Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2018-121
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
03 Apr 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).
Covariance resampling for particle filter – state and parameter estimation for soil hydrology
Daniel Berg1,2, Hannes H. Bauser1,2, and Kurt Roth1,3 1Institute of Environmental Physics (IUP), Heidelberg University
2HGS MathComp, Heidelberg University
3Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University
Abstract. Particle filters are becoming increasingly popular for state and parameter estimation in hydrology. One of their crucial parts is the resampling after the assimilation step. We introduce a resampling method that uses the full weighted covariance information calculated from the ensemble to generate new particles and effectively avoids filter degeneracy. The ensemble covariance contains information between observed and unobserved dimensions and is used to fill the gaps between them. The covariance resampling approximately conserves the first two statistical moments and partly maintains information of higher order moments in the retained ensemble. The effectiveness of this method is demonstrated with a synthetic case – an unsaturated soil consisting of two homogeneous layers – by assimilating time domain reflectometry (TDR)-like measurements. Using this approach we can estimate state and parameters for a rough initial guess with just 100 particles. The estimated states and parameters are tested with a free run after the assimilation, which is found to be in good agreement with the synthetic truth.
Citation: Berg, D., Bauser, H. H., and Roth, K.: Covariance resampling for particle filter – state and parameter estimation for soil hydrology, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-121, in review, 2018.
Daniel Berg et al.
Daniel Berg et al.
Daniel Berg et al.

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Short summary
Particle filters are becoming popular for state and parameter estimation in hydrology. The renewal of the ensemble (resampling) is crucial to prevent filter degeneration. We introduce a resampling method that uses the weighted covariance of the ensemble, which contains information between observed and unobserved dimensions and is used to fill the gaps between them. This allows us to estimate state and parameters for a rough initial guess in a synthetic hydrological case with just 100 particles.
Particle filters are becoming popular for state and parameter estimation in hydrology. The...
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