Journal metrics

Journal metrics

  • IF value: 4.256 IF 4.256
  • IF 5-year value: 4.819 IF 5-year 4.819
  • CiteScore value: 4.10 CiteScore 4.10
  • SNIP value: 1.412 SNIP 1.412
  • SJR value: 2.023 SJR 2.023
  • IPP value: 3.97 IPP 3.97
  • h5-index value: 58 h5-index 58
  • Scimago H index value: 99 Scimago H index 99
Discussion papers | Copyright
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

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 Daniel Berg et al.
  • 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.

Download & links
Daniel Berg et al.
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for Authors/Editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Daniel Berg et al.
Daniel Berg et al.
Viewed
Total article views: 316 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
234 76 6 316 12 8
  • HTML: 234
  • PDF: 76
  • XML: 6
  • Total: 316
  • BibTeX: 12
  • EndNote: 8
Views and downloads (calculated since 03 Apr 2018)
Cumulative views and downloads (calculated since 03 Apr 2018)
Viewed (geographical distribution)
Total article views: 316 (including HTML, PDF, and XML) Thereof 314 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited
Saved
No saved metrics found.
Discussed
No discussed metrics found.
Latest update: 19 Jul 2018
Publications Copernicus
Download
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...
Citation
Share