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21 results for “volcanic ash”
Hourly non-gridded volcanic ash properties retrieved from SEVIRI measurements for the Eyjafjallajökull 2010 eruption
<p>- Publishing date:<br> 14.05.2020</p> <p>- Title:<br> Hourly non-gridded volcanic ash properties retrieved from SEVIRI<br> measurements for the Eyjafjallajökull 2010 eruption </p> <p>- Authors of data set:<br> Arve Kylling (aky@nilu.no), NILU - Norwegian Institute for Air Research<br> Espen Sollum, NILU - Norwegian Institute for Air Research</p> <p>- Description:<br> Ash satellite detection and retrievals were made using infrared<br> measurements by SEVIRI on board the MSG-2 satellite. MSG-2 is<br> geostationary, centred at approximately 0N latitude, and has a 70<br> degree view coverage (Schmetz et al., 2002). Pixel resolution is 3 ×<br> 3 km at nadir, while at the edge of the coverage it increases to 10<br> × 10 km. Observations are available every 15 min. Pixels are<br> identified as containing ash if the brightness temperature<br> difference (BTD) between the SEVIRI 10.8 and 12.0 μm channels<br> (Prata, 1989) is below a certain threshold value, here −0.5 K. The<br> BTDs have been adjusted for water vapour absorption using the approach of<br> Yu et al. (2002). Ash clouds give negative BTDs, ice give positive<br> BTDs, and BTDs of water clouds are closer to zero. The ash mass<br> loading and effective ash particle radius are retrieved as described<br> in Kylling et al. (2015). The retrieval is based on a modification<br> of the Bayesian optimal estimation technique used by Francis et<br> al. (2012). We assume andesite ash with refractive index from Pollack<br> et al. (1973), spherical ash particles, and a lognormal size<br> distribution. The lognormal size distribution is described by the<br> geometric mean radius and the geometric standard deviation. The data<br> set includes retrievals for geometric standard deviation of 1.5,<br> 1.75, 2.0, and 2.25, which is a subset of the values used by Francis<br> et al. (2012). The data set has been used by Steensen et al. (2017).</p> <p> Data comes as hourly files broadly covering Iceland, Europe and the<br> surrounding oceans. The files are in bzip2 netcdf-format which<br> should be self-explanatory. </p> <p>- Version:<br> 1.0</p> <p>- Language:<br> English</p> <p>- Keywords<br> Volcanic ash, remote sensing, SEVIRI, Eyjafjallajökull 2010</p> <p>- Additional notes<br> None</p> <p>- Access right:<br> Open access</p> <p>- License:<br> CC BY-SA 4.0 </p> <p>- Funding:<br> Partly funded by the Norwegian ash project financed by the Norwegian<br> Ministry of Transport and Communications and Avinor. </p> <p>- References:<br> Francis, P. N., Cooke, M. C., and Saunders, R.W.: Retrieval of<br> physical properties of volcanic ash using Meteosat: A case study<br> from the 2010 Eyjafjallajokull eruption, J. Geophys. Res. Atmos.,<br> 117, D00U09, https://doi.org/10.1029/2011JD016788, 2012.</p> <p> Kylling, A., Kristiansen, N., Stohl, A., Buras-Schnell, R., Emde,<br> C., and Gasteiger, J.: A model sensitivity study of the impact of<br> clouds on satellite detection and retrieval of volcanic ash, Atmos. <br> Meas. Tech., 8, 1935-1949, https://doi.org/10.5194/amt-8-1935-<br> 2015, 2015.<br> <br> Pollack, J. B., Toon, O. B., and Khare, B. N.: Optical properties of<br> some terrestrial rocks and glasses, Icarus, 19, 372-389,<br> https://doi.org/10.1016/0019-1035(73)90115-2, 1973. </p> <p> Prata, A. J.: Observations of volcanic ash clouds in the 10-12 um<br> window using AVHRR/2 data, Int. J. Remote Sens., 10, 751-761,<br> 1989.