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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Feb 2019
Research article |
| 28 Feb 2019
| 28 Feb 2019
Covariance resampling for particle filter – state and parameter estimation for soil hydrology
Daniel Berg
CORRESPONDING AUTHOR
Institute of Environmental Physics (IUP), Heidelberg University,
Heidelberg, Germany
HGS MathComp, Heidelberg University, Heidelberg, Germany
Hannes H. Bauser
Institute of Environmental Physics (IUP), Heidelberg University,
Heidelberg, Germany
HGS MathComp, Heidelberg University, Heidelberg, Germany
Kurt Roth
Institute of Environmental Physics (IUP), Heidelberg University,
Heidelberg, Germany
Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
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13 citations as recorded by crossref.
- An ensemble square root filter for the joint assimilation of surface soil moisture and leaf area index within the Land Data Assimilation System LDAS-Monde: application over the Euro-Mediterranean region B. Bonan et al. 10.5194/hess-24-325-2020
- Enhancing state and parameter estimations of a dynamic crop model by a recombination particle filter Y. Orlova & R. Linker 10.1016/j.compag.2023.108355
- Technical Note: Sequential ensemble data assimilation in convergent and divergent systems H. Bauser et al. 10.5194/hess-25-3319-2021
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- Constraining Flood Forecasting Uncertainties through Streamflow Data Assimilation in the Tropical Andes of Peru: Case of the Vilcanota River Basin H. Llauca et al. 10.3390/w15223944
- Genetic Operator-Based Particle Filter Combined with Markov Chain Monte Carlo for Data Assimilation in a Crop Growth Model A. Jamal & R. Linker 10.3390/agriculture10120606
- An Intelligent Particle Filter With Adaptive M-H Resampling for Liquid-Level Estimation During Silicon Crystal Growth X. Zhang et al. 10.1109/TIM.2020.3026760
- Data Assimilation with Sensitivity-Based Particle Filter: A Simulation Study with Aquacrop Y. Orlova & R. Linker 10.2139/ssrn.4165843
- Covariance-Based Selection of Parameters for Particle Filter Data Assimilation in Soil Hydrology A. Jamal & R. Linker 10.3390/w14223606
- 3D–3D Rigid Registration of Echocardiographic Images With Significant Overlap Using Particle Filter T. Uruththirakodeeswaran et al. 10.1109/ACCESS.2024.3418936
- HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model Q. Tang et al. 10.5194/gmd-17-3559-2024
- Impact of leaf area index assimilation and gauge-corrected precipitation on land surface variables in LDAS-Monde: a case study over China E. Liu et al. 10.1016/j.jhydrol.2025.133304
1 citations as recorded by crossref.
Latest update: 23 Apr 2025
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.
Particle filters are becoming popular for state and parameter estimations in hydrology. The...
