Development and evaluation of a high-resolution reanalysis of the East Australian Current region using the Regional Ocean Modelling System (ROMS 3.4) and Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) data assimilation
As with other Western Boundary Currents globally, the East Australian Current (EAC) is highly variable making it a challenge to model and predict. For the EAC region, we combine a high-resolution state-of-the-art numerical ocean model with a variety of traditional and newly available observations us...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2016-10-01
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| Series: | Geoscientific Model Development |
| Online Access: | http://www.geosci-model-dev.net/9/3779/2016/gmd-9-3779-2016.pdf |
| _version_ | 1818466136049582080 |
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| author | C. Kerry B. Powell M. Roughan P. Oke |
| author_facet | C. Kerry B. Powell M. Roughan P. Oke |
| author_sort | C. Kerry |
| collection | DOAJ |
| description | As with other Western Boundary Currents globally, the East Australian Current
(EAC) is highly variable making it a challenge to model and predict. For the
EAC region, we combine a high-resolution state-of-the-art numerical ocean
model with a variety of traditional and newly available observations using an
advanced variational data assimilation scheme. The numerical model is
configured using the Regional Ocean Modelling System (ROMS 3.4) and takes
boundary forcing from the BlueLink ReANalysis (BRAN3). For the data
assimilation, we use an Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) scheme, which uses the model dynamics to perturb the
initial conditions, atmospheric forcing, and boundary conditions, such that
the modelled ocean state better fits and is in balance with the observations.
This paper describes the data assimilative model configuration that achieves
a significant reduction of the difference between the modelled solution and
the observations to give a dynamically consistent “best estimate” of the
ocean state over a 2-year period. The reanalysis is shown to represent both
assimilated and non-assimilated observations well. It achieves mean
spatially averaged root mean squared (rms) residuals with the observations of 7.6 cm for sea surface height (SSH) and
0.4 °C for sea surface temperature (SST) over the assimilation period. The time-mean rms
residual for subsurface temperature measured by Argo floats is a maximum of
0.9 °C between water depths of 100 and 300 m and smaller throughout
the rest of the water column. Velocities at several offshore and continental
shelf moorings are well represented in the reanalysis with complex
correlations between 0.8 and 1 for all observations in the upper 500 m. Surface
radial velocities from a high-frequency radar array are assimilated and the
reanalysis provides surface velocity estimates with complex correlations with
observed velocities of 0.8–1 across the radar footprint. A comparison with
independent (non-assimilated) shipboard conductivity temperature depth (CTD) cast observations shows a marked
improvement in the representation of the subsurface ocean in the reanalysis,
with the rms residual in potential density reduced to about half of the
residual with the free-running model in the upper eddy-influenced part of the
water column. This shows that information is successfully propagated from
observed variables to unobserved regions as the assimilation system uses the
model dynamics to adjust the model state estimate. This is the first study to
generate a reanalysis of the region at such a high resolution, making use of
an unprecedented observational data set and using an assimilation method that
uses the time-evolving model physics to adjust the model in a dynamically
consistent way. As such, the reanalysis potentially represents a marked
improvement in our ability to capture important circulation dynamics in the
EAC. The reanalysis is being used to study EAC dynamics, observation impact
in state-estimation, and as forcing for a variety of downscaling studies. |
| first_indexed | 2024-04-13T20:54:25Z |
| format | Article |
| id | doaj.art-488bf6477ec54736ae5da04c8abe20a4 |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-04-13T20:54:25Z |
