Articles | Volume 18, issue 1
https://doi.org/10.5194/nhess-18-41-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/nhess-18-41-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
04 Jan 2018
Research article |
| 04 Jan 2018
Multi-level emulation of a volcanic ash transport and dispersion model to quantify sensitivity to uncertain parameters
Natalie J. Harvey
CORRESPONDING AUTHOR
Department of Meteorology, University of Reading, Reading, RG6 6BB, UK
Nathan Huntley
Department of Mathematical Sciences, Durham University, Durham, DH1 3LE, UK
Helen F. Dacre
Department of Meteorology, University of Reading, Reading, RG6 6BB, UK
Michael Goldstein
Department of Mathematical Sciences, Durham University, Durham, DH1 3LE, UK
David Thomson
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Helen Webster
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
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Cited
35 citations as recorded by crossref.
- The Impact of Ensemble Meteorology on Inverse Modeling Estimates of Volcano Emissions and Ash Dispersion Forecasts: Grímsvötn 2011 N. Harvey et al. https://doi.org/10.3390/atmos11101022
- The 2019 Raikoke volcanic eruption – Part 1: Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide J. de Leeuw et al. https://doi.org/10.5194/acp-21-10851-2021
- The Impact of Eruption Source Parameter Uncertainties on Ash Dispersion Forecasts During Explosive Volcanic Eruptions F. Dioguardi et al. https://doi.org/10.1029/2020JD032717
- Multilevel emulation for stochastic computer models with application to large offshore wind farms J. Kennedy et al. https://doi.org/10.1093/jrsssc/qlad023
- A New Radar‐Based Statistical Model to Quantify Mass Eruption Rate of Volcanic Plumes L. Mereu et al. https://doi.org/10.1029/2022GL100596
- Atmospheric dispersion modelling at the London VAAC: development of quantitative and probabilistic volcanic ash forecasts F. Beckett et al. https://doi.org/10.1186/s13617-026-00166-7
- Particle aging and aerosol–radiation interaction affect volcanic plume dispersion: evidence from the Raikoke 2019 eruption L. Muser et al. https://doi.org/10.5194/acp-20-15015-2020
- Quantifying the impact of meteorological uncertainty on emission estimates and the risk to aviation using source inversion for the Raikoke 2019 eruption N. Harvey et al. https://doi.org/10.5194/acp-22-8529-2022
- Retrieval of Refractive Indices of Ten Volcanic Ash Samples in the Infrared, Visible and Ultraviolet Spectral Region A. Deguine et al. https://doi.org/10.2139/ssrn.4178248
- Epistemic uncertainties and natural hazard risk assessment – Part 1: A review of different natural hazard areas K. Beven et al. https://doi.org/10.5194/nhess-18-2741-2018
- Online treatment of eruption dynamics improves the volcanic ash and SO2 dispersion forecast: case of the 2019 Raikoke eruption J. Bruckert et al. https://doi.org/10.5194/acp-22-3535-2022
- Refining an ensemble of volcanic ash forecasts using satellite retrievals: Raikoke 2019 A. Capponi et al. https://doi.org/10.5194/acp-22-6115-2022
- Aerosol dynamic processes in the Hunga plume in January 2022: does water vapor accelerate aerosol aging? J. Bruckert et al. https://doi.org/10.5194/acp-25-9859-2025
- Characterising, quantifying, and accessing eruption source parameters of explosive volcanic eruptions for operational simulation of tephra dispersion: a current view and future perspectives S. Engwell et al. https://doi.org/10.1007/s00445-024-01706-y
- Retrieval of refractive indices of ten volcanic ash samples in the infrared, visible and ultraviolet spectral region A. Deguine et al. https://doi.org/10.1016/j.jaerosci.2022.106100
- A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME E. Fillola et al. https://doi.org/10.5194/gmd-16-1997-2023
- Impact of climate change on volcanic processes: current understanding and future challenges T. Aubry et al. https://doi.org/10.1007/s00445-022-01562-8
- The importance of grain size and shape in controlling the dispersion of the Vedde cryptotephra J. Saxby et al. https://doi.org/10.1002/jqs.3152
- Atmospheric Dispersion Modelling at the London VAAC: A Review of Developments since the 2010 Eyjafjallajökull Volcano Ash Cloud F. Beckett et al. https://doi.org/10.3390/atmos11040352
