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| Titel |
A practical scheme of the sigma-point Kalman filter for high-dimensional systems |
| VerfasserIn |
Youmin Tang, Xiangming Zhang |
| 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 |
250138651
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| Publikation (Nr.) |
 EGU/EGU2017-1744.pdf |
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| Zusammenfassung |
| While applying a sigma-point Kalman filter (SPKF) to a high-dimensional system
such as the oceanic general circulation model (OGCM), a major challenge is to reduce
its heavy burden of storage memory and costly computation. In this study, we propose
a new scheme for SPKF to address these issues. First, a reduced rank SPKF was introduced
on the high-dimensional model state space using the truncated single value
decomposition (TSVD) method (T-SPKF). Second, the relationship of SVDs between
the model state space and a low-dimensional ensemble space is used to construct sigma
points on the ensemble space (ET-SPKF). As such, this new scheme greatly reduces
the demand of memory storage and computational cost and makes the SPKF method
applicable to high-dimensional systems. Two numerical models are used to test and
validate the ET-SPKF algorithm. The first model is the 40-variable Lorenz model,
which has been a test bed of new assimilation algorithms. The second model is a realistic
OGCM for the assimilation of actual observations, including Argo and in situ
observations over the Pacific Ocean. The experiments show that ET-SPKF is computationally
feasible for high-dimensional systems and capable of precise analyses. In
particular, for realistic oceanic assimilations, the ET-SPKF algorithm can significantly
improve oceanic analysis and improve ENSO prediction. A comparison between the
ET-SPKF algorithm and EnKF (ensemble Kalman filter) is also tribally conducted
using the OGCM and actual observations. |
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