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| Titel |
Design of a Satellite Observational Operator (SOO) for ensemble-based data assimilation to improve volcanic plume forecasts |
| VerfasserIn |
Guangliang Fu, Fred Prata, Hai Xiang Lin, Arnold Heemink, Arjo Segers, Jianbing Jin, Sha Lu |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250139588
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| Publikation (Nr.) |
 EGU/EGU2017-2855.pdf |
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| Zusammenfassung |
| Using data assimilation (DA) is efficient to improve volcanic model forecast accuracy.
Infrared satellite measurements of volcanic ash mass loadings are often used as
input observations for the assimilation scheme. However, because these primary
satellite-retrieved data are often 2D and the ash plume is usually vertically narrow, thus
directly assimilating the 2D ash mass loadings in a 3D volcanic ash model (with
an integral observational operator) can usually introduce large spurious vertical
correlations.
In this study, we look at an approach to avoid the spurious vertical correlations
by not involving the integral operator. (We focus on the case study of the 2010
Eyjafjallajökull volcanic ash plume.) By integrating available data of ash mass loadings
and cloud heights, and data-based thickness assumptions, a Satellite Observational
Operator (SOO) is proposed that translates satellite-retrieved 2D mass loadings to 3D
concentrations. The SOO makes the analysis step of assimilation comparable in the 3D model
space.
Ensemble-based data assimilation is used to assimilate the extracted measurements of ash
concentrations. The results show that satellite data assimilation with SOO can improve the
estimate of volcanic ash state better than the standard assimilation without SOO.
Comparison with both satellite retrieved data and aircraft in situ measurements shows
that the effective volcanic ash forecasts can be obtained after assimilation with
SOO.
In addition, this study provides an idea in the sense of incorporating many available
measurements. We expect the SOO can be potentially improved by incorporating more data,
but at the moment DA with SOO has shown its advantage than the standard way (without
SOO) in dealing with passive satellite data assimilation. |
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