Articles | Volume 15, issue 3
https://doi.org/10.5194/gmd-15-1037-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1037-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Particle-filter-based volcanic ash emission inversion applied to a hypothetical sub-Plinian Eyjafjallajökull eruption using the Ensemble for Stochastic Integration of Atmospheric Simulations (ESIAS-chem) version 1.0
Institute for Energy and Climate Research – Troposphere (IEK-8), Forschungszentrum Jülich, Jülich, Germany
Rhenish Institute for Environmental Research at the University of Cologne, Cologne, Germany
Anne Caroline Lange
Institute for Energy and Climate Research – Troposphere (IEK-8), Forschungszentrum Jülich, Jülich, Germany
Rhenish Institute for Environmental Research at the University of Cologne, Cologne, Germany
Hendrik Elbern
Institute for Energy and Climate Research – Troposphere (IEK-8), Forschungszentrum Jülich, Jülich, Germany
Rhenish Institute for Environmental Research at the University of Cologne, Cologne, Germany
Related authors
Lu Liu, Thorsten Hohaus, Andreas Hofzumahaus, Frank Holland, Hendrik Fuchs, Ralf Tillmann, Birger Bohn, Stefanie Andres, Zhaofeng Tan, Franz Rohrer, Vlassis A. Karydis, Vaishali Vardhan, Philipp Franke, Anne C. Lange, Anna Novelli, Benjamin Winter, Changmin Cho, Iulia Gensch, Sergej Wedel, Andreas Wahner, and Astrid Kiendler-Scharr
EGUsphere, https://doi.org/10.5194/egusphere-2025-3074, https://doi.org/10.5194/egusphere-2025-3074, 2025
Short summary
Short summary
We measured air particles at a rural site in Germany over a year to understand how their sources and properties change with the seasons. Particles from natural sources peaked in summer, especially during heatwaves, while those from burning activities like residential heating and wildfires dominated in colder months. Winds carrying air from other regions also influenced particle levels. These findings link air quality to climate change and energy transitions.
Alexander Hermanns, Anne Caroline Lange, Julia Kowalski, Hendrik Fuchs, and Philipp Franke
EGUsphere, https://doi.org/10.5194/egusphere-2025-450, https://doi.org/10.5194/egusphere-2025-450, 2025
Short summary
Short summary
For air quality analyses, data assimilation models split available data into assimilation and validation data sets. The former is used to generate the analysis, the latter to verify the simulations. A preprocessor classifying the observations by the data characteristics is developed based on clustering algorithms. The assimilation and validation data sets are compiled by equally allocating data of each cluster. The resulting improvement of the analysis is evaluated with EURAD-IM.
Hassnae Erraji, Philipp Franke, Astrid Lampert, Tobias Schuldt, Ralf Tillmann, Andreas Wahner, and Anne Caroline Lange
Atmos. Chem. Phys., 24, 13913–13934, https://doi.org/10.5194/acp-24-13913-2024, https://doi.org/10.5194/acp-24-13913-2024, 2024
Short summary
Short summary
Four-dimensional variational data assimilation allows for the simultaneous optimisation of initial values and emission rates by using trace-gas profiles from drone observations in a regional air quality model. Assimilated profiles positively impact the representation of air pollutants in the model by improving their vertical distribution and ground-level concentrations. This case study highlights the potential of drone data to enhance air quality analyses including local emission evaluation.
Ralf Tillmann, Georgios I. Gkatzelis, Franz Rohrer, Benjamin Winter, Christian Wesolek, Tobias Schuldt, Anne C. Lange, Philipp Franke, Elmar Friese, Michael Decker, Robert Wegener, Morten Hundt, Oleg Aseev, and Astrid Kiendler-Scharr
Atmos. Meas. Tech., 15, 3827–3842, https://doi.org/10.5194/amt-15-3827-2022, https://doi.org/10.5194/amt-15-3827-2022, 2022
Short summary
Short summary
We report in situ measurements of air pollutant concentrations within the planetary boundary layer on board a Zeppelin in Germany. The low costs of commercial flights provide an affordable and efficient method to improve our understanding of changes in emissions in space and time. The experimental setup expands the capabilities of this platform and provides insights into primary and secondary pollution observations and planetary boundary layer dynamics which determine air quality significantly.
