Exploring a hybrid ensemble–variational data assimilation technique (4DEnVar) with a simple ecosystem carbon model

Douglas, N. ORCID: https://orcid.org/0000-0002-3404-8761, Quaife, T. ORCID: https://orcid.org/0000-0001-6896-4613 and Bannister, R. ORCID: https://orcid.org/0000-0002-6846-8297 (2025) Exploring a hybrid ensemble–variational data assimilation technique (4DEnVar) with a simple ecosystem carbon model. Environmental Modelling and Software, 186. 106361. ISSN 1873-6726 doi: 10.1016/j.envsoft.2025.106361

Abstract/Summary

The study presented here evaluates the ability of the 4DEnVar data assimilation technique to estimate the parameters from synthetically generated observations from a simple carbon model. The method is particularly attractive in its speed and ease of use, and its avoidance in construction of adjoint or tangent linear model code. Additionally, the assimilation analysis step can be performed independently of ensemble generation; there is no need to integrate the 4DEnVar code with that of the underlying model, assuming parameters are static in time. The 4DEnVar method is capable of closely estimating the model parameters with increased certainty given that the ensemble produces a sufficient number of trajectories exhibiting behaviour seen in the observations. We find that the root mean squared error between trajectories and observations is significantly reduced when compared with the prior — in one case a 96% and 99% reduction in the biomass and soil pools respectively.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/121204
Identification Number/DOI	10.1016/j.envsoft.2025.106361
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Elsevier
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