</p> <p> Schmetz, J., Pili, P., Tjemkes, S., and Just, D.: An introduction to<br> Meteosat second generation (MSG), B. Am. Meteorol. Soc., 83,<br> 977-992, 2002.<br> <br> Steensen, B. M., Kylling, A., Kristiansen, N. I., and Schulz, M.:<br> Uncertainty assessment and applicability of an inversion method for<br> volcanic ash forecasting, Atmos. Chem. Phys., 17, 9205-9222,<br> https://doi.org/10.5194/acp-17-9205-2017, 2017. </p> <p> Yu, T., Rose, W. I., and Prata, A. J.: Atmospheric correction for<br> satellite-based volcanic ash mapping and retrievals using "split<br> window" IR data from GOES and AVHRR, J. Geophys. Res. Atmos., 107,<br> https://doi.org/10.1029/2001JD000706, 2002. </p>
Exploiting the Greenland volcanic ash repository to date caldera-forming eruptions and widespread isochrons during the Holocene
<p>Polar ice-cores have long been recognised as unrivalled repositories of past volcanic events. Although tephra products from local eruptions tend to dominate these records, improvements in micro-sampling and analytical techniques are uncovering a growing number of cryptotephras erupted from exceptionally distant volcanoes. We present a series of nine Middle Holocene cryptotephra deposits detected within the NGRIP ice-core that originate from five different volcanic regions across the Northern Hemisphere (Alaska, Cascades, Iceland, Japan, Kamchatka). Unique compositional signatures are employed to identify ash from three large caldera-forming events in Kamchatka (KS<sub>2 </sub>from Ksudach), the Cascades (Mazama) and North East Japan (Mashu), along with ash from the Hekla 4 eruption in Iceland. High-precision ice-core ages (adopting a 1950 datum for the GICC05 timescale assigned to the Greenland ice cores) are derived for each eruption: Hekla 4 (4325 ± 8 a b1.95k), KS<sub>2</sub> (7089 ± 26 a b1.95k), Mashu (i-f) (7473 ± 33 a b1.95k) and Mazama (7562 ± 35 a b1.95k), all of which can be employed as chronological fix-points in other proxy records where these deposits are also preserved. Four further cryptotephra deposits and one macro-deposit (in the GRIP ice core) are also identified and traced to sources in Iceland and Alaska. The cryptotephra originating from Alaska is correlated to a deposit identified in lake records from the Kenai Peninsula, thought to originate from Redoubt Volcano. The remaining four deposits are typical of the products of Katla, Grímsvötn and Veiðivötn in Iceland. This ensemble of mid-Holocene tephra deposits highlights the pivotal position of the Greenland ice-sheet and its ice-cores to capture deposition from the convergence of several far-travelled ash clouds. Precise age estimates derived from the annually resolved ice-core record greatly enhances the value of these tephra isochrons.</p> <p> </p>
Supplementary material for "Inverse Modeling of the Initial Stage of the 1991 Pinatubo Volcanic Cloud Accounting for Radiative Feedback of Volcanic ash" paper
<p>Supplementary material for "Inverse Modeling of the Initial Stage of the 1991 Pinatubo<br>Volcanic Cloud Accounting for Radiative Feedback of Volcanic ash" by A. Ukhov, <br>G. Stenchikov, S.Osipov, N. Krotkov, N. Gorkavyi, C. Li, O. Dubovik, and A. Lopatin.