Benjamin Gaubert, Louisa K. Emmons, Kevin Raeder, Simone Tilmes, Kazuyuki Miyazaki, Avelino F. Arellano Jr., Nellie Elguindi, Claire Granier, Wenfu Tang, Jérôme Barré, Helen M. Worden, Rebecca R. Buchholz, David P. Edwards, Philipp Franke, Jeffrey L. Anderson, Marielle Saunois, Jason Schroeder, Jung-Hun Woo, Isobel J. Simpson, Donald R. Blake, Simone Meinardi, Paul O. Wennberg, John Crounse, Alex Teng, Michelle Kim, Russell R. Dickerson, Hao He, Xinrong Ren, Sally E. Pusede, and Glenn S. Diskin
Atmos. Chem. Phys., 20, 14617–14647, https://doi.org/10.5194/acp-20-14617-2020, https://doi.org/10.5194/acp-20-14617-2020, 2020
Short summary
Short summary
This study investigates carbon monoxide pollution in East Asia during spring using a numerical model, satellite remote sensing, and aircraft measurements. We found an underestimation of emission sources. Correcting the emission bias can improve air quality forecasting of carbon monoxide and other species including ozone. Results also suggest that controlling VOC and CO emissions, in addition to widespread NOx controls, can improve ozone pollution over East Asia.
Lu Liu, Thorsten Hohaus, Andreas Hofzumahaus, Frank Holland, Hendrik Fuchs, Ralf Tillmann, Birger Bohn, Stefanie Andres, Zhaofeng Tan, Franz Rohrer, Vlassis A. Karydis, Vaishali Vardhan, Philipp Franke, Anne C. Lange, Anna Novelli, Benjamin Winter, Changmin Cho, Iulia Gensch, Sergej Wedel, Andreas Wahner, and Astrid Kiendler-Scharr
EGUsphere, https://doi.org/10.5194/egusphere-2025-3074, https://doi.org/10.5194/egusphere-2025-3074, 2025
Short summary
Short summary
We measured air particles at a rural site in Germany over a year to understand how their sources and properties change with the seasons. Particles from natural sources peaked in summer, especially during heatwaves, while those from burning activities like residential heating and wildfires dominated in colder months. Winds carrying air from other regions also influenced particle levels. These findings link air quality to climate change and energy transitions.
Alexander Hermanns, Anne Caroline Lange, Julia Kowalski, Hendrik Fuchs, and Philipp Franke
EGUsphere, https://doi.org/10.5194/egusphere-2025-450, https://doi.org/10.5194/egusphere-2025-450, 2025
Short summary
Short summary
For air quality analyses, data assimilation models split available data into assimilation and validation data sets. The former is used to generate the analysis, the latter to verify the simulations. A preprocessor classifying the observations by the data characteristics is developed based on clustering algorithms. The assimilation and validation data sets are compiled by equally allocating data of each cluster. The resulting improvement of the analysis is evaluated with EURAD-IM.
Hassnae Erraji, Philipp Franke, Astrid Lampert, Tobias Schuldt, Ralf Tillmann, Andreas Wahner, and Anne Caroline Lange
Atmos. Chem. Phys., 24, 13913–13934, https://doi.org/10.5194/acp-24-13913-2024, https://doi.org/10.5194/acp-24-13913-2024, 2024
Short summary
Short summary
Four-dimensional variational data assimilation allows for the simultaneous optimisation of initial values and emission rates by using trace-gas profiles from drone observations in a regional air quality model. Assimilated profiles positively impact the representation of air pollutants in the model by improving their vertical distribution and ground-level concentrations. This case study highlights the potential of drone data to enhance air quality analyses including local emission evaluation.
