Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 6
Articles | Volume 17, issue 6
https://doi.org/10.5194/gmd-17-2359-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-2359-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 6
Model description paper
 | 
22 Mar 2024
Model description paper |  | 22 Mar 2024

Comparison of 4-dimensional variational and ensemble optimal interpolation data assimilation systems using a Regional Ocean Modeling System (v3.4) configuration of the eddy-dominated East Australian Current system

Colette Gabrielle Kerry, Moninya Roughan, Shane Keating, David Gwyther, Gary Brassington, Adil Siripatana, and Joao Marcos A. C. Souza

Data sets

A high-resolution reanalysis of the East Australian Current System assimilating an unprecedented observational data set using 4D-Var data assimilation over a two-year period (2012-2013). Version 2017. Colette Kerry et al. https://doi.org/10.26190/5ebe1f389dd87

South East Australian Coastal Ocean Forecast System (SEA-COFS) Colette Kerry and Moninya Roughan https://doi.org/10.5281/zenodo.8294716

Argo float data and metadata from Global Data Assembly Centre (Argo GDAC) G. Notarstefano https://doi.org/10.17882/42182

South East Australian Coastal Ocean Forecast System (SEA-COFS) (1.0) M. Roughan and C. Kerry https://doi.org/10.5281/zenodo.8294716

A high-resolution, 22-year, free-running, hydrodynamci simulation of the EAC System using the ROMS C. Kerry and M. Roughan https://doi.org/10.26190/5e683944e1369

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Short summary
Ocean forecasting relies on the combination of numerical models and ocean observations through data assimilation (DA). Here we assess the performance of two DA systems in a dynamic western boundary current, the East Australian Current, across a common modelling and observational framework. We show that the more advanced, time-dependent method outperforms the time-independent method for forecast horizons of 5 d. This advocates the use of advanced methods for highly variable oceanic regions.
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