| publishDate | 2016-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-488bf6477ec54736ae5da04c8abe20a42022-12-22T02:30:22ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032016-10-019103779380110.5194/gmd-9-3779-2016Development and evaluation of a high-resolution reanalysis of the East Australian Current region using the Regional Ocean Modelling System (ROMS 3.4) and Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) data assimilationC. Kerry0B. Powell1M. Roughan2P. Oke3University of New South Wales, Sydney, NSW, 2052, AustraliaUniversity of Hawai'i at Manoa, Honolulu, Hawaii, USAUniversity of New South Wales, Sydney, NSW, 2052, AustraliaCSIRO Marine and Atmospheric Research, Hobart, AustraliaAs with other Western Boundary Currents globally, the East Australian Current (EAC) is highly variable making it a challenge to model and predict. For the EAC region, we combine a high-resolution state-of-the-art numerical ocean model with a variety of traditional and newly available observations using an advanced variational data assimilation scheme. The numerical model is configured using the Regional Ocean Modelling System (ROMS 3.4) and takes boundary forcing from the BlueLink ReANalysis (BRAN3). For the data assimilation, we use an Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) scheme, which uses the model dynamics to perturb the initial conditions, atmospheric forcing, and boundary conditions, such that the modelled ocean state better fits and is in balance with the observations. This paper describes the data assimilative model configuration that achieves a significant reduction of the difference between the modelled solution and the observations to give a dynamically consistent “best estimate” of the ocean state over a 2-year period. The reanalysis is shown to represent both assimilated and non-assimilated observations well. It achieves mean spatially averaged root mean squared (rms) residuals with the observations of 7.6 cm for sea surface height (SSH) and 0.4 °C for sea surface temperature (SST) over the assimilation period. The time-mean rms residual for subsurface temperature measured by Argo floats is a maximum of 0.9 °C between water depths of 100 and 300 m and smaller throughout the rest of the water column. Velocities at several offshore and continental shelf moorings are well represented in the reanalysis with complex correlations between 0.8 and 1 for all observations in the upper 500 m. Surface radial velocities from a high-frequency radar array are assimilated and the reanalysis provides surface velocity estimates with complex correlations with observed velocities of 0.8–1 across the radar footprint. A comparison with independent (non-assimilated) shipboard conductivity temperature depth (CTD) cast observations shows a marked improvement in the representation of the subsurface ocean in the reanalysis, with the rms residual in potential density reduced to about half of the residual with the free-running model in the upper eddy-influenced part of the water column. This shows that information is successfully propagated from observed variables to unobserved regions as the assimilation system uses the model dynamics to adjust the model state estimate. This is the first study to generate a reanalysis of the region at such a high resolution, making use of an unprecedented observational data set and using an assimilation method that uses the time-evolving model physics to adjust the model in a dynamically consistent way. As such, the reanalysis potentially represents a marked improvement in our ability to capture important circulation dynamics in the EAC. The reanalysis is being used to study EAC dynamics, observation impact in state-estimation, and as forcing for a variety of downscaling studies.http://www.geosci-model-dev.net/9/3779/2016/gmd-9-3779-2016.pdf |
| spellingShingle | C. Kerry B. Powell M. Roughan P. Oke Development and evaluation of a high-resolution reanalysis of the East Australian Current region using the Regional Ocean Modelling System (ROMS 3.4) and Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) data assimilation Geoscientific Model Development |
| title | Development and evaluation of a high-resolution reanalysis of the East Australian Current region using the Regional Ocean Modelling System (ROMS 3.4) and Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) data assimilation |
| title_full | Development and evaluation of a high-resolution reanalysis of the East Australian Current region using the Regional Ocean Modelling System (ROMS 3.4) and Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) data assimilation |
| title_fullStr | Development and evaluation of a high-resolution reanalysis of the East Australian Current region using the Regional Ocean Modelling System (ROMS 3.4) and Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) data assimilation |
| title_full_unstemmed | Development and evaluation of a high-resolution reanalysis of the East Australian Current region using the Regional Ocean Modelling System (ROMS 3.4) and Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) data assimilation |
| title_short | Development and evaluation of a high-resolution reanalysis of the East Australian Current region using the Regional Ocean Modelling System (ROMS 3.4) and Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) data assimilation |
| title_sort | development and evaluation of a high resolution reanalysis of the east australian current region using the regional ocean modelling system roms 3 4 and incremental strong constraint 4 dimensional variational is4d var data assimilation |
| url | http://www.geosci-model-dev.net/9/3779/2016/gmd-9-3779-2016.pdf |
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