Augustin Colette, Gaëlle Collin, François Besson, Etienne Blot, Vincent Guidard, Frederik Meleux, Adrien Royer, Valentin Petiot, Claire Miller, Oihana Fermond, Alizé Jeant, Mario Adani, Joaquim Arteta, Anna Benedictow, Robert Bergström, Dene Bowdalo, Jorgen Brandt, Gino Briganti, Ana C. Carvalho, Jesper Heile Christensen, Florian Couvidat, Ilia D’Elia, Massimo D’Isidoro, Hugo Denier van der Gon, Gaël Descombes, Enza Di Tomaso, John Douros, Jeronimo Escribano, Henk Eskes, Hilde Fagerli, Yalda Fatahi, Johannes Flemming, Elmar Friese, Lise Frohn, Michael Gauss, Camilla Geels, Guido Guarnieri, Marc Guevara, Antoine Guion, Jonathan Guth, Risto Hänninen, Kaj Hansen, Ulas Im, Ruud Janssen, Marine Jeoffrion, Mathieu Joly, Luke Jones, Oriol Jorba, Evgeni Kadantsev, Michael Kahnert, Jacek W. Kaminski, Rostislav Kouznetsov, Richard Kranenburg, Jeroen Kuenen, Anne Caroline Lange, Joachim Langner, Victor Lannuque, Francesca Macchia, Astrid Manders, Mihaela Mircea, Agnes Nyiri, Miriam Olid, Carlos Pérez García-Pando, Yuliia Palamarchuk, Antonio Piersanti, Blandine Raux, Miha Razinger, Lennard Robertson, Arjo Segers, Martijn Schaap, Pilvi Siljamo, David Simpson, Mikhail Sofiev, Anders Stangel, Joanna Struzewska, Carles Tena, Renske Timmermans, Thanos Tsikerdekis, Svetlana Tsyro, Svyatoslav Tyuryakov, Anthony Ung, Andreas Uppstu, Alvaro Valdebenito, Peter van Velthoven, Lina Vitali, Zhuyun Ye, Vincent-Henri Peuch, and Laurence Rouïl
EGUsphere, https://doi.org/10.5194/egusphere-2024-3744, https://doi.org/10.5194/egusphere-2024-3744, 2024
Short summary
Short summary
The Copernicus Atmosphere Monitoring Service – Regional Production delivers daily forecasts, analyses, and reanalyses of air quality in Europe. The Service relies on a distributed modelling production by eleven leading European modelling teams following stringent requirements with an operational design which has no equivalent in the world. All the products are full, free, open and quality assured and disseminated with a high level of reliability.
Yen-Sen Lu, Garrett H. Good, and Hendrik Elbern
Geosci. Model Dev., 16, 1083–1104, https://doi.org/10.5194/gmd-16-1083-2023, https://doi.org/10.5194/gmd-16-1083-2023, 2023
Short summary
Short summary
The Weather Forecasting and Research (WRF) model consists of many parameters and options that can be adapted to different conditions. This expansive sensitivity study uses a large-scale simulation system to determine the most suitable options for predicting cloud cover in Europe for deterministic and probabilistic weather predictions for day-ahead forecasting simulations.
Ralf Tillmann, Georgios I. Gkatzelis, Franz Rohrer, Benjamin Winter, Christian Wesolek, Tobias Schuldt, Anne C. Lange, Philipp Franke, Elmar Friese, Michael Decker, Robert Wegener, Morten Hundt, Oleg Aseev, and Astrid Kiendler-Scharr
Atmos. Meas. Tech., 15, 3827–3842, https://doi.org/10.5194/amt-15-3827-2022, https://doi.org/10.5194/amt-15-3827-2022, 2022
Short summary
Short summary
We report in situ measurements of air pollutant concentrations within the planetary boundary layer on board a Zeppelin in Germany. The low costs of commercial flights provide an affordable and efficient method to improve our understanding of changes in emissions in space and time. The experimental setup expands the capabilities of this platform and provides insights into primary and secondary pollution observations and planetary boundary layer dynamics which determine air quality significantly.
Annika Vogel and Hendrik Elbern
Geosci. Model Dev., 14, 5583–5605, https://doi.org/10.5194/gmd-14-5583-2021, https://doi.org/10.5194/gmd-14-5583-2021, 2021
Short summary
Short summary
While atmospheric chemical forecasts rely on uncertain model parameters, their huge dimensions hamper an efficient uncertainty estimation. This study presents a novel approach to efficiently sample these uncertainties by extracting dominant dependencies and correlations. Applying the algorithm to biogenic emissions, their uncertainties can be estimated from a low number of dominant components. This states the capability of an efficient treatment of parameter uncertainties in atmospheric models.
Annika Vogel and Hendrik Elbern
Atmos. Chem. Phys., 21, 4039–4057, https://doi.org/10.5194/acp-21-4039-2021, https://doi.org/10.5194/acp-21-4039-2021, 2021
Short summary
Short summary
Forecasts of biogenic trace gases highly depend on the model setup and input fields. This study identifies sources of related forecast uncertainties for biogenic gases. Exceptionally high differences in both biogenic emissions and pollutant transport in the Po Valley are identified to be caused by the representation of the land surface and boundary layer dynamics. Consequently, changes in the model configuration are shown to induce significantly different local concentrations of biogenic gases.
