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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Cited articles

Abbaszadeh, P., Moradkhani, H., and Yan, H.: Enhancing hydrologic data assimilation by evolutionary particle filter and Markov chain Monte Carlo, Adv. Water Resour., 111, 192–204, https://doi.org/10.1016/j.advwatres.2017.11.011, 2018. a
Anderson, J. L.: An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus A, 59, 210–224, https://doi.org/10.1111/j.1600-0870.2006.00216.x, 2007. a
Anderson, J. L. and Anderson, S. L.: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev., 127, 2741–2758, https://doi.org/10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2, 1999. a
Bauser, H. H., Jaumann, S., Berg, D., and Roth, K.: EnKF with closed-eye period – towards a consistent aggregation of information in soil hydrology, Hydrol. Earth Syst. Sci., 20, 4999–5014, https://doi.org/10.5194/hess-20-4999-2016, 2016. a, b
Bauser, H. H., Berg, D., Klein, O., and Roth, K.: Inflation method for ensemble Kalman filter in soil hydrology, Hydrol. Earth Syst. Sci., 22, 4921–4934, https://doi.org/10.5194/hess-22-4921-2018, 2018. a
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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.