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

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.

Automated summary

This study investigates the characteristics of divergent and convergent geophysical systems in relation to data assimilation methods, showing that a sufficient divergent component is necessary for proper application of sequential ensemble data assimilation methods. The transfer of methods from divergent to convergent systems is challenging, highlighting the importance of adequately representing model errors and incorporating parameter uncertainties in ensemble data assimilation for convergent systems.