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9 - Data Assimilation of Volcanic Clouds: Recent Advances and Implications on Operational Forecasts

from Part II - ‘Fluid’ Earth Applications: From the Surface to the Space

Published online by Cambridge University Press:  20 June 2023

Alik Ismail-Zadeh
Affiliation:
Karlsruhe Institute of Technology, Germany
Fabio Castelli
Affiliation:
Università degli Studi, Florence
Dylan Jones
Affiliation:
University of Toronto
Sabrina Sanchez
Affiliation:
Max Planck Institute for Solar System Research, Germany
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Summary

Abstract: Operational forecasts of volcanic clouds are a key decision-making component for civil protection agencies and aviation authorities during the occurrence of volcanic crises. Quantitative operational forecasts are challenging due to the large uncertainties that typically exist on characterising volcanic emissions in real time. Data assimilation, including source term inversion, has long been recognised by the scientific community as a mechanism to reduce quantitative forecast errors. In terms of research, substantial progress has occurred during the last decade following the recommendations from the ash dispersal forecast workshops organised by the International Union of Geodesy and Geophysics (IUGG) and the World Meteorological Organization (WMO). The meetings held in Geneva in 2010–11 in the aftermath of the 2010 Eyjafjallajökull eruption identified data assimilation as a research priority. This Chapter reviews the scientific progress and its transfer into operations, which is leveraging a new generation of operational forecast products.

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Publisher: Cambridge University Press
Print publication year: 2023