Benjamin Gaubert, Louisa K. Emmons, Kevin Raeder, Simone Tilmes, Kazuyuki Miyazaki, Avelino F. Arellano Jr., Nellie Elguindi, Claire Granier, Wenfu Tang, Jérôme Barré, Helen M. Worden, Rebecca R. Buchholz, David P. Edwards, Philipp Franke, Jeffrey L. Anderson, Marielle Saunois, Jason Schroeder, Jung-Hun Woo, Isobel J. Simpson, Donald R. Blake, Simone Meinardi, Paul O. Wennberg, John Crounse, Alex Teng, Michelle Kim, Russell R. Dickerson, Hao He, Xinrong Ren, Sally E. Pusede, and Glenn S. Diskin
Atmos. Chem. Phys., 20, 14617–14647, https://doi.org/10.5194/acp-20-14617-2020, https://doi.org/10.5194/acp-20-14617-2020, 2020
Short summary
Short summary
This study investigates carbon monoxide pollution in East Asia during spring using a numerical model, satellite remote sensing, and aircraft measurements. We found an underestimation of emission sources. Correcting the emission bias can improve air quality forecasting of carbon monoxide and other species including ozone. Results also suggest that controlling VOC and CO emissions, in addition to widespread NOx controls, can improve ozone pollution over East Asia.
Cited articles
Ackermann, I. J., Hass, H., Memmesheimer, M., Ebel, A., Binkowski, F. S., and
Shankar, U.: Modal aerosol dynamics model for Europe: Development and
first applications, Atmos. Environ., 32, 2981–2999, 1998. a
Arason, P., Petersen, G. N., and Bjornsson, H.: Observations of the altitude of the volcanic plume during the eruption of Eyjafjallajökull, April–May 2010, Earth Syst. Sci. Data, 3, 9–17, https://doi.org/10.5194/essd-3-9-2011, 2011. a, b
Bardintzeff, J.-M. and McBirney, A. R.: Volcanology, Jones & Bartlett
Learning, 268 pp., 2000. a
Bengtsson, T., Bickel, P., and Li, B.: Curse-of-dimensionality revisited:
Collapse of the particle filter in very large scale systems, in: Probability
and Statistics: Essays in Honor of David A. Freedman, edited by: Nolan, D. and
Speed, T., Vol. 2 of Collections, Institute of
Mathematical Statistics, Beachwood, Ohio, USA,
316–334, https://doi.org/10.1214/193940307000000518, 2008. a
Bickel, P., Li, B., and Bengtsson, T.: Sharp failure rates for the bootstrap
particle filter in high dimensions, in: Pushing the limits of contemporary
statistics: Contributions in honor of Jayanta K. Ghosh, Vol. 3,
Institute of Mathematical Statistics, 318–329, 2008. a
Bursik, M., Jones, M., Carn, S., Dean, K., Patra, A., Pavolonis, M., Pitman,
E. B., Singh, T., Singla, P., Webley, P., Bjornsson, H., and Ripepe, M.:
Estimation and propagation of volcanic source parameter uncertainty in an ash
transport and dispersal model: Application to the Eyjafjallajökull
plume of 14–16 April 2010, Bull Volc., 74, 2321–2338,
https://doi.org/10.1007/s00445-012-0665-2, 2012. a
Clarisse, L. and Prata, F.: Chapter 11 – Infrared Sounding of Volcanic Ash, in:
Volcanic Ash, edited by: Mackie, S., Cashman, K., Ricketts, H., Rust, A., and
Watson, M., Elsevier,
189–215, https://doi.org/10.1016/B978-0-08-100405-0.00017-3, 2016. a
Costa, A., Suzuki, Y. J., Cerminara, M., Devenish, B., Ongaro, T. E., Herzog,
M., Van Eaton, A., Denby, L., Bursik, M., de' Michieli Vitturi, M.,