- Bayesian atmospheric tomography for detection and quantification of methane emissions: application to data from the 2015 Ginninderra release experiment L. Cartwright et al. https://doi.org/10.5194/amt-12-4659-2019
- A case study analysis of the impact of a new free tropospheric turbulence scheme on the dispersion of an atmospheric tracer A. Mirza et al. https://doi.org/10.1002/qj.4681
- Global sensitivity analysis of models for volcanic ash forecasting E. Scott et al. https://doi.org/10.1016/j.jvolgeores.2025.108393
- Exploring How Eruption Source Parameters Affect Volcanic Radiative Forcing Using Statistical Emulation L. Marshall et al. https://doi.org/10.1029/2018JD028675
- Using synthetic case studies to explore the spread and calibration of ensemble atmospheric dispersion forecasts A. Jones et al. https://doi.org/10.5194/acp-23-12477-2023
- Long-term hazard assessment of explosive eruptions at Jan Mayen (Norway) and implications for air traffic in the North Atlantic M. Titos et al. https://doi.org/10.5194/nhess-22-139-2022
- Novel statistical emulator construction for volcanic ash transport model Ash3d with physically motivated measures Q. Yang et al. https://doi.org/10.1098/rspa.2020.0161
- How dependent are quantitative volcanic ash concentration and along‐flight dosage forecasts to model structural choices? L. James et al. https://doi.org/10.1002/met.70003
- Emulation of greenhouse‐gas sensitivities using variational autoencoders L. Cartwright et al. https://doi.org/10.1002/env.2754
- The ash concentration of co-PDC clouds: implications for operational modelling and the aviation hazard M. Hagenbourger et al. https://doi.org/10.1038/s44304-026-00214-7
- Airspace Contamination by Volcanic Ash from Sequences of Etna Paroxysms: Coupling the WRF-Chem Dispersion Model with Near-Source L-Band Radar Observations U. Rizza et al. https://doi.org/10.3390/rs15153760
- Technical note: Exploring parameter and meteorological uncertainty via emulation in volcanic ash atmospheric dispersion modelling J. Salter et al. https://doi.org/10.5194/acp-24-6251-2024
- Calculating and communicating ensemble‐based volcanic ash dosage and concentration risk for aviation A. Prata et al. https://doi.org/10.1002/met.1759
- The Development of Volcanic Ash Cloud Layers over Hours to Days Due to Atmospheric Turbulence Layering M. Bursik et al. https://doi.org/10.3390/atmos12020285
- Assessing the value meteorological ensembles add to dispersion modelling using hypothetical releases S. Leadbetter et al. https://doi.org/10.5194/acp-22-577-2022
- Advancing Scientific Understanding of the Global Methane Budget in Support of the Paris Agreement A. Ganesan et al. https://doi.org/10.1029/2018GB006065
35 citations as recorded by crossref.
- The Impact of Ensemble Meteorology on Inverse Modeling Estimates of Volcano Emissions and Ash Dispersion Forecasts: Grímsvötn 2011 N. Harvey et al. https://doi.org/10.3390/atmos11101022
- The 2019 Raikoke volcanic eruption – Part 1: Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide J. de Leeuw et al. https://doi.org/10.5194/acp-21-10851-2021
- The Impact of Eruption Source Parameter Uncertainties on Ash Dispersion Forecasts During Explosive Volcanic Eruptions F. Dioguardi et al. https://doi.org/10.1029/2020JD032717
- Multilevel emulation for stochastic computer models with application to large offshore wind farms J. Kennedy et al. https://doi.org/10.1093/jrsssc/qlad023
- A New Radar‐Based Statistical Model to Quantify Mass Eruption Rate of Volcanic Plumes L. Mereu et al. https://doi.org/10.1029/2022GL100596
- Atmospheric dispersion modelling at the London VAAC: development of quantitative and probabilistic volcanic ash forecasts F. Beckett et al. https://doi.org/10.1186/s13617-026-00166-7
- Particle aging and aerosol–radiation interaction affect volcanic plume dispersion: evidence from the Raikoke 2019 eruption L. Muser et al. https://doi.org/10.5194/acp-20-15015-2020
- Quantifying the impact of meteorological uncertainty on emission estimates and the risk to aviation using source inversion for the Raikoke 2019 eruption N. Harvey et al. https://doi.org/10.5194/acp-22-8529-2022
- Retrieval of Refractive Indices of Ten Volcanic Ash Samples in the Infrared, Visible and Ultraviolet Spectral Region A. Deguine et al. https://doi.org/10.2139/ssrn.4178248
- Epistemic uncertainties and natural hazard risk assessment – Part 1: A review of different natural hazard areas K. Beven et al. https://doi.org/10.5194/nhess-18-2741-2018
