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)
Quantitative volcanic ash cloud forecasts are prone to uncertainties coming from the source term quantification (e.g., the eruption strength or vertical distribution of the emitted particles), with consequent implications for an operational ash impact assessment. We present an ensemble-based data as...
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Geoscientific Model Development
2020
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Acceso en línea: | http://hdl.handle.net/20.500.12160/1265 https://doi.org/10.5194/gmd-13-1-2020 |
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I63-R169-20.500.12160-1265 |
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institution |
Servicio Meteorológico Nacional (SMN) |
institution_str |
I-63 |
repository_str |
R-169 |
collection |
El Abrigo - Repositorio Institucional del Servicio Meteorológico Nacional (SMN) |
language |
Inglés |
topic |
VOLCANIC ASH FORECAST DATA ASSIMILATION KALMAN FILTER ETKF–FALL3D |
spellingShingle |
VOLCANIC ASH FORECAST DATA ASSIMILATION KALMAN FILTER ETKF–FALL3D Osores, María Soledad Ruiz, Juan José Collini, Estela Ángela 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) |
topic_facet |
VOLCANIC ASH FORECAST DATA ASSIMILATION KALMAN FILTER ETKF–FALL3D |
description |
Quantitative volcanic ash cloud forecasts are prone to uncertainties coming from the source term quantification (e.g., the eruption strength or vertical distribution of the emitted particles), with consequent implications for an 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 the 3-D ash concentration and source parameters. The ETKF–FALL3D data assimilation system is evaluated by performing observing system simulation experiments (OSSEs) 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 3-D ash concentrations. The results show the potential of the methodology to improve volcanic ash cloud forecasts in operational contexts. |
format |
Artículo |
author |
Osores, María Soledad Ruiz, Juan José Collini, Estela Ángela |
author_facet |
Osores, María Soledad Ruiz, Juan José Collini, Estela Ángela |
author_sort |
Osores, María Soledad |
title |
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) |
title_short |
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) |
title_full |
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) |
title_fullStr |
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) |
title_full_unstemmed |
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) |
title_sort |
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) |
publisher |
Geoscientific Model Development |
publishDate |
2020 |
url |
http://hdl.handle.net/20.500.12160/1265 https://doi.org/10.5194/gmd-13-1-2020 |
work_keys_str_mv |
AT osoresmariasoledad volcanicashforecastusingensemblebaseddataassimilationanensembletransformkalmanfiltercoupledwiththefall3d72modeletkffall3dversion10 AT ruizjuanjose volcanicashforecastusingensemblebaseddataassimilationanensembletransformkalmanfiltercoupledwiththefall3d72modeletkffall3dversion10 AT colliniestelaangela volcanicashforecastusingensemblebaseddataassimilationanensembletransformkalmanfiltercoupledwiththefall3d72modeletkffall3dversion10 |
bdutipo_str |
Repositorios |
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1764820545698791425 |