Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 13, issue 1
Articles | Volume 13, issue 1
https://doi.org/10.5194/gmd-13-1-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-1-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 1
Development and technical paper
 | 
02 Jan 2020
Development and technical paper |  | 02 Jan 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)

Soledad Osores, Juan Ruiz, Arnau Folch, and Estela Collini

Data sets

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 code S. Osores, J. Ruiz, A. Folch, and E. Collini https://doi.org/10.5281/zenodo.3066310

FALL3D (version 7.3.1) model A. Folch, A. Costa, G. Macedonio http://datasim.ov.ingv.it/models/fall3d.html

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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 ash concentration forecasts.
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