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

Berg, Daniel; Bauser, Hannes H.; Roth, Kurt

doi:https://doi.org/10.5194/hess-23-1163-2019

Articles | Volume 23, issue 2

https://doi.org/10.5194/hess-23-1163-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-23-1163-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 2

Research article

|

28 Feb 2019

Research article |

| 28 Feb 2019

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

Daniel Berg, Hannes H. Bauser, and Kurt Roth

Supplement

https://doi.org/10.5194/hess-23-1163-2019-supplement

Data sets

Covariance resampling for particle filter -- state and parameter estimation for soil hydrology [dataset] D. Berg, H. H. Bauser, and K. Roth https://doi.org/10.11588/data/MFU6EV

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.