</p> <p>Corresponding author: Alexander Ukhov, alexander.ukhov@kaust.edu.sa</p> <p>Contents<br>0. This 'README' file</p> <p>1. Emission profiles for ash and SO2<br> 1.1 In pickle and txt format, when radiative feedback is accounted for:<br> 1.1.1 Files 'ash_2d_emission_profiles_rad_on' [Mt/sec] and 'ash_2d_emission_profiles.txt' [Mt/(m sec)]<br> 1.1.2 Files 'so2_2d_emission_profiles_rad_on' [Mt/sec] and 'so2_2d_emission_profiles.txt' [Mt/(m sec)]</p> <p> 1.2 In pickle format, when radiative feedback is not accounted for:<br> 1.2.1 Files 'ash_2d_emission_profiles_rad_off' [Mt/sec]<br> 1.2.2 Files 'so2_2d_emission_profiles_rad_off' [Mt/sec]</p> <p>2. python script 'draw_supplementary_profiles.py' plots inverted emission profiles <br> (in pickle format) and their time integrated variants.</p> <p>3. WRF-Chem output file 'wrfout_d01_1991-06-16_00:00:00' in netcdf format contains <br> 3-D fields of ash, sulfate, and SO2 concentrations at 0000 UTC on 16 of June. <br> Instructions on how to process WRF-Chem output are available at the Appendix of [1].</p> <p>4. WRF-Chem domain grid description in the file 'wrf_small_grid.txt'. This file can be<br> used for conservative interpolation of 3-D fields to another grid, for example <br> using 'cdo remapcon'.</p> <p>There are two options: <br>1. Use inverted ash and SO2 emission profiles (see p.1 and p.2)<br>2. Use ash, sulfate, and SO2 concentrations from WRF-Chem output file (see p.3 and p.4)<br> as initial conditions for another run.</p> <p><br>References:<br>1. Ukhov, A., Ahmadov, R., Grell, G., and Stenchikov, G.: Improving dust simulations<br> in WRF-Chem v4.1.3 coupled with the GOCART aerosol module, <br> Geosci. Model Dev., 14, 473–493, https://doi.org/10.5194/gmd-14-473-2021, 2021.</p> <p>2. Ukhov et. al, Enhancing Volcanic Eruption Simulations with the WRF-Chem v4.7.x</p>
Three-hourly gridded volcanic ash emissions for the Eyjafjallajökull 2010 eruption
<p>Forward simulations of the Eyjafjälla 2010 eruption with unit emissions. These files are used to create an emission estimate of a volcanic eruption.</p> <p>Each file corresponds to an individual emission time point, and contains 19 individual emission simulations. Each emission simulation emits 1 teragram of ash into a unique vertical level of the model. The levels are labeled L01 .. L19, and designate level number from the top of the atmosphere (top of level 1 is close to 130 hPa or around 14 km ASL). The hybrid sigma levels are defined in Vertical_levels_22_650m.txt. </p> <p>The files were created using eEMEP Unimod_ASH compiled by Alvaro Valdebenito (module cams50/201809) on the Nebula supercomputer. </p>
Fig. 3 in Effects of dietary intake of volcanic ash from Puyehue Cordon Caulle on Tenebrio molitor (Coleoptera: Tenebrionidae) larvae under laboratory conditions
Fig. 3. Mean body weight of larvae (mg) fed 30,000 and 50,000 ppm of volcanic ash treated flour disks afer 15 d. Bars with the same letter are not significantly different α = 0.05. Bioassay endpoint = 15 d, n = 10, substrate = treated and control insect food (ANOVA: F = 93.67; df = 2; P <0.0001).
Fig. 1 in Effects of dietary intake of volcanic ash from Puyehue Cordon Caulle on Tenebrio molitor (Coleoptera: Tenebrionidae) larvae under laboratory conditions
Fig. 1. Chemical composition of ash from Puyehue Cordon Caulle eruption collected in Collón Curá, Neuquén, Argentina (40.0400°S, 70.2405°W) 15 Jun 2011, determined by energy dispersive spectroscopy. Previously published in Buteler et al. (2011), Revista de la Sociedad Entomológica Argentina 70 (3–4), Figure 3, copyright RSEA, reproduced with permission.
Fig. 6 in Effects of dietary intake of volcanic ash from Puyehue Cordon Caulle on Tenebrio molitor (Coleoptera: Tenebrionidae) larvae under laboratory conditions
Fig. 6. Molting rate of Tenebrio molitor larvae feed on flour disks treated with sub-lethal concentrations (500, 1,000, 5,000 ppm) of volcanic ash. Molting rate = number of molts per incubation period of 27 d.
