Articles | Volume 22, issue 9
https://doi.org/10.5194/hess-22-4921-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-4921-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Sep 2018
Research article |
| 21 Sep 2018
| 21 Sep 2018
Inflation method for ensemble Kalman filter in soil hydrology
Hannes H. Bauser
CORRESPONDING AUTHOR
Institute of Environmental Physics (IUP), Heidelberg University, Heidelberg, Germany
HGS MathComp, Heidelberg University, Heidelberg, Germany
Daniel Berg
Institute of Environmental Physics (IUP), Heidelberg University, Heidelberg, Germany
HGS MathComp, Heidelberg University, Heidelberg, Germany
Ole Klein
Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
Kurt Roth
Institute of Environmental Physics (IUP), Heidelberg University, Heidelberg, Germany
Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
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Short summary
Data assimilation methods like the ensemble Kalman filter (EnKF) can combine models and measurements to estimate states and parameters, but require a proper representation of uncertainties. In soil hydrology, model errors typically vary rapidly in space and time, which is difficult to represent. Inflation methods can account for unrepresented model errors. To improve estimations in soil hydrology, we designed a method that can adjust the inflation of states and parameters to fast varying errors.
Data assimilation methods like the ensemble Kalman filter (EnKF) can combine models and...
