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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-95
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-95
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 03 Jun 2019

Development and technical paper | 03 Jun 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Volcanic ash forecast using ensemble-based data assimilation: the Ensemble Transform Kalman Filter coupled with FALL3D-7.2 model (ETKF-FALL3D, version 1.0)

Soledad Osores1,2,3, Juan Ruiz4, Arnau Folch5, and Estela Collini6 Soledad Osores et al.
  • 1Servicio Meteorológico Nacional (SMN), Buenos Aires, Argentina
  • 2Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
  • 3Comisión Nacional de Actividades Espaciales (CONAE), Buenos Aires, Argentina
  • 4Centro de Investigaciones del Mar y la Atmósfera, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, CONICET, UBA. UMI-IFAECI (CNRS-CONICET-UBA). Departamento de Ciencias de la Atmòsfera y los Océanos, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires. Buenos Aires, Argentina, Argentina
  • 5Barcelona Supercomputing Center (BSC), Barcelona, Spain
  • 6Servicio de Hidrografía Naval (SHN), Buenos Aires, Argentina

Abstract. Quantitative volcanic ash cloud forecasts are prone to uncertainties coming from the source term quantification (e.g. eruption strength or vertical distribution of the emitted particles), with consequent implications on operational ash impact assessment. We present an ensemble-based data assimilation and forecast system for volcanic ash dispersal and deposition aimed at reducing uncertainties related to eruption source parameters. The FALL3D atmospheric dispersal model is coupled with the Ensemble Transform Kalman Filter (ETKF) data assimilation technique by combining ash mass loading observations with ash dispersal simulations in order to obtain a better joint estimation of 3D ash concentration and source parameters. The ETKF-FALL3D data assimilation system is evaluated performing Observation System Simulation Experiments (OSSE) in which synthetic observations of fine ash mass loadings are assimilated. The evaluation of the ETKF-FALL3D system considering reference states of steady and time-varying eruption source parameters shows that the assimilation process gives both better estimations of ash concentration and time-dependent optimized values of eruption source parameters. The joint estimation of concentrations and source parameters leads to a better analysis and forecast of the 3D ash concentrations. Results show the potential of the methodology to improve volcanic ash cloud forecasts in operational contexts.

Soledad Osores et al.
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Soledad Osores et al.
Data sets

FALL3D-ETKF-V1.0 S. Osores, J. Ruiz, A. Folch, and E. Collini https://doi.org/10.5281/zenodo.3066310

Historical Unidata Internet Data Distribution (IDD) Gridded Model Data. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce, European Centre for Medium-Range Weather Forecasts, and Unidata/University Corporation for Atmospheric Research https://doi.org/10.5065/549X-KE89

Model code and software

FALL3D-ETKF-V1.0 S. Osores, J. Ruiz, A. Folch, and E. Collini https://doi.org/10.5281/zenodo.3066310

Soledad Osores et al.
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Latest update: 16 Jun 2019
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Short summary
Volcanic ash dispersal forecasts are routinely used to avoid aircraft encounters with volcanic ash. However the accuracy of these forecasts depends on the knowledge of key factors that are usually difficult to observe directly. In this work we apply an inverse methodology to improve ash concentration forecasts. Results are encouraging showing that accurate estimations of ash emissions can be performed using the proposed approach leading to an improvement in the ash concentration forecast.
Volcanic ash dispersal forecasts are routinely used to avoid aircraft encounters with volcanic...
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