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References

Amezcua, J., and Van Leeuwen, P. J. (2014). Gaussian anamorphosis in the analysis step of the EnKF: A joint state-variable/observation approach. Tellus A: Dynamic Meteorology and Oceanography, 6(1), 23493. http://doi.org/10.3402/tellusa.v66.23493.Google Scholar
Anderson, J. L., and Anderson, S. L. (1999). A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Monthly Weather Review, 127(12), 2741–58.2.0.CO;2>CrossRefGoogle Scholar
Balis, D., Koukouli, M. E., Siomos, N. et al. (2016). Validation of ash optical depth and layer height retrieved from passive satellite sensors using EARLINET and airborne lidar data: The case of the Eyjafjallajokull eruption. Atmospheric Chemistry and Physics, 16, 5705–20.Google Scholar
Bishop, C. H., Brian, J. E., and Sharanya, J. M. (2001). Adaptive sampling with the Ensemble Transform Kalman Filter. Part 1: Theoretical aspects. Monthly Weather Review, 129(3), 420–36.Google Scholar
Bishop, C. H. (2016). The GIGG-EnKF: Ensemble Kalman filtering for highly skewed non-negative uncertainty distributions. Quarterly Journal of the Royal Meteorological Society, 142(696), 1395–412.CrossRefGoogle Scholar
Boichu, M., Clarisse, L., Khvorostyanov, D., and Clerbaux, C. (2014). Improving volcanic sulfur dioxide cloud dispersal forecasts by progressive assimilation of satellite observations, Geophysical Research Letters. American Geophysical Union, 41, 2637–43.Google Scholar
Bonadonna, C., Folch, A., Loughlin, S., and Puempel, H. (2012). Future developments in modelling and monitoring of volcanic ash clouds: Outcomes from the first IAVCEI-WMO workshop on ash dispersal forecast and civil aviation. Bulletin of Volcanology, 74, 110.CrossRefGoogle Scholar
Burgers, G., Leeuwen, P. J. van, and Evensen, G. (1998). Analysis scheme in the ensemble Kalman filter. Monthly Weather Review, 126(6), 1719–24.Google Scholar
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G. (2018). Data assimilation in the geosciences: An overview of methods, issues, and perspectives. WIREs Climate Change, 9(e535), 150.Google Scholar
Carn, S. A., Krueger, A. J., Krotkov, N. A., Yang, K., and Evans, K. (2009). Tracking volcanic sulfur dioxide clouds for aviation hazard mitigation. Natural Hazards, 51, 325–43.CrossRefGoogle Scholar
Chai, T., Crawford, A., Stunder, B. et al. (2017). Improving volcanic ash predictions with the HYSPLIT dispersion model by assimilating MODIS satellite retrievals. Atmospheric Chemistry and Physics, 17, 2865–79.Google Scholar
Clarisse, L., Hurtmans, D., Clerbaux, C. et al. (2012). Retrieval of sulphur dioxide from the infrared atmospheric sounding interferometer (IASI). Atmospheric Measurement Techniques, 5, 581–94.CrossRefGoogle Scholar
Clarkson, R., and Simpson, H. (2017). Maximising airspace use during volcanic eruptions: Matching engine durability against ash cloud occurrence. In Proceedings of the NATO STO AVT-272 Specialists Meeting on Impact of Volcanic Ash Clouds on Military Operations, vol. 1, pp. 17-1–17-19. www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-AVT-272/MP-AVT-272-17.pdf.Google Scholar
Costa, A., Suzuki, Y., Cerminara, M. et al. (2016). Results of the eruptive column model inter-comparison study, Journal of Volcanology and Geothermal Research, 326, 225.Google Scholar
Crawford, A., Stunder, B., Ngan, F., and Pavalonis, M. (2016). Initializing HYSPLIT with satellite observations of volcanic ash: A case study of the 2008 Kasatochi eruption. Journal of Geophysical Research: Atmospheres, 121(10), 786–10.Google Scholar
Dunn, M. G., and Wade, D. P. (1994). Influence of volcanic ash clouds on gas turbine engines. In Casadevall, T. J., ed., Volcanic ash and aviation safety; Proceedings of the First International Symposium on Volcanic Ash and Aviation Safety held in Seattle, Washington, in July 1991. Reston, VA: US Geological Survey Bulletin 2047, pp. 107–17. https://doi.org/10.3133/b2047.Google Scholar
Eckhardt, S., Prata, A. J., Seibert, P., Stebel, K., and Stohl, A. (2008). Estimation of the vertical profile of sulfur dioxide injection into the atmosphere by a volcanic eruption using satellite column measurements and inverse transport modeling. Atmospheric Chemistry and Physics, 8, 3881–97.Google Scholar
Eliasson, J., and Yoshitani, J. (2015). Airborne measurements of volcanic ash and current state of ash cloud prediction, Disaster Prevention Research Institute Annuals, 58B, 3541. www.dpri.kyoto-u.ac.jp/nenpo/no58/ronbunB/a58b0p03.pdf.Google Scholar
Evensen, G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans, 99(C5), 10143–62.CrossRefGoogle Scholar
Evensen, G. (2003). The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dynamics, 53(4), 343–67.Google Scholar
Flemming, J., and Inness, A. (2013). Volcanic sulfur dioxide plume forecasts based on UV satellite retrievals for the 2011 Grímsvötn and the 2010 Eyjafjallajökull eruption. Journal of Geophysical Research: Atmospheres, 118(17), 10172–89.Google Scholar
