding and reconstructing past events. Most atmospheric dispersion models include a sedimentation velocity term that is sensitive to the physical properties of the particle, but many do not use particle shape as an input parameter; instead particles are assumed to be spherical. There are many empirical and semi-empirical shape-dependent drag laws. We measure the velocity of scaled analogue particles over the range of flow conditions anticipated for volcanic ash dispersion to test published formulae against an independent dataset. We use a semi-empirical formula we determined to be accurate for non-spheres to investigate the sensitivity of the modelled transport of an ash cloud to particle shape, using the atmospheric dispersion model NAME and a shape parameter we measure for non-spherical ash particles from Katla volcano, Iceland. We find that model particle trajectories are sensitive to particle shape for particles >1–3 μm diameter; the sedimentation velocity of smaller particles is low compared to atmospheric vertical velocities. Sensitivity to shape increases with size such that 100 μm particles can travel 44% further from the source when they are highly non-spherical (sphericity = 0.5). Despite the sensitivity of the fall velocity of large particles to their shape, however, forecasts of distal ash concentration using particle size distributions of 0.1–100 μm and 0.1–250 μm show relatively good agreement between a spherical and non-spherical case for the first 36 h after an eruption. The vertical structure of an ash cloud is more sensitive to particle shape than the horizontal extent. Model particle trajectories are also sensitive to particle size, and we find a discrepancy between different particle size parameters for non-spherical ash: particle long axis L, used in cryptotephra studies, was on average twice the equivalent-volume sphere diameter dv, used in dispersion modelling, for tephra samples from Katla volcano, Iceland. This discrepancy in size measurements could explain the observed travel distance of large distal cryptotephra shards.

Meteorological Controls on Local and Regional Volcanic Ash Dispersal

Volcanic ash has the capacity to impact human health, livestock, crops and infrastructure, including international air traffic. For recent major eruptions, information on the volcanic ash plume has been combined with relatively coarse-resolution meteorological model output to provide simulations of regional ash dispersal, with reasonable success on the scale of hundreds of kilometres. However, to predict and mitigate these impacts locally, significant improvements in modelling capability are required. Here, we present results from a dynamic meteorological-ash-dispersion model configured with sufficient resolution to represent local topographic and convectively-forced flows. We focus on an archetypal volcanic setting, Soufrière, St Vincent, and use the exceptional historical records of the 1902 and 1979 eruptions to challenge our simulations. We find that the evolution and characteristics of ash deposition on St Vincent and nearby islands can be accurately simulated when the wind shear associated with the trade wind inversion and topographically-forced flows are represented. The wind shear plays a primary role and topographic flows a secondary role on ash distribution on local to regional scales. We propose a new explanation for the downwind ash deposition maxima, commonly observed in volcanic eruptions, as resulting from the detailed forcing of mesoscale meteorology on the ash plume.

Identification Characteristic Dispersion of Volcanic Ash Using PUFF Model with Weather Radar on Eruption of Mt. Rinjani August 2016

Observing volcanic ash dispersion becomes critical following the abundance incidents of aircraft damage caused by volcanic ash, especially in Indonesia that located along the Pacific ring of fire. To enable an accurate forecast of volcanic ash dispersion, model PUFF offers information regarding the direction of volcanic ash dispersion based on height and size of volcanic ash. This research shows that model PUFF serves better explanation over volcanic ash advisory from VAAV Darwin and information derived from the Himawari-8 satellite. This research also further suggests for PUFF model to utilize data regarding eruption time and plume’s height provided in the interpretation result of weather radar.

Characterizing the Atmospheric Conditions Leading to Large Error Growth in Volcanic Ash Cloud Forecasts

ABSTRACTVolcanic ash poses an ongoing risk to safety in the airspace worldwide. The accuracy with which volcanic ash dispersion can be forecast depends on the conditions of the atmosphere into which it is emitted. In this study, meteorological ensemble forecasts are used to drive a volcanic ash transport and dispersion model for the 2010 Eyjafjallajökull eruption in Iceland. From analysis of these simulations, the authors determine why the skill of deterministic-meteorological forecasts decreases with increasing ash residence time and identify the atmospheric conditions in which this drop in skill occurs most rapidly. Large forecast errors are more likely when ash particles encounter regions of large horizontal flow separation in the atmosphere. Nearby ash particle trajectories can rapidly diverge, leading to a reduction in the forecast accuracy of deterministic forecasts that do not represent variability in wind fields at the synoptic scale. The flow‐separation diagnostic identifies where and why large ensemble spread may occur. This diagnostic can be used to alert forecasters to situations in which the ensemble mean is not representative of the individual ensemble‐member volcanic ash distributions. Knowledge of potential ensemble outliers can be used to assess confidence in the forecast and to avoid potentially dangerous situations in which forecasts fail to predict harmful levels of volcanic ash.