- Online treatment of eruption dynamics improves the volcanic ash and SO2 dispersion forecast: case of the 2019 Raikoke eruption J. Bruckert et al. https://doi.org/10.5194/acp-22-3535-2022
- Refining an ensemble of volcanic ash forecasts using satellite retrievals: Raikoke 2019 A. Capponi et al. https://doi.org/10.5194/acp-22-6115-2022
- Aerosol dynamic processes in the Hunga plume in January 2022: does water vapor accelerate aerosol aging? J. Bruckert et al. https://doi.org/10.5194/acp-25-9859-2025
- Characterising, quantifying, and accessing eruption source parameters of explosive volcanic eruptions for operational simulation of tephra dispersion: a current view and future perspectives S. Engwell et al. https://doi.org/10.1007/s00445-024-01706-y
- Retrieval of refractive indices of ten volcanic ash samples in the infrared, visible and ultraviolet spectral region A. Deguine et al. https://doi.org/10.1016/j.jaerosci.2022.106100
- A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME E. Fillola et al. https://doi.org/10.5194/gmd-16-1997-2023
- Impact of climate change on volcanic processes: current understanding and future challenges T. Aubry et al. https://doi.org/10.1007/s00445-022-01562-8
- The importance of grain size and shape in controlling the dispersion of the Vedde cryptotephra J. Saxby et al. https://doi.org/10.1002/jqs.3152
- Atmospheric Dispersion Modelling at the London VAAC: A Review of Developments since the 2010 Eyjafjallajökull Volcano Ash Cloud F. Beckett et al. https://doi.org/10.3390/atmos11040352
- Bayesian atmospheric tomography for detection and quantification of methane emissions: application to data from the 2015 Ginninderra release experiment L. Cartwright et al. https://doi.org/10.5194/amt-12-4659-2019
- A case study analysis of the impact of a new free tropospheric turbulence scheme on the dispersion of an atmospheric tracer A. Mirza et al. https://doi.org/10.1002/qj.4681
- Global sensitivity analysis of models for volcanic ash forecasting E. Scott et al. https://doi.org/10.1016/j.jvolgeores.2025.108393
- Exploring How Eruption Source Parameters Affect Volcanic Radiative Forcing Using Statistical Emulation L. Marshall et al. https://doi.org/10.1029/2018JD028675
- Using synthetic case studies to explore the spread and calibration of ensemble atmospheric dispersion forecasts A. Jones et al. https://doi.org/10.5194/acp-23-12477-2023
- Long-term hazard assessment of explosive eruptions at Jan Mayen (Norway) and implications for air traffic in the North Atlantic M. Titos et al. https://doi.org/10.5194/nhess-22-139-2022
- Novel statistical emulator construction for volcanic ash transport model Ash3d with physically motivated measures Q. Yang et al. https://doi.org/10.1098/rspa.2020.0161
- How dependent are quantitative volcanic ash concentration and along‐flight dosage forecasts to model structural choices? L. James et al. https://doi.org/10.1002/met.70003
- Emulation of greenhouse‐gas sensitivities using variational autoencoders L. Cartwright et al. https://doi.org/10.1002/env.2754
- The ash concentration of co-PDC clouds: implications for operational modelling and the aviation hazard M. Hagenbourger et al. https://doi.org/10.1038/s44304-026-00214-7
- Airspace Contamination by Volcanic Ash from Sequences of Etna Paroxysms: Coupling the WRF-Chem Dispersion Model with Near-Source L-Band Radar Observations U. Rizza et al. https://doi.org/10.3390/rs15153760
- Technical note: Exploring parameter and meteorological uncertainty via emulation in volcanic ash atmospheric dispersion modelling J. Salter et al. https://doi.org/10.5194/acp-24-6251-2024
- Calculating and communicating ensemble‐based volcanic ash dosage and concentration risk for aviation A. Prata et al. https://doi.org/10.1002/met.1759
- The Development of Volcanic Ash Cloud Layers over Hours to Days Due to Atmospheric Turbulence Layering M. Bursik et al. https://doi.org/10.3390/atmos12020285
- Assessing the value meteorological ensembles add to dispersion modelling using hypothetical releases S. Leadbetter et al. https://doi.org/10.5194/acp-22-577-2022
- Advancing Scientific Understanding of the Global Methane Budget in Support of the Paris Agreement A. Ganesan et al. https://doi.org/10.1029/2018GB006065
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