Folch, A. (2012). A review of tephra transport and dispersal models: Evolution, current status, and future perspectives. Journal of Volcanology and Geothermal Research, 235–236, 96115.CrossRefGoogle Scholar
Folch, A., Mingari, L., Gutierrez, N. et al. (2020). FALL3D-8.0: a computational model for atmospheric transport and deposition of particles, aerosols and radionuclides. Part 1: Model physics and numerics. Geoscientific Model Development, 13, 1431–58.CrossRefGoogle Scholar
Francis, P. N., Cooke, M. C., and Saunders, R.W. (2012). Retrieval of physical properties of volcanic ash using Meteosat: A case study from the 2010 Eyjafjallajökull eruption. Journal of Geophysical Research: Atmospheres, 117, D00U09. https://doi.org/10.1029/2011JD016788Google Scholar
Fu, G., Lin, H.X., Heemink, A.W. et al. (2015). Assimilating aircraft-based measurements to improve forecast accuracy of volcanic ash transport. Atmospheric Environment, 115, 170–84.CrossRefGoogle Scholar
Fu, G., Heemink, A., Lu, S. et al. (2016). Model-based aviation advice on distal volcanic ash clouds by assimilating aircraft in situ measurements. Atmospheric Chemistry and Physics, 16(14), 9189–200.Google Scholar
Fu, G., Prata, F., Lin, H. X. et al. (2017). Data assimilation for volcanic ash plumes using a satellite observational operator: A case study on the 2010 Eyjafjallajökull volcanic eruption. Atmospheric Chemistry and Physics, 17(2), 1187–205.Google Scholar
Houtekamer, P. L., and Zhang, F. (2016). Review of the ensemble Kalman filter for atmospheric data assimilation. Monthly Weather Review, 144(12), 4489–532.Google Scholar
Hunt, B. R., Kostelich, E. J., and Szunyogh, I. (2007). Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230(1), 112–26.Google Scholar
Inness, A., Ades, M., Balis, D. et al. (2022). The CAMS volcanic forecasting system utilizing near-real time data assimilation of S5P/TROPOMI SO2 retrievals. Geoscientific Model Development, 15, 971–94.Google Scholar
Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82, 3545.Google Scholar
Kristiansen, N. I., Stohl, A., Prata, A. J. et al. (2010). Remote sensing and inverse transport modeling of the Kasatochi eruption sulfur dioxide cloud. Journal of Geophysical Research: Atmospheres, 115, 118.Google Scholar
Kristiansen, N., Stohl, A., Prata, A. et al. (2012). Performance assessment of a volcanic ash transport model mini-ensemble used for inverse modeling of the 2010 Eyjafjallajökull eruption. Journal of Geophysical Research: Atmospheres, 117, 125.Google Scholar
Lu, S., Lin, H. X., Heemink, A. W., Fu, G., and Segers, A. J. (2016a). Estimation of volcanic ash emissions using trajectory-based 4D-Var data assimilation. Monthly Weather Review, 144(2), 575–89.Google Scholar
Lu, S., Lin, H. X., Heemink, A., Segers, A., and Fu, G. (2016b). Estimation of volcanic ash emissions through assimilating satellite data and ground-based observations. Journal of Geophysical Research: Atmospheres, 121(18), 10971–94.Google Scholar
Mastin, L. G., Guffanti, M. C., and Servranckx, R. et al. (2009). A multidisciplinary effort to assign realistic source parameters to models of volcanic ash-cloud transport and dispersion during eruptions. Journal of Volcanology and Geothermal Research, 186(1–2), 1021.Google Scholar
Miller, T. P., and Casadevall, T. J. (2000). Volcanic ash hazards to aviation. In Sigurdsson, H., ed., Encyclopedia of Volcanoes.San Diego, CA: Academic Press, pp. 915–30.Google Scholar
Mingari, L., Folch, A., Prata, A. T. et al. (2022). Data assimilation of volcanic aerosols using FALL3D+PDAF. Atmospheric Chemistry and Physics, 22, 1773–92.Google Scholar
Moxnes, E. D., Kristiansen, N. I., Stohl, A. et al. (2014). Separation of ash and sulfur dioxide during the 2011 Grímsvötn eruption. Journal of Geophysical Research: Atmospheres, 119, 7477–01.Google Scholar
Muser, L. O., Hoshyaripour, G. A., Bruckert, J. et al. (2020). Particle aging and aerosol–radiation interaction affect volcanic plume dispersion: Evidence from the Raikoke 2019 eruption. Atmospheric Chemistry and Physics, 20, 15015–36.Google Scholar
Nerger, L., Janjić, T., Schröter, J., and Hiller, W. (2012). A unification of ensemble square root Kalman filters. Monthly Weather Review, 140(7), 2335–45.Google Scholar
Osores, S., Ruiz, J., Folch, A., and Collini, E. (2020). 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). Geoscientific Model Development, 13(1), 122.Google Scholar
Pardini, F., Corradini, S., Costa, A. et al. (2020). Ensemble-based data assimilation of volcanic ash clouds from satellite observations: Application to the 24 December 2018 Mt. Etna Explosive Eruption. Atmosphere, 11(4), 359.CrossRefGoogle Scholar
Pavolonis, M. J., Feltz, W. F., Heidinger, A. K., and Gallina, G. M. (2006). A daytime complement to the reverse absorption technique for improved automated detection of volcanic ash. Journal of Atmospheric and Oceanic Technology, 23, 1422–44.Google Scholar
