EAT v0.9.6: a 1D testbed for physical-biogeochemical data assimilation in natural waters

Bruggeman, J, Bolding, K, Nerger, L, Teruzzi, A, Spada, S, Skákala, J and Ciavatta, S 2024 EAT v0.9.6: a 1D testbed for physical-biogeochemical data assimilation in natural waters. https://doi.org/10.5194/gmd-2023-238 (In Press)

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Official URL: http://dx.doi.org/10.5194/gmd-2023-238

Abstract/Summary

Data assimilation (DA) in marine and freshwater systems combines numerical models and observations to deliver the best possible characterisation of a water body’s physical and biogeochemical state. This underpins the widely used 3D ocean state reanalyses and forecasts produced operationally by e.g. the Copernicus Marine Service. The use of DA in natural waters is an active field of research, but testing new developments in realistic setting can be challenging, as operational DA systems are demanding in terms of computational resources and technical skill. There is a need for testbeds that sufficiently realistic but also efficient to run and easy to operate. Here, we present the Ensemble and Assimilation Tool (EAT): a flexible and extensible software package that enables data assimilation of physical and biogeochemical variables in a one-dimensional water column. EAT builds on established open-source components for hydrodynamics (GOTM), biogeochemistry (FABM) and data assimilation (PDAF). It is easy to install and operate, and flexible through support for user-written plugins. EAT is well suited to explore and advance the state-of-the-art in DA in natural waters thanks to its support for (1) strongly and weakly coupled data assimilation, (2) observations describing any prognostic and diagnostic element of the physical-biogeochemical model, and (3) estimation of biogeochemical parameters. Its range of capabilities is demonstrated with three applications: ensemble-based coupled physical-biogeochemical assimilation, the use of variational methods (3D-Var) to assimilate sea surface chlorophyll, and the estimation of biogeochemical parameters.

Item Type: Publication - Article
Divisions: Plymouth Marine Laboratory > National Capability categories > National Capability Modelling
Plymouth Marine Laboratory > National Capability categories > National Centre for Earth Observation
Plymouth Marine Laboratory > Science Areas > Marine System Modelling
Depositing User: S Hawkins
Date made live: 14 May 2024 11:39
Last Modified: 14 May 2024 11:39
URI: https://plymsea.ac.uk/id/eprint/10210

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