Editorial Type:
Article
Article Type:
Research Article

Estimation of Volcanic Ash Emissions Using Trajectory-Based 4D-Var Data Assimilation

S. Lu Delft University of Technology, Delft, Netherlands

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H. X. Lin Delft University of Technology, Delft, Netherlands

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A. W. Heemink Delft University of Technology, Delft, Netherlands

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G. Fu Delft University of Technology, Delft, Netherlands

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A. J. Segers TNO, Department of Climate, Air and Sustainability, Utrecht, Netherlands

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Print Publication:
01 Feb 2016
DOI:
https://doi.org/10.1175/MWR-D-15-0194.1
Page(s):
575–589
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Abstract

Volcanic ash forecasting is a crucial tool in hazard assessment and operational volcano monitoring. Emission parameters such as plume height, total emission mass, and vertical distribution of the emission plume rate are essential and important in the implementation of volcanic ash models. Therefore, estimation of emission parameters using available observations through data assimilation could help to increase the accuracy of forecasts and provide reliable advisory information. This paper focuses on the use of satellite total-ash-column data in 4D-Var based assimilations. Experiments show that it is very difficult to estimate the vertical distribution of effective volcanic ash injection rates from satellite-observed ash columns using a standard 4D-Var assimilation approach. This paper addresses the ill-posed nature of the assimilation problem from the perspective of a spurious relationship. To reduce the influence of a spurious relationship created by a radiate observation operator, an adjoint-free trajectory-based 4D-Var assimilation method is proposed, which is more accurate to estimate the vertical profile of volcanic ash from volcanic eruptions. The method seeks the optimal vertical distribution of emission rates of a reformulated cost function that computes the total difference between simulated and observed ash columns. A 3D simplified aerosol transport model and synthetic satellite observations are used to compare the results of both the standard method and the new method.

Denotes Open Access content.

Publisher’s Note: This article was revised on 29 July 2016 to make it available as open access.

Corresponding author address: S. Lu, Delft University of Technology, Mekelweg 4, 2628 CD Delft, Netherlands. E-mail: s.lu-1@tudelft.nl

Abstract

Volcanic ash forecasting is a crucial tool in hazard assessment and operational volcano monitoring. Emission parameters such as plume height, total emission mass, and vertical distribution of the emission plume rate are essential and important in the implementation of volcanic ash models. Therefore, estimation of emission parameters using available observations through data assimilation could help to increase the accuracy of forecasts and provide reliable advisory information. This paper focuses on the use of satellite total-ash-column data in 4D-Var based assimilations. Experiments show that it is very difficult to estimate the vertical distribution of effective volcanic ash injection rates from satellite-observed ash columns using a standard 4D-Var assimilation approach. This paper addresses the ill-posed nature of the assimilation problem from the perspective of a spurious relationship. To reduce the influence of a spurious relationship created by a radiate observation operator, an adjoint-free trajectory-based 4D-Var assimilation method is proposed, which is more accurate to estimate the vertical profile of volcanic ash from volcanic eruptions. The method seeks the optimal vertical distribution of emission rates of a reformulated cost function that computes the total difference between simulated and observed ash columns. A 3D simplified aerosol transport model and synthetic satellite observations are used to compare the results of both the standard method and the new method.

Denotes Open Access content.

Publisher’s Note: This article was revised on 29 July 2016 to make it available as open access.

Corresponding author address: S. Lu, Delft University of Technology, Mekelweg 4, 2628 CD Delft, Netherlands. E-mail: s.lu-1@tudelft.nl
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  • Cacuci, D. G., I. M. Navon, and M. Ionescu-Bujor, 2013: Computational Methods for Data Evaluation and Assimilation. Chapman and Hall/CRC, 373 pp.

    • Search Google Scholar
    • Export Citation

  • Casadevall, T. J., 1994: Alaska Volcano Observatory—The 1989–1990 eruption of Redoubt Volcano, Alaska: Impacts on aircraft operations. J. Volcanol. Geotherm. Res., 62, 301–316, doi:10.1016/0377-0273(94)90038-8.

    • Search Google Scholar
    • Export Citation

  • Courtier, P., and O. Talagrand, 1987: Variational assimilation of meteorological observations with the adjoint vorticity equation. II: Numerical results. Quart. J. Roy. Meteor. Soc., 113, 1329–1347, doi:10.1002/qj.49711347813.

    • Search Google Scholar
    • Export Citation

  • Courtier, P., J. N. Thépaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 1367–1387, doi:10.1002/qj.49712051912.

