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- Title
Simultaneous estimation of model state variables and observation and forecast biases using a two-stage hybrid Kalman filter.
- Authors
Pauwels, V. R. N.; De Lannoy, G. J. M.; Hendricks Franssen, H.-J.; Vereecken, H.
- Abstract
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.
- Subjects
KALMAN filtering; HYDROLOGIC models; ESTIMATION theory; RUNOFF; THERMODYNAMIC state variables; SCIENTIFIC observation
- Publication
Hydrology & Earth System Sciences, 2013, Vol 17, Issue 9, p3499
- ISSN
1027-5606
- Publication type
Article
- DOI
10.5194/hess-17-3499-2013