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| Titel |
Assimilating Aircraft-based measurements to improve the State of Distal Volcanic Ash Cloud |
| VerfasserIn |
Guangliang Fu, Hai Xiang Lin, Arnold Heemink, Arjo Segers, Sha Lu, Thorgeir Palsson |
| Konferenz |
EGU General Assembly 2015
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250106416
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| Publikation (Nr.) |
 EGU/EGU2015-6086.pdf |
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| Zusammenfassung |
| The sudden eruption at the 1666 m high, ice-capped Eyjafjallajökull volcano, in south Iceland
during 14 April to 23 May 2010, had caused an unprecedented closure of the European and
North Atlantic airspace resulting in global economic losses of US$5 billion. This has initiated
a lot of research on how to improve aviation advice after eruption onset. Good estimation of
both the state of volcanic ash cloud and the emission of volcano are crucial for providing a
successful aviation advice.
Currently most of the approaches, employing satellite-based and ground-based
measurements, are in the focus of improving the definition of Eruption Source Parameters
(ESPs) such as plume height and mass eruption rate, which are certainly very important for
estimating volcano emission and state of volcanic ash cloud near to the volcano. However, for
ash cloud state in a far field, these approaches can hardly make improvements. This is mainly
because the influence of ESPs on the ash plume becomes weaker as the distance to the
volcano is getting farther, thus for a distal plume the information of ESPs will have little
influence.
This study aims to find an efficient way to improve the state of distal volcanic ash cloud.
We use real-life aircraft-based observations, measured along Dutch border between
Borken and Twist during the 2010 Eyjafjallajökull eruption, in an data assimilation
system combining with a transport model to identify the potential benefit of this
kind of observations and the influence on the ash state around Dutch border. We
show that assimilating aircraft-based measurements can significantly improve the
state of distal ash clouds, and further provide an improved aviation advice on distal
ash plume. We compare the performances of different sequential data assimilation
methods. The results show standard Ensemble Kalman Filter (EnKF) works better
than others, which is because of the strong nonlinearity of the dynamics and the
EnKF’s resampling Gaussianity nature. Furthermore, another important aspect of data
assimilation methodology related to time-correlated errors is also investigated. The
result shows for assimilating aircraft-based timely measurements in a far field,
time-correlation of model errors on the state is critical to the performance of the assimilation
system. |
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