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| Titel |
A two-way-tracking localized ensemble Kalman filter for assimilating aircraft in situ volcanic ash measurements |
| VerfasserIn |
Guangliang Fu, Hai Xiang Lin, Arnold Heemink, Arjo Segers, Martin Verlaan, Tongchao Lu, 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 |
250137422
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| Publikation (Nr.) |
 EGU/EGU2017-119.pdf |
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| Zusammenfassung |
| After the eruption of volcano Eyjafjallajökull in 2010, which had a huge impact to
aviation and economy, improvements of volcanic ash forecasts have been put onto the
research agenda. Data assimilation uses observations to improve the forecast accuracy.
Among the data assimilation approaches, the ensemble Kalman filter (EnKF) is a
well-known and popular method. A proper covariance localization strategy in the
analysis step of EnKF is essential for reducing spurious covariances caused by the
finite ensemble size, as shown for this application for assimilation of aircraft in situ
measurements.
After analyzing the characteristics of the physical forecast error covariances, we
present a two-way tracking approach to define the localization matrix for covariance
localization. The result shows that the Two-way-tracking Localized EnKF (TL-EnKF)
effectively maintains the correctly specified physical covariances and largely reduces
the spurious ones. The computational cost of TL-EnKF is also evaluated and is
shown to be advantageous for both serial and parallel implementations. Compared to
the commonly used distance-based covariance localization, the two-way tracking
approach is shown to be more suitable. In addition, the covariance inflation approach is
verified as an additional improvement to TL-EnKF to achieve more accurate results. |
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