Fig. 5 in Effects of dietary intake of volcanic ash from Puyehue Cordon Caulle on Tenebrio molitor (Coleoptera: Tenebrionidae) larvae under laboratory conditions
Fig. 5. Larval body length (cm) of Tenebrio molitor larvae fed on flour disks treated with sub-lethal concentrations (500, 1,000, 5,000 ppm) of volcanic ash. Bars with the same letter are not significantly different α = 0.05. Bioassay endpoint = 27 d, n = 10, substrate = treated and control insect food (ANOVA: F = 95.15; df = 3; P <0.0001).
Fig. 4 in Effects of dietary intake of volcanic ash from Puyehue Cordon Caulle on Tenebrio molitor (Coleoptera: Tenebrionidae) larvae under laboratory conditions
Fig. 4. Mean body weight of larvae (mg) fed on sub lethal concentrations (500, 1,000, 5,000 ppm) of volcanic ash treated flour disks. Bars with the same letter are not significantly different at α = 0.05. Bioassay endpoint = 27 d, n = 10, substrate = treated and control insect food (ANOVA: F = 133.97; df = 3; P <0.0001).
Data and Code for : Transport and environmental impact of ash induced by the Hunga Tonga- Hunga Ha'apai volcanic eruption
<p>The dataset describes the atmospheric information and oceanic responses to the eruption of Hunga Tonga-Hunga Ha'apai (HTHH) Volcano. Satellite observations captured the direct impact of volcanic ash on modifying the landscape, including the transport and profile of aerosol content captured respectively by Suomi and CALIPSO, chlorophyll from the reanalyzed satellite products. </p>
Timing and provenance of volcanic fluxes around the Permian-Triassic Boundary Mass Extinction in South China: U-Pb zircon geochronology, volcanic ash geochemistry and mercury isotopes
<p>The enclosed dataset contains all of the raw data supporting the results presented in the paper titled: <strong>"Timing and provenance of volcanic fluxes around the Permian-Triassic Boundary Mass Extinction in South China: U-Pb zircon geochronology, volcanic ash geochemistry and mercury isotopes". </strong></p> <p>The Excel data file contains four data sheets as follows: </p> <p>1. Table S1: This sheet contains the U-Pb output table from ETRedux. The data sheet contains all the U and Pb isotopic data generated for the current study.</p> <p>2. Table S2: This excel sheet contains the geochemical compositions of analyzed volcanic ash beds as well as LOI-normalized major element compositions of these ashes.</p> <p>3. Table S3: This excel sheet contains the other geochemical and isotope data for all analyzed samples. This includes Hg concentration and isotope compositions, TOC data, as well as major and trace element concentrations and ratios.</p> <p>4. a final table containing the analyzed Hg isotope compositions for the utilized standard reference materials - ETH Fluka, UM-Almaden, NIST 1632D and MESS-3.</p>
Rock magnetic fingerprint of Mt. Etna volcanic ash: the dataset
<p>This dataset refers to the article: "Rock magnetic fingerprint of Mt. Etna volcanic ash" by the same authors, published in Geophysical Journal International, https://doi.org/10.1093/gji/ggac213.</p> <p>A detailed rock magnetic study was conducted on ash samples collected from different products erupted during explosive activity of Mount Etna, Italy, in order to test the use of magnetic properties as discriminating factors among them, and their explosive character in particular.<br> Samples include tephra emplaced during the last 18 ka: the benmoreitic Plinian eruptions of the Pleistocene Ellittico activity from marine core ET97-70 (Ionian Sea) and the basaltic Holocene FG eruption (122 BC), the Strombolian/Phreatomagmatic/sub-Plinian eruptions (namely, the Holocene TV, FS, FL, ETP products, and the 1990, 1998 eruptions) collected from the slope of the volcano, and the Recent explosive activity (lava fountains referred to as “Ash Rich Jets and Plumes”, or ARJP) that occurred in the 2001-2002 period, related to flank eruptions.