Inflation method for ensemble Kalman filter in soil hydrology
The ensemble Kalman filter (EnKF) is a popular data assimilation method in soil hydrology. In this context, it is used to estimate states and parameters simultaneously. Due to unrepresented model errors and a limited ensemble size, state and parameter uncertainties can become too small during assimi...
Gespeichert in:
| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
21 Sep 2018
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| In: |
Hydrology and earth system sciences
Year: 2018, Jahrgang: 22, Heft: 9, Pages: 4921-4934 |
| ISSN: | 1607-7938 |
| DOI: | https://doi.org/10.5194/hess-22-4921-2018 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/https://doi.org/10.5194/hess-22-4921-2018 Verlag, lizenzpflichtig, Volltext: https://www.hydrol-earth-syst-sci.net/22/4921/2018/ 
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| Verfasserangaben: | Hannes H. Bauser, Daniel Berg, Ole Klein, and Kurt Roth |
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| 520 | |a The ensemble Kalman filter (EnKF) is a popular data assimilation method in soil hydrology. In this context, it is used to estimate states and parameters simultaneously. Due to unrepresented model errors and a limited ensemble size, state and parameter uncertainties can become too small during assimilation. Inflation methods are capable of increasing state uncertainties, but typically struggle with soil hydrologic applications. We propose a multiplicative inflation method specifically designed for the needs in soil hydrology. It employs a Kalman filter within the EnKF to estimate inflation factors based on the difference between measurements and mean forecast state within the EnKF. We demonstrate its capabilities on a small soil hydrologic test case. The method is capable of adjusting inflation factors to spatiotemporally varying model errors. It successfully transfers the inflation to parameters in the augmented state, which leads to an improved estimation. | ||
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