Predictability of Mesoscale Variability in the East Australian Current Given Strong-Constraint Data Assimilation

2012 ◽  
Vol 42 (9) ◽  
pp. 1402-1420 ◽  
Author(s):  
Javier Zavala-Garay ◽  
J. L. Wilkin ◽  
H. G. Arango
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Empirical Relationship ◽  
Ocean Modeling ◽  
Sea Surface ◽  
Initial Time ◽  
East Australian Current ◽  
Mesoscale Variability ◽  
Regional Ocean Modeling System ◽  
Strong Constraint

Abstract One of the many applications of data assimilation is the estimation of adequate initial conditions for model forecasts. In this work, the authors evaluate to what extent the incremental, strong-constraint, four-dimensional variational data assimilation (IS4DVAR) can improve prediction of mesoscale variability in the East Australian Current (EAC) using the Regional Ocean Modeling System (ROMS). The observations considered in the assimilation experiments are daily composites of satellite sea surface temperature (SST), 7-day average reanalysis of satellite altimeter sea level anomalies, and subsurface temperature profiles from high-resolution expendable bathythermograph (XBT). Considering all available observations for years 2001 and 2002, ROMS forecast initial conditions are generated every week by assimilating the available observations from the 7 days prior to the forecast initial time. It is shown that assimilation of surface information only [SST and sea surface height (SSH)] results in poor estimates of the true subsurface ocean state (as depicted by the XBTs) and therefore poor forecast skill of subsurface conditions. Including the XBTs in the assimilation experiments improves the ocean state estimation in the vicinity of the XBT transects. By introducing subsurface pseudo-observations (which are called synthetic CTD) based on an empirical relationship between satellite surface observations and subsurface variability, the authors find a significant improvement in ocean state estimates that leads to skillful forecasts for up to 2 weeks in the domain considered.

scholarly journals 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

2016 ◽  
Vol 9 (10) ◽  
pp. 3779-3801 ◽  
Author(s):  
Colette Kerry ◽  
Brian Powell ◽  
Moninya Roughan ◽  
Peter Oke
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Water Column ◽  
Marked Improvement ◽  
Sea Surface ◽  
East Australian Current ◽  
Strong Constraint ◽  
Ocean Modelling ◽  
Model Dynamics ◽  
Modelling System

Abstract. 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.

Weak and strong constraint data assimilation in the inverse Regional Ocean Modeling System (ROMS): Development and application for a baroclinic coastal upwelling system

2007 ◽  
Vol 16 (3-4) ◽  
pp. 160-187 ◽  
Author(s):  
Emanuele Di Lorenzo ◽  
Andrew M. Moore ◽  
Hernan G. Arango ◽  
Bruce D. Cornuelle ◽  
Arthur J. Miller ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Coastal Upwelling ◽  
Ocean Modeling ◽  
Regional Ocean Modeling System ◽  
Strong Constraint ◽  
Upwelling System

scholarly journals Reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System, an implementation of the Regional Ocean Modeling System v3.6

2019 ◽  
Vol 12 (1) ◽  
pp. 195-213
Author(s):  
Dale Partridge ◽  
Tobias Friedrich ◽  
Brian S. Powell
Keyword(s):  
Initial Conditions ◽  
Hawaiian Island ◽  
The State ◽  
Ocean Modeling ◽  
Surface Velocity ◽  
Regional Ocean Modeling System ◽  
Forecast System ◽  
Strong Constraint ◽  
State Estimate ◽  
High Frequency Radar

Abstract. A 10-year reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System was produced using an incremental strong-constraint 4-D variational data assimilation with the Regional Ocean Modeling System (ROMS v3.6). Observations were assimilated from a range of sources: satellite-derived sea surface temperature (SST), salinity (SSS), and height anomalies (SSHAs); depth profiles of temperature and salinity from Argo floats, autonomous Seagliders, and shipboard conductivity–temperature–depth (CTD); and surface velocity measurements from high-frequency radar (HFR). The performance of the state estimate is examined against a forecast showing an improved representation of the observations, especially the realization of HFR surface currents. EOFs of the increments made during the assimilation to the initial conditions and atmospheric forcing components are computed, revealing the variables that are influential in producing the state-estimate solution and the spatial structure the increments form.

scholarly journals An Advanced Data Assimilation System for the Chesapeake Bay: Performance Evaluation

2012 ◽  
Vol 29 (10) ◽  
pp. 1542-1557 ◽  
Author(s):  
Matthew J. Hoffman ◽  
Takemasa Miyoshi ◽  
Thomas W. N. Haine ◽  
Kayo Ide ◽  
Christopher W. Brown ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Chesapeake Bay ◽  
Nonlinear Dynamical Systems ◽  
Initial Conditions ◽  
Real Data ◽  
Ocean Modeling ◽  
Forecast Errors ◽  
Regional Ocean Modeling System ◽  
Data Assimilation System ◽  
Assimilation System