<br> A full set of rock magnetic experiments were carried out to determine the magnetic mineralogy and the magnetic grain size at the Institute for Rock Magnetism at the University of Minnesota, including First-Order Reversal Curves (FORCs), hysteresis loops and backfield DC demagnetization remanence curves (DCD or Backfield curves) at room temperature on Princeton Measurements Corporation (Princeton, NJ) Vibrating Sample Magnetometers (VSMs).<br> Low temperature (LT) experiments were conducted on Quantum Design (San Diego, CA) Magnetic Properties Measurement Systems (MPMS-XL and 5S). LT experiments were carried out by measuring the magnetic remanence on warming from 10 K to room temperature (300 K) after cooling in a 2.5 T field (field cooled remanence, FC), as well as after cooling in zero field and applying a saturation isothermal remanent magnetization (SIRM) of 2.5 T at 10 K (zero-field cooled remanence, ZFC). A room temperature (RT) 2.5 T SIRM was also applied at 300 K and the remanence was measured upon temperature cycling to 10 K and back (RTSIRM). AC susceptibility as a function of temperature and frequency (1, 10, 100 Hz or 1, 5, 32, 178, 1000 Hz) was also measured for selected specimens from the three groups of samples.<br> Room temperature susceptibility measurements as a function of field amplitude (10, 20, 40, 80, 120, 200, 400 A/m) were carried out on the Late Pleistocene samples using a Magnon susceptibility system. Saturation magnetization on warming between room temperature and 700°C (Ms-T) was measured on selected specimens using a horizontal Curie balance with Argon gas circulation to limit oxidation processes during heating. Likewise, magnetic susceptibility on warming between room temperature and 700°C (<em>X</em>-T) was measured on a Kappabridge KLY-2 (Brno, Czech Republic) using fields of 300 A/m and 920 Hz. Ms-T and <em>X</em>-T curves are collectively referred to as thermomagnetic curves.</p>
Data for "Including ash in UKESM1 model simulations of the Raikoke volcanic eruption reveal improved agreement with observations" by Wells et al., 2023
<p>Data used for figures in "Including ash in UKESM1 model simulations of the Raikoke volcanic eruption reveal improved agreement with observations" by Wells et al., 2023</p> <p>See https://github.com/awells96/Raikoke for code to reproduce the figures.</p>
IDW interpolation dataset of ash and tephra deposition following the 2021 Tajogaite volcanic eruption on La Palma, Canary Islands, Spain
<p>The compiled dataset is the result of a field collection campaign to measure the depth of the ash and tephra layer in the aftermath of the 2021 volcanic eruption (Tajogaite) on the island of La Palma, Canary Islands, Spain.</p> <p>The dataset consists of two files: a shapefile and a GeoTIFF raster. The shapefile is a point layer file that displays the location of all ash depth measurements (415 points) taken in the field. To improve our sampling near the crater, where safety and time constraints prevented field collection, we manually sampled additional drone-based measurements (66 points). We combined these data into a single dataset ("ash_depth"; 481 total points) that was then used as input for a spatial interpolation using Inverse Distance Weighting (IDW).</p> <p>The IDW interpolation was performed using the Spatial Analyst toolbox in ArcMap 10.8.1 (Esri, 2021). As an exact deterministic interpolation, IDW estimates pixels values of unknown points by using average distance and a weight between sample points (Watson & Philip, 1985). This is ideal for a dataset that includes many field measurements since IDW interpolates between the minimum and maximum of the collected data. The model parameters were adjusted manually but the best results were yielded using the default settings, with the exception of the output cell size. The output cell size was calibrated to 2 m. The IDW parameters can be viewed in Table 1.