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

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 init...

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Bibliographic Details
Main Authors: Bauser, Hannes (Author) , Berg, Daniel (Author) , Roth, Kurt (Author)
Format: Article (Journal)
Language:English
Published: 16 June 2021
In: Hydrology and earth system sciences
Year: 2021, Volume: 25, Issue: 6, Pages: 3319-3329
ISSN:1607-7938
DOI:10.5194/hess-25-3319-2021
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.5194/hess-25-3319-2021
Verlag, lizenzpflichtig, Volltext: https://hess.copernicus.org/articles/25/3319/2021/
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Author Notes:Hannes Helmut Bauser, Daniel Berg, and Kurt Roth
Description
Summary: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.
Physical Description:Online Resource
ISSN:1607-7938
DOI:10.5194/hess-25-3319-2021