    • Search Google Scholar
    • Export Citation

  • Dacre, H. F., and Coauthors, 2011: Evaluating the structure and magnitude of the ash plume during the initial phase of the 2010 Eyjafjallajökull eruption using lidar observations and NAME simulations. J. Geophys. Res., 116, D00U03, doi:10.1029/2011JD015608.

    • Search Google Scholar
    • Export Citation

  • Doicu, A., T. Trautmann, and F. Schreier, 2010: Numerical Regularization for Atmospheric Inverse Problems. Springer Praxis, 426 pp.

  • Elbern, H., H. Schmidt, O. Talagrand, and A. Ebel, 2000: 4D-variational data assimilation with an adjoint air quality model for emission analysis. Environ. Model. Software, 15, 539–548, doi:10.1016/S1364-8152(00)00049-9.

    • Search Google Scholar
    • Export Citation

  • Elbern, H., A. Strunk, H. Schmidt, and O. Talagrand, 2007: Emission rate and chemical state estimation by 4-dimensional variational inversion. Atmos. Chem. Phys., 7, 3749–3769, doi:10.5194/acp-7-3749-2007.

    • Search Google Scholar
    • Export Citation

  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 143–10 162, doi:10.1029/94JC00572.

    • Search Google Scholar
    • Export Citation

  • Flemming, J., and A. Inness, 2013: Volcanic sulfur dioxide plume forecasts based on UV satellite retrievals for the 2011 Grímsvötn and the 2010 Eyjafjallajökull eruption. J. Geophys. Res. Atmos., 118, 10 172–10 189, doi:10.1002/jgrd.50753.

    • Search Google Scholar
    • Export Citation

  • Fu, G., H. X. Lin, A. W. Heemink, A. J. Segers, S. Lu, and T. Palsson, 2015: Assimilating aircraft-based measurements to improve forecast accuracy of volcanic ash transport. Atmos. Environ., 115, 170–184, doi:10.1016/j.atmosenv.2015.05.061.

    • Search Google Scholar
    • Export Citation

  • Horwell, C., and P. Baxter, 2006: The respiratory health hazards of volcanic ash: A review for volcanic risk mitigation. Bull. Volcanol., 69, 1–24, doi:10.1007/s00445-006-0052-y.

    • Search Google Scholar
    • Export Citation

  • Le Dimet, F.-X., and O. Talagrand, 1986: Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects. Tellus, 38A, 97–110, doi:10.1111/j.1600-0870.1986.tb00459.x.

    • Search Google Scholar
    • Export Citation

  • Liu, C., Q. Xiao, and B. Wang, 2009: An ensemble-based four-dimensional variational data assimilation scheme. Part II: Observing system simulation experiments with Advanced Research WRF (ARW). Mon. Wea. Rev., 137, 1687–1704, doi:10.1175/2008MWR2699.1.

    • Search Google Scholar
    • Export Citation

  • Mankin, W. G., M. T. Coffey, and A. Goldman, 1992: Airborne observations of SO2, HC1, and O3 in the stratospheric plume of the Pinatubo Volcano in July 1991. Geophys. Res. Lett., 19, 179–182, doi:10.1029/91GL02942.

    • Search Google Scholar
    • Export Citation

  • Mastin, L. G., and Coauthors, 2009: A multidisciplinary effort to assign realistic source parameters to models of volcanic ash-cloud transport and dispersion during eruptions. J. Volcanol. Geotherm. Res., 186, 10–21, doi:10.1016/j.jvolgeores.2009.01.008.

    • Search Google Scholar
    • Export Citation

  • Meirink, J. F., and Coauthors., 2008: Four-dimensional variational data assimilation for inverse modeling of atmospheric methane emissions: Analysis of SCIAMACHY observations. J. Geophys. Res., 113, D17301, doi:10.1029/2007JD009740.

  • Millington, S. C., R. W. Saunders, P. N. Francis, and H. N. Webster, 2012: Simulated volcanic ash imagery: A method to compare NAME ash concentration forecasts with SEVIRI imagery for the Eyjafjallajökull eruption in 2010. J. Geophys. Res., 117, D00U17, doi:10.1029/2011JD016770.

    • Search Google Scholar
    • Export Citation

  • Pierce, R. B., G. Carmichael, P. Sadie, G. Grell, M. Stuefer, M. Pavolonis, and K. Chance, 2013: Development of data assimilation capabilities for SEVIRI volcanic ash retrievals. NOAA Satellite Science Week Virtual Meeting, NOAA. [Available online at http://www.goes-r.gov/downloads/ScienceWeek/2013/presentations/03-20/01-R-Pierce.pdf.]

  • Prata, A. J., 1989: Infrared radiative transfer calculations for volcanic ash clouds. Geophys. Res. Lett., 16, 1293–1296, doi:10.1029/GL016i011p01293.

