34 citations as recorded by crossref.

1. The Impact of Ensemble Meteorology on Inverse Modeling Estimates of Volcano Emissions and Ash Dispersion Forecasts: Grímsvötn 2011 N. Harvey et al. 10.3390/atmos11101022
2. The 2019 Raikoke volcanic eruption – Part 1: Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide J. de Leeuw et al. 10.5194/acp-21-10851-2021
3. The Impact of Eruption Source Parameter Uncertainties on Ash Dispersion Forecasts During Explosive Volcanic Eruptions F. Dioguardi et al. 10.1029/2020JD032717
4. Multilevel emulation for stochastic computer models with application to large offshore wind farms J. Kennedy et al. 10.1093/jrsssc/qlad023
5. A New Radar‐Based Statistical Model to Quantify Mass Eruption Rate of Volcanic Plumes L. Mereu et al. 10.1029/2022GL100596
6. Particle aging and aerosol–radiation interaction affect volcanic plume dispersion: evidence from the Raikoke 2019 eruption L. Muser et al. 10.5194/acp-20-15015-2020
7. 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. 10.5194/acp-22-8529-2022
8. Retrieval of Refractive Indices of Ten Volcanic Ash Samples in the Infrared, Visible and Ultraviolet Spectral Region A. Deguine et al. 10.2139/ssrn.4178248
9. Epistemic uncertainties and natural hazard risk assessment – Part 1: A review of different natural hazard areas K. Beven et al. 10.5194/nhess-18-2741-2018
10. Online treatment of eruption dynamics improves the volcanic ash and SO2 dispersion forecast: case of the 2019 Raikoke eruption J. Bruckert et al. 10.5194/acp-22-3535-2022
11. Refining an ensemble of volcanic ash forecasts using satellite retrievals: Raikoke 2019 A. Capponi et al. 10.5194/acp-22-6115-2022
12. Aerosol dynamic processes in the Hunga plume in January 2022: does water vapor accelerate aerosol aging? J. Bruckert et al. 10.5194/acp-25-9859-2025
13. 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. 10.1007/s00445-024-01706-y
14. Retrieval of refractive indices of ten volcanic ash samples in the infrared, visible and ultraviolet spectral region A. Deguine et al. 10.1016/j.jaerosci.2022.106100
15. A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME E. Fillola et al. 10.5194/gmd-16-1997-2023
16. Impact of climate change on volcanic processes: current understanding and future challenges T. Aubry et al. 10.1007/s00445-022-01562-8
17. The importance of grain size and shape in controlling the dispersion of the Vedde cryptotephra J. Saxby et al. 10.1002/jqs.3152
18. Atmospheric Dispersion Modelling at the London VAAC: A Review of Developments since the 2010 Eyjafjallajökull Volcano Ash Cloud F. Beckett et al. 10.3390/atmos11040352
19. Bayesian atmospheric tomography for detection and quantification of methane emissions: application to data from the 2015 Ginninderra release experiment L. Cartwright et al. 10.5194/amt-12-4659-2019
20. 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. 10.1002/qj.4681
21. Global sensitivity analysis of models for volcanic ash forecasting E. Scott et al. 10.1016/j.jvolgeores.2025.108393
22. Exploring How Eruption Source Parameters Affect Volcanic Radiative Forcing Using Statistical Emulation L. Marshall et al. 10.1029/2018JD028675
23. Using synthetic case studies to explore the spread and calibration of ensemble atmospheric dispersion forecasts A. Jones et al. 10.5194/acp-23-12477-2023
24. Long-term hazard assessment of explosive eruptions at Jan Mayen (Norway) and implications for air traffic in the North Atlantic M. Titos et al. 10.5194/nhess-22-139-2022
25. Novel statistical emulator construction for volcanic ash transport model Ash3d with physically motivated measures Q. Yang et al. 10.1098/rspa.2020.0161
26. How dependent are quantitative volcanic ash concentration and along‐flight dosage forecasts to model structural choices? L. James et al. 10.1002/met.70003
27. Emulation of greenhouse‐gas sensitivities using variational autoencoders L. Cartwright et al. 10.1002/env.2754
28. 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. 10.3390/rs15153760
29. Technical note: Exploring parameter and meteorological uncertainty via emulation in volcanic ash atmospheric dispersion modelling J. Salter et al. 10.5194/acp-24-6251-2024
30. Calculating and communicating ensemble‐based volcanic ash dosage and concentration risk for aviation A. Prata et al. 10.1002/met.1759
31. The Development of Volcanic Ash Cloud Layers over Hours to Days Due to Atmospheric Turbulence Layering M. Bursik et al. 10.3390/atmos12020285
32. Assessing the value meteorological ensembles add to dispersion modelling using hypothetical releases S. Leadbetter et al. 10.5194/acp-22-577-2022
33. Advancing Scientific Understanding of the Global Methane Budget in Support of the Paris Agreement A. Ganesan et al. 10.1029/2018GB006065
34. Visualizing Volcanic Ash Forecasts: Scientist and Stakeholder Decisions Using Different Graphical Representations and Conflicting Forecasts K. Mulder et al. 10.1175/WCAS-D-16-0062.1