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| Titel |
Assimilation of satellite-retrieved data to improve forecasts of volcanic ash concentrations |
| VerfasserIn |
Guangliang Fu, Haixiang Lin, Arnold Heemink, Arjo Segers, Sha Lu |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250123846
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| Publikation (Nr.) |
 EGU/EGU2016-3166.pdf |
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| Zusammenfassung |
| Since the 2010 Eyjafjallajökull volcano eruption caused a big problem to aviation and
economy, improvement on volcanic ash forecast has been put onto the research
agenda. Satellite-based measurements are considered as the most common and
cheapest type of volcanic ash observations. However, due to its intrinsic functionality,
satellite-retrived two-dimensional data can not be easily and directly combined
with a three-dimensional volcanic ash model to improve volcanic ash forecasts
continuously.
Here we propose a satellite observational operator to transfer 2D volcanic ash
mass loadings to 3D concentrations. The uncertainties of reconstructed 3D ash
concentrations are also quantified. Sequential data assimilation is used to continuously
assimilate the reconstructed volcanic ash concentrations. The results are evaluated in a
multi-observational network including satellite-based measurements and aircraft in-situ
measurements.
Here we show for long-time assimilating satellite-based measurements, Ensemble Squre
Root Filter (EnSR), as a common sequential data assimilation technique, is more efficient
than Ensemble Kalman Filter (EnKF) because the ensemble size required for EnSR is
considerable less than the ensemble size of EnKF for a comparable assimilation performance.
Moreover, the forecast after assimilation is validated to be accurate and valid within 15 hours. |
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