    • Search Google Scholar
    • Export Citation

  • Reichel, L., and Q. Ye, 2008: Simple square smoothing regularization operators. Electron. Trans. Numer. Anal., 33, 63–83, doi:10.1.1.418.5500.

    • Search Google Scholar
    • Export Citation

  • Robock, A., 2000: Volcanic eruptions and climate. Rev. Geophys., 38, 191–219, doi:10.1029/1998RG000054.

  • Rose, W. I., G. J. S. Bluth, D. J. Schneider, G. G. J. Ernst, C. M. Riley, L. J. Henderson, and R. G. McGimsey, 2001: Observations of volcanic clouds in their first few days of atmospheric residence: The 1992 eruptions of Crater Peak, Mount Spurr Volcano, Alaska. J. Geol., 109, 677–694, doi:10.1086/323189.

    • Search Google Scholar
    • Export Citation

  • Schmetz, J., P. Pili, S. Tjemkes, D. Just, J. Kerkmann, S. Rota, and A. Ratier, 2002: An introduction to Meteosat Second Generation (MSG). Bull. Amer. Meteor. Soc., 83, 977–992, doi:10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2.

    • Search Google Scholar
    • Export Citation

  • Searcy, C., K. Dean, and W. Stringer, 1998: PUFF: A high-resolution volcanic ash tracking model. J. Volcanol. Geotherm. Res., 80, 1–16, doi:10.1016/S0377-0273(97)00037-1.

    • Search Google Scholar
    • Export Citation

  • Smith, A., T. Sears, G. Thomas, E. Carboni, and D. Grainger, 2014: Can we use sulphur dioxide as a proxy for volcanic ash in aviation hazard avoidance? Research Highlights 2014, National Centre for Earth Observation, National Environment Research Council, 8–8. [Available online at http://www.nceo.ac.uk/assets/documents/NCEO_highlights_2014.pdf.]

  • Strunk, A., A. Ebel, H. Elbern, E. Friese, N. Goris, and L. Nieradzik, 2010: Four-dimensional variational assimilation of atmospheric chemical data application to regional modelling of air quality. Large-Scale Scientific Computing, I. Lirkov, S. Margenov, and J. Waśniewski, Eds., Lecture Notes in Computer Science, Vol. 5910, Springer, 214–222.

  • Stuefer, M., S. R. Freitas, G. Grell, P. Webley, S. Peckham, and S. A. McKeen, 2012: Inclusion of ash and SO2 emissions from volcanic eruptions in WRF-CHEM: Development and some applications. Geosci. Model Dev. Discuss., 5, 2571–2597, doi:10.5194/gmdd-5-2571-2012.

    • Search Google Scholar
    • Export Citation

  • Talagrand, O., and P. Courtier, 1987: Variational assimilation of meteorological observations with the adjoint vorticity equation. I: Theory. Quart. J. Roy. Meteor. Soc., 113, 1311–1328, doi:10.1002/qj.49711347812.

    • Search Google Scholar
    • Export Citation

  • Tian, X., Z. Xie, and A. Dai, 2008: An ensemble-based explicit four-dimensional variational assimilation method. J. Geophys. Res., 113, D21124, doi:10.1029/2008JD010358.

  • Tian, X., Z. Xie, and Q. Sun, 2011: A POD-based ensemble four-dimensional variational assimilation method. Tellus, 63A, 805–816, doi:10.1111/j.1600-0870.2011.00529.x.

    • Search Google Scholar
    • Export Citation

  • Valdebenito, A. M., S. Tsyro, M. Kahnert, and H. Heiberg, 2010: The EMEP data assimilation system: Technical description and first results. Tech. Rep. 4/2010, Norwegian Meteorological Institute, 30 pp.

  • Wang, X., and Coauthors, 2008: Volcanic dust characterization by EARLINET during Etna’s eruptions in 2001–2002. Atmos. Environ., 42, 893–905, doi:10.1016/j.atmosenv.2007.10.020.

    • Search Google Scholar
    • Export Citation

  • Webley, P. W., T. Steensen, M. Stuefer, G. Grell, S. Freitas, and M. Pavolonis, 2012: Analyzing the Eyjafjallajökull 2010 eruption using satellite remote sensing, lidar and WRF-Chem dispersion and tracking model. J. Geophys. Res., 117, D00U26, doi:10.1029/2011JD016817.

    • Search Google Scholar
    • Export Citation

  • Wen, S., and W. I. Rose, 1994: Retrieval of sizes and total masses of particles in volcanic clouds using AVHRR bands 4 and 5. J. Geophys. Res., 99, 5421–5431, doi:10.1029/93JD03340.

    • Search Google Scholar
    • Export Citation
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