Prediction of Dispersal Directions and Ranges of Volcanic Ashes from the Possible Eruption of Mt. Baekdu

To predict the influence of volcano eruption on agriculture in South Korea we evaluated the dispersal ranges of the volcanic ashes toward the South Korea based on the possibilities of volcano eruption in Mt. Baekdu. The possibilities of volcano eruption in Mt. Baekdu have been still being intensified by the signals including magmatic unrest of the volcano and the frequency of volcanic earthquakes swarm, the horizontal displacement and vertical uplift around the Mt. Baekdu, the temperature rises of hot springs, high ratios of N2/O2 and 3He/4He in volcanic gases. The dispersal direction and ranges and the predicted amount of volcanic ash can be significantly influenced by Volcanic Explosivity Index (VEI) and the trend of seasonal wind. The prediction of volcanic ash dispersion by the model showed that the ash cloud extended to Ulleung Island and Japan within 9 hours and 24 hours by the northwestern monsoon wind in winter while the ash cloud extended to northern side by the south-east monsoon wind during June and September. However, the ash cloud may extent to Seoul and southwest coast within 9 hours and 15 hours by northern wind in winter, leading to severe ash deposits over the whole area of South Korea, although the thickness of the ash deposits generally decreases exponentially with increasing distance from a volcano. In case of VEI 7, the ash deposits of Daejeon and Gangneung are 1.31 × 104 g m-2 and 1.80 × 105 g m-2, respectively. In addition, ash particles may compact close together after they fall to the ground, resulting in increase of the bulk density that can alter the soil physical and chemical properties detrimental to agricultural practices and crop growth. Schematic assumption for directions and ranges of the dispersal of volcanic ash due to eruption of Mt, Baekdu (modified from Lee, 2011).

Estimation of optimal dispersion model source parameters using satellite detections of volcanic ash

AbstractIn this paper we demonstrate how parameters describing the geometry of the volcanic ash source for a particular volcanic ash dispersion model (Hybrid Single‐Particle Lagrangian Integrated Trajectory (HYSPLIT)) may be inferred by the use of satellite data and multiple trial simulations. The areas of space likely to be contaminated by ash are identified with the aid of various remote sensing techniques, and polygons are drawn around these areas as they would be in an operational setting. Dispersion model simulations are initialized by either a cylindrical source or a specified ash distribution depending on the context. Parameters of interest such as the base and top height, diameter, and optimal release time of the cylindrical source or the height of the specified ash distribution are inferred by forming a parameter grid and running multiple simulations for each parameter grid point value. Optimal values of the parameter values are identified by calculating spatial correlations between the model simulations and observations. We demonstrate that the methodology can be used to correctly infer various model parameters and improve volcanic ash forecasts in various eruption case studies.

Toward quantitative forecasts of volcanic ash dispersal: Using satellite retrievals for optimal estimation of source terms

AbstractAirborne volcanic ash is a hazard to aviation. There is an increasing demand for quantitative forecasts of ash properties such as ash mass load to allow airline operators to better manage the risks of flying through airspace likely to be contaminated by ash. In this paper we show how satellite‐derived mass load information at times prior to the issuance of the latest forecast can be used to estimate various model parameters that are not easily obtained by other means such as the distribution of mass of the ash column at the volcano. This in turn leads to better forecasts of ash mass load. We demonstrate the efficacy of this approach using several case studies.