Pelley, R., Cooke, M., Manning, A. et al. (2015). Initial implementation of an inversion technique for estimating volcanic ash source parameters in near real time using satellite retrievals: Forecasting Research Technical Report, vol. 604. Met Exeter, UK: Met Office. https://library.metoffice.gov.uk/Portal/Default/en-GB/DownloadImageFile.ashx?objectId=415&ownerType=0&ownerId=212804.Google Scholar
Prata, A. J. (1989). Observations of volcanic ash clouds in the 10–12-micron window using AVHRR/2 Data. International Journal of Remote Sensing, 10, 751–61.Google Scholar
Prata, A. T. (2016). Remote sensing of volcanic eruptions. In Duarte, J. C. and Schellart, W. P., eds., Plate Boundaries and Natural Hazards, American Geophysical Union (AGU). Hoboken, NJ: Wiley and Sons, pp. 289322.Google Scholar
Prata, F., and Lynch, M. (2019). Passive Earth observations of volcanic clouds in the atmosphere. Atmosphere, 10, 199.Google Scholar
Prata, A. T., Mingari, L., Folch, A., Macedonio, G., and Costa, A. (2021). FALL3D-8.0: A computational model for atmospheric transport and deposition of particles, aerosols and radionuclides. Part 2: Model validation. Geoscientific Model Development, 14, 409–36.Google Scholar
Robock, A., and Oppenheimer, C. (2003). Volcanism and the Earth’s atmosphere: American Geophysical Union, Geophysical Monograph, 139, 360 pp.Google Scholar
Seibert, P. (2000). Inverse modelling of sulfur emissions in Europe based on trajectories. In Kasibhatla, P., Heimann, M., Rayner, P., Mahowald, N., Prinn, R. G., and Hartley, D. E, eds., Inverse Methods in Global Biogeochemical Cycles, Geophysical Monograph 114. Washington, DC: American Geophysical Union, pp. 147–54,Google Scholar
Sparks, R. S. J., Bursik, M. I., Carey, S. N. et al. (1997). Volcanic Plumes. Chichester: John Wiley & Sons.Google Scholar
Steensen, B. M., Kylling, A., Kristiansen, N. I., and Schulz, M. (2017). Uncertainty assessment and applicability of an inversion method for volcanic ash forecasting. Atmospheric Chemistry and Physics, 17, 9205–22.Google Scholar
Stohl, A., Prata, A., Eckhardt, S. et al. (2011). Determination of time- and height-resolved volcanic ash emissions and their use for quantitative ash dispersion modeling: The 2010 Eyjafjallajokull eruption. Atmospheric Chemistry and Physics, 11, 4333–51.Google Scholar
Suzuki, T. (1983). A theoretical model for dispersion of tephra. In Shimozuru, D. and Yokoyama, I, eds., Volcanism: Physics and Tectonics. Tokyo: Arc, pp. 95113.Google Scholar
Theys, N., Hedelt, P., De Smedt, I. et al. (2019). Global monitoring of volcanic SO2 degassing with unprecedented resolution from TROPOMI onboard Sentinel-5 Precursor. Scientific Reports, 9, 2643.Google Scholar
Van Leeuwen, P. J., and Ades, M. (2013). Efficient fully nonlinear data assimilation for geophysical fluid dynamics. Computers & Geosciences, 55, 1627.Google Scholar
Vira, J., Carboni, E., Grainger, R. G., and Sofiev, M. (2017). Variational assimilation of IASI SO2 plume height and total column retrievals in the 2010 eruption of Eyjafjallajökull using the SILAM v5.3 chemistry transport model.Geoscientific Model Development, 10, 19852008.Google Scholar
Wilkins, K. L., Mackie, S., Watson, M. et al. (2014). Data insertion in volcanic ash cloud forecasting. Annals of Geophysics, Fast Track, 2. https://doi.org/10.4401/ag-6624.Google Scholar
Wilkins, K. L., Watson, I. M., Kristiansen, N. I. et al. (2016). Using data insertion with the NAME model to simulate the 8 May 2010 Eyjafjallajökull volcanic ash cloud. Journal of Geophysical Research: Atmospheres, 121, 306–23. https://doi.org/10.1002/2015JD023895.Google Scholar
Zhou, H., Gómez-Hernández, J., Hendricks Franssen, H. J., and Li, L. (2011). An approach to handling non-Gaussianity of parameters and state variables in ensemble Kalman filtering. Advances in Water Resources, 34(7), 844–64.Google Scholar
Zidikheri, M. J., and Potts, R. J. (2015). A simple inversion method for determining optimal dispersion model parameters from satellite detections of volcanic sulfur dioxide. Journal of Geophysical Research: Atmospheres, 120, 9702–17.Google Scholar
Zidikheri, M. J., and Lucas, C. (2021a). A computationally efficient ensemble filtering scheme for quantitative volcanic ash forecasts. Journal of Geophysical Research: Atmospheres, 126, e2020JD033094.CrossRefGoogle Scholar
Zidikheri, M. J., and Lucas, C. (2021b). Improving ensemble volcanic ash forecasts by direct insertion of satellite data and ensemble filtering. Atmosphere, 12, 1215.Google Scholar
Zidikheri, M., Potts, R. J., and Lucas, C. (2016). A probabilistic inverse method for volcanic ash dispersion modelling. The ANZIAM Journal, 56, 194209.CrossRefGoogle Scholar
Zidikheri, M., Lucas, C., and Potts, R. (2017a), Estimation of optimal dispersion model source parameters using satellite detections of volcanic ash. Journal of Geophysical Research: Atmospheres, 122, 8207–32.Google Scholar
Zidikheri, M., Lucas, C., and Potts, R. (2017b). Toward quantitative forecasts of volcanic ash dispersal: Using satellite retrievals for optimal estimation of source terms. Journal of Geophysical Research: Atmospheres, 122, 8187–206.Google Scholar

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