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A scalable model-independent iterative data assimilation tool for sequential and batch estimation of high dimensional model parameters and states

Ayman H. Alzraiee·Jeremy T. White·Matthew Knowling·Randall J. Hunt·Michael N. Fienen
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Abstract

Ensemble-based data assimilation (DA) methods have displayed strong potential to improve model state and parameter estimation across several disciplines due to their computational efficiency, scalability, and ability to estimate uncertainty in the dynamic states and the parameters. However, a barrier to adoption of ensemble DA methods remains. Namely, there is currently a lack of available tools that enable efficient and scalable DA in a non-intrusive fashion and that support implementation flexibility. This paper presents an open-source software tool (PESTPP-DA) that implements a range of data assimilation methods—Ensemble Kalman filter, Ensemble Kalman Smoother and Ensemble Smoother—using the widely known PEST model-interface protocols, to interact with any model. Two iterative solutions can be used for nonlinear and/or non-Gaussian assimilation problems. To demonstrate the broad range of PESTPP-DA applications, two synthetic case studies are presented: (1) the Lorenz model and (2) a groundwater pumping test in the presence of a non-Gaussian hydraulic conductivity field.

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Ayman H. Alzraiee, Jeremy T. White, Matthew Knowling, Randall J. Hunt, Michael N. Fienen. 2022. A scalable model-independent iterative data assimilation tool for sequential and batch estimation of high dimensional model parameters and states. https://doi.org/10.1016/j.envsoft.2021.105284

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