Engwell, S., Neri, A., Barsotti, S., Folch, A., Macedonio, G., Girault, F.,
Carazzo, G., Tait, S., Kaminski, E., Mastin, L., Woodhouse, M., Phillips, J.,
Hogg, A., Degruyter, W., and Bonadonna, C.: Results of the eruptive column
model inter-comparison study, J. Volcanol. Geotherm. Res.,
326, 2–25, 2016. a
Daley, R.: Atmospheric Data Analysis, Cambridge Univ. Press, 457 pp., 1991. a
Dare, R. A., Smith, D. H., and Naughton, M. J.: Ensemble prediction of the
dispersion of volcanic ash from the 13 February 2014 eruption of Kelut,
Indonesia, J. Appl. Meteor. Clim., 55, 61–78, 2016. a
Denlinger, R. P., Pavolonis, M., and Sieglaff, J.: A robust method to forecast
volcanic ash clouds, J. Geophys. Res., 117, D13208,
https://doi.org/10.1029/2012JD017732, 2012. a
Eckhardt, S., Prata, A. J., Seibert, P., Stebel, K., and Stohl, A.: Estimation of the vertical profile of sulfur dioxide injection into the atmosphere by a volcanic eruption using satellite column measurements and inverse transport modeling, Atmos. Chem. Phys., 8, 3881–3897, https://doi.org/10.5194/acp-8-3881-2008, 2008. a
Elbern, H., Strunk, A., Schmidt, H., and Talagrand, O.: Emission rate and chemical state estimation by 4-dimensional variational inversion, Atmos. Chem. Phys., 7, 3749–3769, https://doi.org/10.5194/acp-7-3749-2007, 2007. a
Folch, A., Costa, A., and Macedonio, G.: FPLUME-1.0: An integral volcanic plume model accounting for ash aggregation, Geosci. Model Dev., 9, 431–450, https://doi.org/10.5194/gmd-9-431-2016, 2016. a
Franke, P.: ESIAS-chem, Zenodo [data set], https://doi.org/10.5281/zenodo.4736071, 2021. a
Friese, E. and Ebel, A.: Temperature dependent thermodynamic model of the
system H+ – NH – Na+ – SO – NO – Cl− –
H2O, J. Phys. Chem. A, 114, 11595–11631, 2010. a
Fu, G., Lin, H. X., Heemink, A., Lu, S., Segers, A., van Velzen, N., Lu, T., and Xu, S.: Accelerating volcanic ash data assimilation using a mask-state algorithm based on an ensemble Kalman filter: a case study with the LOTOS-EUROS model (version 1.10), Geosci. Model Dev., 10, 1751–1766, https://doi.org/10.5194/gmd-10-1751-2017, 2017. a
Gao, F. and Han, L.: Implementing the Nelder-Mead simplex algorithm with
adaptive parameters, Comput. Opt. Appl., 51, 259–277,
https://doi.org/10.1007/s10589-010-9329-3, 2012. a
Gordon, N. J., Salmond, D. J., and Smith, A. F. M.: Novel approach to
nonlinear/non-Gaussian Bayesian state estimation, in: IEE Proceedings-F
(Radar and Signal Processing), 140, 107–113, 1993. a
Houtekamer, P. L. and Mitchell, H. L.: Data assimilation using an ensemble
Kalman filter technique, Mon. Weather Rev., 126, 796–811, 1998. a
Jülich Supercomputing Centre: JUQUEEN: IBM Blue Gene/Q Supercomputer
System at the Jülich Supercomputing Centre, Journal of Large-Scale
Research Facilities, 1, https://doi.org/10.17815/jlsrf-1-18, 2015. a
Klein, K. and Neira, J.: Nelder-Mead simplex optimization routine for
large-scale problems: A distributed memory implementation, Comput. Econ.,
43, 447–461, 2014. a
Kristiansen, N. I., Stohl, A., Prata, A. J., Bukowiecki, N., Dacre, H.,
