Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 25, issue 6
Articles | Volume 25, issue 6
https://doi.org/10.5194/hess-25-3319-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-3319-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 6
Technical note
 | 
16 Jun 2021
Technical note |  | 16 Jun 2021

Technical Note: Sequential ensemble data assimilation in convergent and divergent systems

Hannes Helmut Bauser, Daniel Berg, and Kurt Roth

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Cited articles

Aksoy, A., Zhang, F., and Nielsen-Gammon, J. W.: Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model, Mon. Weather Rev., 134, 2951–2970, https://doi.org/10.1175/MWR3224.1, 2006. 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
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, b
Berg, D.: Particle filters for nonlinear data assimilation, PhD thesis, Ruperto-Carola University Heidelberg, Heidelberg, Germany, 2018. a
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Short summary
Data assimilation methods are used throughout the geosciences to combine information from uncertain models and uncertain measurement data. In this study, we distinguish between the characteristics of geophysical systems, i.e., divergent systems (initially nearby states will drift apart) and convergent systems (initially nearby states will coalesce), and demonstrate the implications for sequential ensemble data assimilation methods, which require a sufficient divergent component.
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