</p> <p>The sample point locations were resampled from the raster file to estimate the Root Square Mean Error (RMSE) and were saved to the shapefile as "ash_idw". The RMSE of the dataset is 0.34 m. For further inquiries please contact Christopher Shatto (email: christopher.shatto@uni-bayreuth.de).</p> <p> </p> <p>Please cite the data paper link to this repository as: </p> <p><strong>C. Shatto, F. Weiser and A. Walentowitz et al., Volcanic tephra deposition dataset based on interpolated field measurements following the 2021 Tajogaite Eruption on La Palma, Canary Islands, Spain, Data in Brief, https://doi.org/10.1016/j.dib.2023.109949</strong></p> <p> </p> <table> <tbody> <tr> <td>Power</td> <td>2</td> </tr> <tr> <td>Output cell size</td> <td>2</td> </tr> <tr> <td>Search neighborhood type</td> <td>Standard (circular)</td> </tr> <tr> <td>Major/minor semiaxis</td> <td>12161.79/12161.79</td> </tr> <tr> <td>Max/minimum neighbors</td> <td>15/10</td> </tr> <tr> <td>Sector type</td> <td>1</td> </tr> <tr> <td>Angle</td> <td>0</td> </tr> <tr> <td>Weight field</td> <td>None</td> </tr> </tbody> </table> <p> </p>
Determination of complex refractive indices and optical properties of volcanic ashes in the thermal infrared based on generic petrological compositions: supplementary material
<p>This dataset contains supplementary material to the paper "Determination of complex refractive indices and optical properties of volcanic ashes in the thermal infrared based on generic petrological compositions", doi: 10.1016/j.jvolgeores.2021.107174</p> <p>It contains a set of volcanic ash refractive indices and optical properties derived for certain microphysical properties.</p> <p>For more information please consider the manuscript or contact the authors.</p>
Dataset for "Physicochemical properties and bioreactivity of sub-10 µm geogenic particles: comparison of volcanic ash and desert dust" published in GeoHealth
<p>Data Repository for:</p> <h2><strong>Physicochemical properties and bioreactivity of sub-10 µm geogenic particles: comparison of volcanic ash and desert dust</strong></h2> <p>Ines Tomašek<sup>1,2,3*</sup>, Julia Eychenne<sup>1,2</sup>, David E. Damby<sup>4</sup>, Adrian Hornby<sup>5,6</sup>, Manolis N. Romanias<sup>7</sup>, Severine Moune<sup>1</sup>, Gaëlle Uzu<sup>8</sup>, Federica Schiavi<sup>1</sup>, Maeva Dole<sup>1</sup>, Emmanuel Gardès<sup>1</sup>, Mickael Laumonier<sup>1</sup>, Clara Gorce<sup>1</sup>, Régine Minet-Quinard<sup>2,9</sup>, Julie Durif<sup>9</sup>, Corinne Belville<sup>2</sup>, Ousmane Traoré<sup>10,11</sup>, Loïc Blanchon<sup>2</sup><sup>†</sup>, Vincent Sapin<sup>2,9</sup><sup>†</sup></p> <p><sup>1</sup>Laboratoire Magmas et Volcans (LMV), CNRS, IRD, OPGC, Université Clermont Auvergne, France.</p> <p><sup>2</sup>Institute of Genetic Reproduction and Development (iGReD), Translational Approach to Epithelial Injury and Repair Team, CNRS, INSERM, Université Clermont Auvergne, France.</p> <p><sup>3</sup>Istituto Nazionale di Geofisica e Vulcanologia (INGV), Osservatorio Etneo, Catania, Italy.</p> <p><sup>4</sup>Volcano Science Center, U.S. Geological Survey (USGS), USA.</p> <p><sup>5</sup>Department of Earth and Atmospheric Sciences, Cornell University, USA.</p> <p><sup>6</sup>Department of Cellular and Molecular Biology, School of Medicine, University of Texas at Tyler, USA.</p> <p><sup>7</sup>Institut Mines-Télécom (IMT) Nord Europe, Centre for Energy and Environment, Université Lille, France.</p> <p><sup>8</sup>Université Grenoble Alpes, IRD, CNRS, INRAE, INP-G, IGE (UMR 5001), France.</p> <p><sup>9</sup>Centre Hospitalier Universitaire (CHU) Clermont-Ferrand, Biochemistry and Molecular Genetics Department, France.</p> <p><sup>10</sup>Centre Hospitalier Universitaire (CHU) Clermont-Ferrand, Infection Control Department, France.</p> <p><sup>11</sup>Laboratoire Microorganismes: Génome Environnement (LMGE), UMR, CNRS, Université Clermont Auvergne, France.</p> <p><sup>†</sup>These authors share the last authorship.</p> <p><sup>*</sup>Correspondence.</p>