Eckhardt, S., Henne, S., Hort, M. C., Johnson, B. T., Marenco, F., Neininger,
B., Reitebuch, O., Seibert, P., Thomson, D. J., Webster, H. N., and
Weinzierl, B.: Performance assessment of a volcanic ash transport model
mini-ensemble used for inverse modeling of the 2010 Eyjafjallajökull
eruption, J. Geophys. Res., 117, D00U11, https://doi.org/10.1029/2011JD016844, 2012. a
Kylling, A., Kahnert, M., Lindqvist, H., and Nousiainen, T.: Volcanic ash infrared signature: porous non-spherical ash particle shapes compared to homogeneous spherical ash particles, Atmos. Meas. Tech., 7, 919–929, https://doi.org/10.5194/amt-7-919-2014, 2014. a
Ventress, L. J., McGarragh, G., Carboni, E., Smith, A. J., and Grainger, R. G.: Retrieval of ash properties from IASI measurements, Atmos. Meas. Tech., 9, 5407–5422, https://doi.org/10.5194/amt-9-5407-2016, 2016. a
Lagarias, J. C., Reeds, J. A., Wright, M. H., and Wright, P. E.: Convergence
properties of the Nelder-Mead simplex method in low dimensions,
SIAM J. Optim., 9, 112–147, 1998. a
Lee, D. and Wiswall, M.: A parallel implementation of the simplex function
minimization routine, Comput. Econ., 30, 171–187,
https://doi.org/10.1007/s10614-007-9094-2, 2007. a
Liu, J. S. and Chen, R.: Sequential Monte Carlo methods for dynamic
systems, J. Am. Stat. Assoc., 93, 1032–1044, 1998. a
Lu, S., Lin, H. X., Heemink, A. W., Fu, G., and Segers, A. J.: Estimation of
volcanic ash emissions using trajectory-based 4d-var data assimilation,
Mon. Weather Rev., 144, 575–589, 2016. a
Madankan, R., Pouget, S., Singla, P., Bursik, M., Dehn, J., Jones, M., Patra,
A., Pavolonis, M., Pitman, E. B., Singh, T., and Webley, P.: Computation of
probabilistic hazard maps and source parameter estimation for volcanic ash
transport and dispersion, J. Comp. Phys., 271, 39–59, 2014. a
Mastin, L. G., Guffanti, M., Servranckx, R., Webley, P., Barsotti, S., Dean,
K., Durant, A., Ewert, J. W., Neri, A., Rose, W. I., Schneider, D., Siebert,
L., Stunder, B., Swanson, G., Tupper, A., Volentik, A., and Waythomas, C. F.:
A multidisciplinary effort to assign realistic source parameters to models of
volcanic ash–cloud transport and dispersion during eruptions,
J. Volc. Geotherm. Res., 186, 10–21, 2009. a, b
McKinnon, K. I. M.: Convergence of the Nelder-Mead simplex method to a
nonstationary point, SIAM J. Optim., 9, 148–158, 1998. a
Nelder, J. A. and Mead, R.: A simplex method for function minimization,
Comp. J., 7, 308–313, 1965. a
Osores, S., Ruiz, J., Folch, A., and Collini, E.: 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), Geosci. Model Dev., 13, 1–22, https://doi.org/10.5194/gmd-13-1-2020, 2020. a
Pardini, F., Corradini, S., Costa, A., Ongaro, T. E., Merucci, L., Neri, A.,
Stelitano, D., and de’ Michieli Vitturi, M.: Ensemble-Based Data
Assimilation of Volcanic Ash Clouds from Satellite Observations: Application
to the 24 December 2018 Mt. Etna Explosive Eruption, Atmosphere, 11, 4,
https://doi.org/10.3390/atmos11040359, 2020. a
Piontek, D., Bugliaro, L., Kar, J., Schumann, U., Marenco, F., Plu, M., and Voigt, C.: The New Volcanic Ash
Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2.
