Covariance resampling for particle filter – state and parameter estimation for soil hydrology
<p>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 g...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2019-02-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://www.hydrol-earth-syst-sci.net/23/1163/2019/hess-23-1163-2019.pdf |
| _version_ | 1818343000189698048 |
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| author | D. Berg D. Berg H. H. Bauser H. H. Bauser K. Roth K. Roth |
| author_facet | D. Berg D. Berg H. H. Bauser H. H. Bauser K. Roth K. Roth |
| author_sort | D. Berg |
| collection | DOAJ |
| description | <p>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 avoid 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 the
structure of the estimated distribution 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-like (TDR-like) measurements. Using this approach we can estimate state and
parameters for a rough initial guess with 100 particles. The estimated states
and parameters are tested with a forecast after the assimilation, which is
found to be in good agreement with the synthetic truth.</p> |
| first_indexed | 2024-12-13T16:23:37Z |
| format | Article |
| id | doaj.art-6c7c6baf1014487486afd317f7898863 |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-12-13T16:23:37Z |
| publishDate | 2019-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-6c7c6baf1014487486afd317f78988632022-12-21T23:38:40ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382019-02-01231163117810.5194/hess-23-1163-2019Covariance resampling for particle filter – state and parameter estimation for soil hydrologyD. Berg0D. Berg1H. H. Bauser2H. H. Bauser3K. Roth4K. Roth5Institute of Environmental Physics (IUP), Heidelberg University, Heidelberg, GermanyHGS MathComp, Heidelberg University, Heidelberg, GermanyInstitute of Environmental Physics (IUP), Heidelberg University, Heidelberg, GermanyHGS MathComp, Heidelberg University, Heidelberg, GermanyInstitute of Environmental Physics (IUP), Heidelberg University, Heidelberg, GermanyInterdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany<p>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 avoid 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 the structure of the estimated distribution 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-like (TDR-like) measurements. Using this approach we can estimate state and parameters for a rough initial guess with 100 particles. The estimated states and parameters are tested with a forecast after the assimilation, which is found to be in good agreement with the synthetic truth.</p>https://www.hydrol-earth-syst-sci.net/23/1163/2019/hess-23-1163-2019.pdf |
| spellingShingle | D. Berg D. Berg H. H. Bauser H. H. Bauser K. Roth K. Roth Covariance resampling for particle filter – state and parameter estimation for soil hydrology Hydrology and Earth System Sciences |
| title | Covariance resampling for particle filter – state and parameter estimation for soil hydrology |
| title_full | Covariance resampling for particle filter – state and parameter estimation for soil hydrology |
| title_fullStr | Covariance resampling for particle filter – state and parameter estimation for soil hydrology |
| title_full_unstemmed | Covariance resampling for particle filter – state and parameter estimation for soil hydrology |
| title_short | Covariance resampling for particle filter – state and parameter estimation for soil hydrology |
| title_sort | covariance resampling for particle filter state and parameter estimation for soil hydrology |
| url | https://www.hydrol-earth-syst-sci.net/23/1163/2019/hess-23-1163-2019.pdf |
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