Dataset for "Spatial distribution and physicochemical properties of respirable volcanic ash from the 16-17 August 2006 Tungurahua eruption (Ecuador), and alveolar epithelium response in-vitro" published in GeoHealth
<p>Data Repository for:</p> <p><strong>"Spatial distribution and physicochemical properties of respirable volcanic ash from the 16-17 August 2006 Tungurahua eruption (Ecuador), and alveolar epithelium response <em>in-vitro" </em></strong>published in GeoHealth.<br> </p> <p>Julia Eychenne<sup>1,2*</sup>, Lucia Gurioli<sup>1</sup>, David Damby<sup>3</sup>, Corinne Belville², Federica Schiavi<sup>1</sup>, Geoffroy Marceau<sup>2,4</sup>, Claire Szczepaniak<sup>5</sup>, Christelle Blavignac<sup>5</sup>, Mickael Laumonier<sup>1</sup>, Emmanuel Gardés<sup>1</sup>, Jean-Luc Le Pennec<sup>6,7</sup>, Jean-Marie Nedelec<sup>8</sup>, Loïc Blanchon², Vincent Sapin<sup>2,4 </sup></p> <p><sup>1</sup> Université Clermont Auvergne, CNRS, IRD, OPGC, Laboratoire Magmas et Volcans, F-63000 Clermont-Ferrand, France</p> <p><sup>2</sup> Université Clermont Auvergne, CNRS, INSERM, Institut de Génétique Reproduction et Développement, F-63000 Clermont-Ferrand, France</p> <p><sup>3</sup> U.S. Geological Survey, California Volcano Observatory, Moffett Field, CA, USA</p> <p><sup>4</sup> Biochemistry and Molecular Genetic Department, University Hospital, F-63000 Clermont-Ferrand, France</p> <p><sup>5</sup> Université Clermont Auvergne, UCA PARTNER, Centre Imagerie Cellulaire Santé, F-63000 Clermont-Ferrand, France</p> <p><sup>6</sup> Geo-Ocean, CNRS, Ifremer, UMR6538, F-29280 Plouzané, France</p> <p><sup>7</sup> IRD Office for Indonesia & Timor Leste, Jalan Kemang Raya n°4, Jakarta 12730, Indonesia</p> <p><sup>8</sup> Université Clermont Auvergne, Clermont Auvergne INP, CNRS, ICCFn, F-63000 Clermont-Ferrand, France</p> <p><strong>This repository includes the grainsize distributions of the individual tephra fall samples, the grainsize distribution of the respirable ash sample isolated from F2, the Raman point counting data and individual spectra, the SEM images and EDX maps of the respirable ash sample, the SEM and TEM images of the <em>in-vitro</em> experiments, and the data from the LDH assays, multiplex immunoassays and RT-qPCR.</strong></p>
Data for "Using Simulated Radiances to Understand the Limitations of Satellite Retrieved Volcanic Ash Data and the Implications for Volcanic Ash Cloud Forecasting"
<p>This location contains all of the data used in the analysis for the paper "Using Simulated Radiances to Understand the Limitations of Satellite Retrieved Volcanic Ash Data and the Implications for Volcanic Ash Cloud Forecasting" which is currently in prep.</p> <p>All of the retrieved satellite data can be seen in the retrieved_satellite_data.zip folder. the data is organised by the input ash cloud properties being simulated and the hdf files contain all of the retrieved variables where ash has been successfully detected.</p> <p>All of the output dispersion model data is available in NAME_output_data.zip. </p> <p>All of the input source data used in the dispersion model simulations (including the data from REFIR) is available in NAME_source_data_REFIR.zip.</p>
Volcanic ash source inversion data for paper "A near-real-time method for estimating volcanic ash emissions using satellite retrievals"
<p>This dataset consists of volcanic ash source inversion data for the paper "A near-real-time method for estimating volcanic ash emissions using satellite retrievals" by Rachel E. Pelley, David J. Thomson, Helen N. Webster, Michael C. Cooke, Alistair J. Manning, Claire S. Witham and Matthew C. Hort, Atmosphere, 2021, 12, 1573, https://doi.org/10.3390/atmos12121573. Satellite retrievals, dispersion model simulations and inversion calculations are included for the eruptions of Eyjafjallajokull in 2010 and Grimsvotn in 2011.</p>
Probabilistic Volcanic Ash Uncertainty