Validation, Remote Sens., 13, 3128 https://doi.org/10.3390/rs13163128, 2021. a
Prata, A. J. and Prata, A. T.: Eyjafjallajökull volcanic ash concentrations
determined using Spin Enhanced Visible and Infrared Imager
measurements, J. Geophys. Res., 117, D00U23
https://doi.org/10.1029/2011JD016800, 2012. a, b
Prata, A. T., Mingari, L., Folch, A., Macedonio, G., and Costa, A.: FALL3D-8.0: a computational model for atmospheric transport and deposition of particles, aerosols and radionuclides – Part 2: Model validation, Geosci. Model Dev., 14, 409–436, https://doi.org/10.5194/gmd-14-409-2021, 2021. a
Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., and
Ratier, A.: An introduction to Meteosat Second Generation (MSG),
B. Am. Meteor. Soc., 83, 977–992, 2002. a
Schmidt, A.: Modelling Tropospheric Volcanic Aerosol, Springer, 2013. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda,
M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A description of the
advanced research WRF version 3, Tech. Rep., NCAR Technical note
NCAR/TN-475+STR, https://doi.org/10.5065/D68S4MVH, 113 pp., 2008. a
Snyder, C., Bengtsson, T., Bickel, P., and Anderson, J.: Obstacles to
high-dimensional particle filtering, Mon. Weather Rev., 136, 4629–4640,
2008. a
Sparks, R. S. J., Bursik, M. I., Carey, S. N., Gilbert, J., Glaze, L. S.,
Sigurdsson, H., and Woods, A.: Volcanic plumes, John Wiley, Chichester,
N. Y., USA, 557 pp., 1997. a
Stefanescu, E. R., Patra, A. K., Bursik, M. I., Madankan, R., Pouget, S.,
jones, M., Singla, P., Singh, T., Pitman, E. B., Pavolonis, M., Morton, D.,
Webley, P., and Dehn, J.: Temporal, probabilistic mapping of ash clouds using
wind field stochstic variability and uncertain eruption source parameters:
Example of the 14 April 2010 Eyjafjallajökull eruption,
J. Adv. Model. Earth Syst., 6, 1173–1184, 2014. a
Stohl, A., Prata, A. J., Eckhardt, S., Clarisse, L., Durant, A., Henne, S., Kristiansen, N. I., Minikin, A., Schumann, U., Seibert, P., Stebel, K., Thomas, H. E., Thorsteinsson, T., Tørseth, K., and Weinzierl, B.: Determination of time- and height-resolved volcanic ash emissions and their use for quantitative ash dispersion modeling: the 2010 Eyjafjallajökull eruption, Atmos. Chem. Phys., 11, 4333–4351, https://doi.org/10.5194/acp-11-4333-2011, 2011. a, b, c, d
Wen, S. and Rose, W. I.: Retrieval of sizes and total masses of particles in
volcanic clouds using AVHRR bands 4 and 5, J. Geophys. Res., 99,
5421–5431, 1994. a
Western, L., Watson, M., and Francis, P.: Uncertainty in two-channel infrared
remote sensing retrievals of a well-characterised volcanic ash cloud,
Bull Volc., 77, 1–12, 2015. a
Wilkins, K., Benedetti, A., Kristiansen, N., and Lange, A.: Chapter 13 -
Applications of Satellite Observations of Volcanic Ash in Atmospheric
Dispersion Modeling, in: Volcanic Ash, edited by: Mackie, S., Cashman, K.,
Ricketts, H., Rust, A., and Watson, M., Elsevier,
233–246, https://doi.org/10.1016/B978-0-08-100405-0.00019-7, 2016a. a
Wilkins, K. L., Mackie, S., Watson, I. M., Webster, H. N., Thomson, D. J., and
Dacre, H. F.: Data insertion in volcanic ash cloud forecasting,
Ann. Geophys., 57, 1–6, 2014. a
Wilkins, K. L., Western, L. M., and Watson, I. M.: Simulating atmospheric
transport of the 2011 Grímsvötn ash cloud using a data insertion
update scheme, Atmos. Environ., 141, 48–59, 2016c. a
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., Hunt,
W. H., and Young, S. A.: Overview of the CALIPSO mission and CALIOP data
processing algorithms, J. Atmos. Ocean. Tech., 26, 2310–2323, 2009. a
Woodhouse, M. J., Hogg, A. J., Phillips, J. C., and Sparks, R. S. J.:
Interaction between volcanic plumes and wind during the 2010
Eyjafjallajökull eruption, Iceland, J. Geophys. Res.-Sol. Ea., 118,
92–109, 2013. a
Woodhouse, M. J., Hogg, A. J., Phillips, J. C., and Rougier, J. C.: Uncertainty
analysis of a model of wind-blown volcanic plumes, Bull Volc., 77, 83,
https://doi.org/10.1007/s00445-015-0959-2, 2015. a
Short summary
The paper proposes an ensemble-based analysis framework (ESIAS-chem) for time- and altitude-resolved volcanic ash emission fluxes and their uncertainty. The core of the algorithm is an ensemble Nelder–Mead optimization algorithm accompanied by a particle filter update. The performed notional experiments demonstrate the high accuracy of ESIAS-chem in analyzing the vertically resolved volcanic ash in the atmosphere. Further, the system is in general able to estimate the emission fluxes properly.
The paper proposes an ensemble-based analysis framework (ESIAS-chem) for time- and...