Articles | Volume 23, issue 2
Hydrol. Earth Syst. Sci., 23, 1163–1178, 2019
https://doi.org/10.5194/hess-23-1163-2019
Hydrol. Earth Syst. Sci., 23, 1163–1178, 2019
https://doi.org/10.5194/hess-23-1163-2019

Research article 28 Feb 2019

Research article | 28 Feb 2019

Covariance resampling for particle filter – state and parameter estimation for soil hydrology

Daniel Berg et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (16 Aug 2018) by Harrie-Jan Hendricks Franssen
AR by Daniel Berg on behalf of the Authors (27 Sep 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (28 Sep 2018) by Harrie-Jan Hendricks Franssen
RR by Damiano Pasetto (23 Oct 2018)
RR by Jasper Vrugt (14 Nov 2018)
ED: Publish subject to revisions (further review by editor and referees) (22 Nov 2018) by Harrie-Jan Hendricks Franssen
AR by Daniel Berg on behalf of the Authors (31 Dec 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (06 Jan 2019) by Harrie-Jan Hendricks Franssen
RR by Damiano Pasetto (30 Jan 2019)
ED: Publish as is (10 Feb 2019) by Harrie-Jan Hendricks Franssen
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Short summary
Particle filters are becoming popular for state and parameter estimations in hydrology. The renewal of the ensemble (resampling) is crucial in preventing filter degeneration. We introduce a resampling method that uses the weighted covariance of the ensemble, which contains information between observed and unobserved dimensions, to generate new ensemble members. This allows us to estimate the state and parameters for a rough initial guess in a synthetic hydrological case with just 100 particles.