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| Titel |
Simultaneous estimation of model state variables and observation and forecast biases using a two-stage hybrid Kalman filter |
| VerfasserIn |
V. R. N. Pauwels, G. J. M. Lannoy, H.-J. Hendricks Franssen, H. Vereecken |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 17, no. 9 ; Nr. 17, no. 9 (2013-09-13), S.3499-3521 |
| Datensatznummer |
250085929
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| Publikation (Nr.) |
 copernicus.org/hess-17-3499-2013.pdf |
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| Zusammenfassung |
| In this paper, we present a two-stage hybrid Kalman filter to estimate both
observation and forecast bias in hydrologic models, in addition to state
variables. The biases are estimated using the discrete Kalman filter, and the
state variables using the ensemble Kalman filter. A key issue in this
multi-component assimilation scheme is the exact partitioning of the
difference between observation and forecasts into state, forecast bias and
observation bias updates. Here, the error covariances of the forecast bias
and the unbiased states are calculated as constant fractions of the biased
state error covariance, and the observation bias error covariance is a
function of the observation prediction error covariance. In a series of
synthetic experiments, focusing on the assimilation of discharge into a
rainfall-runoff model, it is shown that both static and dynamic observation
and forecast biases can be successfully estimated. The results indicate a
strong improvement in the estimation of the state variables and resulting
discharge as opposed to the use of a bias-unaware ensemble Kalman filter.
Furthermore, minimal code modification in existing data assimilation software
is needed to implement the method. The results suggest that a better
performance of data assimilation methods should be possible if both forecast
and observation biases are taken into account. |
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