Abstract
Abstract. Data assimilation methods are used throughout the geosciences to combine information from uncertain models and uncertain measurement data. However, the characteristics of geophysical systems differ and may be distinguished between divergent and convergent systems. In divergent systems initially nearby states will drift apart, while they will coalesce in convergent systems. This difference has implications for the application of sequential ensemble data assimilation methods. This study explores these implications on two exemplary systems, i.e., the divergent Lorenz 96 model and the convergent description of soil water movement by the Richards equation. The results show that sequential ensemble data assimilation methods require a sufficient divergent component. This makes the transfer of the methods from divergent to convergent systems challenging. We demonstrate, through a set of case studies, that it is imperative to represent model errors adequately and incorporate parameter uncertainties in ensemble data assimilation in convergent systems.
Funder
Deutsche Forschungsgemeinschaft
Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences, University of Heidelberg
Subject
General Earth and Planetary Sciences,General Engineering,General Environmental Science
Reference46 articles.
1. 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
2. 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
3. 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
4. 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
5. Berg, D.: Particle filters for nonlinear data assimilation, PhD thesis, Ruperto-Carola University Heidelberg, Heidelberg, Germany, 2018. a
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献