The operational eEMEP model version 10.4 for volcanic SO<sub>2</sub> and ash forecasting

Abstract. This paper presents a new version of the EMEP MSC-W model called eEMEP developed for transportation and dispersion of volcanic emissions, both gases and ash. EMEP MSC-W is usually applied to study problems with air pollution and aerosol transport and requires some adaptation to treat volcanic eruption sources and effluent dispersion. The operational set-up of model simulations in case of a volcanic eruption is described. Important choices have to be made to achieve CPU efficiency so that emergency situations can be tackled in time, answering relevant questions of ash advisory authorities. An efficient model needs to balance the complexity of the model and resolution. We have investigated here a meteorological uncertainty component of the volcanic cloud forecast by using a consistent ensemble meteorological dataset (GLAMEPS forecast) at three resolutions for the case of SO2 emissions from the 2014 Barðarbunga eruption. The low resolution (40 × 40 km) ensemble members show larger agreement in plume position and intensity, suggesting that the ensemble here does not give much added value. To compare the dispersion at different resolutions, we compute the area where the column load of the volcanic tracer, here SO2, is above a certain threshold, varied for testing purposes between 0.25 and 50 Dobson units. The increased numerical diffusion causes a larger area (+34 %) to be covered by the volcanic tracer in the low resolution simulations than in the high resolution ones. The higher resolution (10 × 10 km) ensemble members show higher column loads farther away from the volcanic eruption site in narrower clouds. Cloud positions are more varied between the high resolution members, and the cloud forms resemble the observed clouds more than the low resolution ones. For a volcanic emergency case this means that to obtain quickly results of the transport of volcanic emissions, an individual simulation with our low resolution is sufficient; however, to forecast peak concentrations with more certainty for forecast or scientific analysis purposes, a finer resolution is needed. The model is further developed to simulate ash from highly explosive eruptions. A possibility of increasing the number of vertical layers, achieving finer vertical resolution, as well as a higher model top, is included in the eEMEP version. Ash size distributions may be altered for different volcanic eruptions and assumptions. Since ash particles are larger than typical particles in the standard model, gravitational settling across all vertical layers is included. We attempt finally a specific validation of the simulation of ash and its vertical distribution. Model simulations with and without gravitational settling for the 2010 Eyjafjallajökull eruption are compared to lidar observations over central Europe. The results show that with gravitation the centre of the ash mass can be 1 km lower over central Europe than without gravitation. However, the height variations in the ash layer caused by real weather situations are not captured perfectly well by either of the two simulations, playing down the role of gravitation parameterization imperfections. Both model simulations have on average an ash centre of mass below the observed values. Correlations between the observed and corresponding model centres of mass are higher for the model simulation with gravitational settling for four of the six stations studied here. The inclusion of gravitational settling is suggested to be required for a volcanic ash model.

Improving volcanic ash predictions with the HYSPLIT dispersion model by assimilating MODIS satellite retrievals

Abstract. Currently, the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) runs the HYSPLIT dispersion model with a unit mass release rate to predict the transport and dispersion of volcanic ash. The model predictions provide information for the Volcanic Ash Advisory Centers (VAAC) to issue advisories to meteorological watch offices, area control centers, flight information centers, and others. This research aims to provide quantitative forecasts of ash distributions generated by objectively and optimally estimating the volcanic ash source strengths, vertical distribution, and temporal variations using an observation-modeling inversion technique. In this top-down approach, a cost functional is defined to quantify the differences between the model predictions and the satellite measurements of column-integrated ash concentrations weighted by the model and observation uncertainties. Minimizing this cost functional by adjusting the sources provides the volcanic ash emission estimates. As an example, MODIS (Moderate Resolution Imaging Spectroradiometer) satellite retrievals of the 2008 Kasatochi volcanic ash clouds are used to test the HYSPLIT volcanic ash inverse system. Because the satellite retrievals include the ash cloud top height but not the bottom height, there are different model diagnostic choices for comparing the model results with the observed mass loadings. Three options are presented and tested. Although the emission estimates vary significantly with different options, the subsequent model predictions with the different release estimates all show decent skill when evaluated against the unassimilated satellite observations at later times. Among the three options, integrating over three model layers yields slightly better results than integrating from the surface up to the observed volcanic ash cloud top or using a single model layer. Inverse tests also show that including the ash-free region to constrain the model is not beneficial for the current case. In addition, extra constraints on the source terms can be given by explicitly enforcing no-ash for the atmosphere columns above or below the observed ash cloud top height. However, in this case such extra constraints are not helpful for the inverse modeling. It is also found that simultaneously assimilating observations at different times produces better hindcasts than only assimilating the most recent observations.