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.
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.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
22 Mar 2024
Model description paper |
| 22 Mar 2024
| 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
CORRESPONDING AUTHOR
Coastal and Regional Oceanography Lab, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, 2052, Australia
Moninya Roughan
Coastal and Regional Oceanography Lab, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, 2052, Australia
Shane Keating
School of Mathematics and Statistics, UNSW Sydney, Sydney, NSW, 2052, Australia
David Gwyther
Coastal and Regional Oceanography Lab, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, 2052, Australia
School of Earth and Environmental Sciences, University of Queensland, Brisbane, Australia
Gary Brassington
The Centre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne, Australia
Adil Siripatana
Coastal and Regional Oceanography Lab, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, 2052, Australia
AI and Computer Engineering, CMKL University, Bangkok, Thailand
Joao Marcos A. C. Souza
Meteorological Service of New Zealand, MetOcean Division, Raglan, New Zealand
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Geosci. Model Dev., 16, 157–178, https://doi.org/10.5194/gmd-16-157-2023, https://doi.org/10.5194/gmd-16-157-2023, 2023
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Ocean eddies are important for weather, climate, biology, navigation, and search and rescue. Since eddies change rapidly, models that incorporate or assimilate observations are required to produce accurate eddy timings and locations, yet the model accuracy is rarely assessed below the surface. We use a unique type of ocean model experiment to assess three-dimensional eddy structure in the East Australian Current and explore two pathways in which this subsurface structure is being degraded.
David E. Gwyther, Colette Kerry, Moninya Roughan, and Shane R. Keating
Geosci. Model Dev., 15, 6541–6565, https://doi.org/10.5194/gmd-15-6541-2022, https://doi.org/10.5194/gmd-15-6541-2022, 2022
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The ocean current flowing along the southeastern coast of Australia is called the East Australian Current (EAC). Using computer simulations, we tested how surface and subsurface observations might improve models of the EAC. Subsurface observations are particularly important for improving simulations, and if made in the correct location and time, can have impact 600 km upstream. The stability of the current affects model estimates could be capitalized upon in future observing strategies.
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Matthew A. Chamberlain, Peter R. Oke, Russell A. S. Fiedler, Helen M. Beggs, Gary B. Brassington, and Prasanth Divakaran
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BRAN2020 is a dynamical reconstruction of the ocean, combining observations with a high-resolution global ocean model. BRAN2020 currently spans January 1993 to December 2019, assimilating in situ temperature and salinity, as well as satellite-based sea level and sea surface temperature. A new multiscale approach to data assimilation constrains the broad-scale ocean properties and turbulent mesoscale dynamics in two steps, showing closer agreement to observations than all previous versions.
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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.
Ocean forecasting relies on the combination of numerical models and ocean observations through...