<h1>Probabilistic Volcanic Ash Uncertainty</h1> <p>Incorporating source parameter (MER and plume height) alongside meteorological variability in volcanic ash hazard dispersion forecasting. Contains code and supporting data submitted alongside manuscript "Incorporating Source Parameter and Meteorological Variability in the Generation of Probabilistic Volcanic Ash Hazard Forecasts" to Journal of Geophysical Research: Atmospheres. Requires The Met Office's <a href="https://www.metoffice.gov.uk/research/approach/modelling-systems/dispersion-model">Numerical Atmospheric-dispersion Modelling Environment</a> (NAME), which is available by licence from the UK Met Office.</p> <h2>Introduction</h2> <p>Airborne volcanic ash is hazardous for aircraft. To manage this risk, Volcanic Ash Advisory Centres (VAACs) provide forecasts of ash clouds following a volcanic eruption. These forecasts are created using dispersion models that predict the transport of ash based on eruption details and weather data. These sets of inputs have large uncertainties that can affect the accuracy of the forecasts. The paper this project is associated with presents a method for producing probabilistic forecasts that account for these uncertainties. Typically, weather uncertainties are handled by using multiple weather predictions, referred to as an ensemble. Dispersion outcomes depend on the eruption plume height and mass eruption rate (MER), which are related but have large associated uncertainties. Our method uses a statistical approach to incorporate these uncertainties into forecasts to allow for the calculation of probabilities of different ash concentration levels for aviation. It uses the same number of model runs as there are ensemble members, and does not require eruption details (plume height, MER, and emission profile) to be specified in advance, making it a computationally efficient approach as the bulk of computations can be done after a small number of initial model runs.</p> <h2>Contents</h2> <p>The zip file consists of four folders: analysis, notebooks, pvauncertainty, and scripts.</p> <h3>analysis</h3> <p>Contains two sub-folders:</p> <ul> <li>fig-scripts: scripts for post-processing of data and generation of figures for the submitted manuscript.</li> <li>data: post-processed output data of NAME simulations.</li> </ul> <h3>notebooks</h3> <p>The Jupyter notebooks get-started-pt1 and get-started-pt2 illustrate how the package can be used with NAME. Users must provide their own NAME input files to simulate volcanic ash dispersion; minimal non-working examples of code block segments that must be changed are given in scripts.</p> <h3>pvauncertainty</h3> <p>The Python package contains classes to set up volcanic ash simulations in NAME and evaluate resultant probabilistic quantities:</p> <ul> <li>Set up NAME inputs for a volcanic ash emission given a plume height observation, or range for the height: <ul> <li>Using deterministic or ensemble meteorology</li> <li>Provides a unit MER for later rescaling</li> <li>Given a plume height range and interval step size, initialises NAME with multiple interval emissions to be saved separately</li> <li>Sets NAME running on a SLURM environment</li> </ul> </li> <li>Evaluate probabilistic quantities of volcanic ash concentrations: <ul> <li>Conditional exceedance probabilities given ensemble member</li> <li>Conditional exceedance probabilities given plume height observation</li> <li>Percentiles of ash concentration given plume height observation</li> <li>Overall exceedance probabilities given plume height distribution (Gaussian distribution by default) by numerical integration (quadrature)</li> <li>Plotting of probabilistic quantities of volcanic ash concentrations</li> </ul> </li> </ul> <p>To install the package, navigate to its parent folder and execute "pip install -e .".</p> <h3>scripts</h3> <p>Contains scripts for setting up ensemble or deterministic NAME runs, given a csv file of plume height and MER values, and minimal example NAME input files.</p> <h2>License</h2> <p>This project is licensed under the BSD 3-Clause License.</p>
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