Abstract An advanced data assimilation system, the local ensemble transform Kalman filter (LETKF), has been interfaced with a Regional Ocean Modeling System (ROMS) implementation on the Chesapeake Bay (ChesROMS) as a first step toward a reanalysis and improved forecast system for the Chesapeake Bay. The LETKF is among the most advanced data assimilation methods and is very effective for large, nonlinear dynamical systems with sparse data coverage. Errors in the Chesapeake Bay system are due more to errors in forcing than errors in initial conditions. To account for forcing errors, a forcing ensemble is used to drive the ensemble states for the year 2003. In the observing system simulation experiments (OSSEs) using the ChesROMS-LETKF system presented here, the filter converges quickly and greatly reduces the analysis and subsequent forecast errors in the temperature, salinity, and current fields in the presence of errors in wind forcing. Most of the improvement in temperature and currents comes from satellite sea surface temperature (SST), while in situ salinity profiles provide improvement to salinity. Corrections permeate through all vertical levels and some correction to stratification is seen in the analysis. In the upper Bay where the nature-run summer stratification is −0.2 salinity units per meter, stratification is improved from −0.01 per meter in the unassimilated model to −0.16 per meter in the assimilation. Improvements are seen in other parts of the Bay as well. The results from the OSSEs are promising for assimilating real data in the future.

scholarly journals Reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System, an implementation of the Regional Ocean Modeling System v3.6

2018 ◽  
Author(s):  
Dale Partridge ◽  
Brian S. Powell
Keyword(s):  
Initial Conditions ◽  
Hawaiian Island ◽  
The State ◽  
Ocean Modeling ◽  
Regional Ocean Modeling System ◽  
Forecast System ◽  
Strong Constraint ◽  
State Estimate ◽  
High Frequency Radar ◽  
Free Running

Abstract. A 10-year reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System was produced using an incremental strong constraint 4D-Variational data assimilation with the Regional Ocean Modeling System (ROMS). Observations were assimilated from a range of sources: satellite-derived sea surface temperature (SST), salinity (SSS), and height anomalies (SSHA); depth profiles of temperature and salinity from Argo floats, autonomous SeaGliders, shipboard conductivity-temperature-depth (CTDs); and surface HFR velocity measurements from high frequency radar (HFR). The performance of the state-estimate is examined against a free-running forecast showing an improved representation of the observations, especially the realization of HFR surface currents. EOFs of the increments made during the assimilation to the initial conditions and atmospheric forcing components are computed, revealing the variables that are influential in producing the state-estimate solution and the spatial structure the increments form.

scholarly journals Multi-Physics Ensemble versus Atmosphere–Ocean Coupled Model Simulations for a Tropical-Like Cyclone in the Mediterranean Sea

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 202 ◽  
Author(s):  
Antonio Ricchi ◽  
Mario Marcello Miglietta ◽  
Davide Bonaldo ◽  
Guido Cioni ◽  
Umberto Rizza ◽  
...  
Keyword(s):  
High Resolution ◽  
Mediterranean Sea ◽  
Computational Cost ◽  
Ocean Model ◽  
Surface Fluxes ◽  
Ocean Modeling ◽  
Sea Surface ◽  
Regional Ocean Modeling System ◽  
The Mediterranean ◽  
Physics Ensemble

Between 19 and 22 January 2014, a baroclinic wave moving eastward from the Atlantic Ocean generated a cut-off low over the Strait of Gibraltar and was responsible for the subsequent intensification of an extra-tropical cyclone. This system exhibited tropical-like features in the following stages of its life cycle and remained active for approximately 80 h, moving along the Mediterranean Sea from west to east, eventually reaching the Adriatic Sea. Two different modeling approaches, which are comparable in terms of computational cost, are analyzed here to represent the cyclone evolution. First, a multi-physics ensemble using different microphysics and turbulence parameterization schemes available in the WRF (weather research and forecasting) model is employed. Second, the COAWST (coupled ocean–atmosphere wave sediment transport modeling system) suite, including WRF as an atmospheric model, ROMS (regional ocean modeling system) as an ocean model, and SWAN (simulating waves in nearshore) as a wave model, is used. The advantage of using a coupled modeling system is evaluated taking into account air–sea interaction processes at growing levels of complexity. First, a high-resolution sea surface temperature (SST) field, updated every 6 h, is used to force a WRF model stand-alone atmospheric simulation. Later, a two-way atmosphere–ocean coupled configuration is employed using COAWST, where SST is updated using consistent sea surface fluxes in the atmospheric and ocean models. Results show that a 1D ocean model is able to reproduce the evolution of the cyclone rather well, given a high-resolution initial SST field produced by ROMS after a long spin-up time. Additionally, coupled simulations reproduce more accurate (less intense) sea surface heat fluxes and a cyclone track and intensity, compared with a multi-physics ensemble of standalone atmospheric simulations.

4DVAR data assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS)

2008 ◽  
Vol 23 (3-4) ◽  
pp. 130-145 ◽  
Author(s):  
B.S. Powell ◽  
H.G. Arango ◽  
A.M. Moore ◽  
E. Di Lorenzo ◽  
R.F. Milliff ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Ocean Modeling ◽  
Regional Ocean Modeling System

scholarly journals 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 data assimilation (IS4D-Var)

2016 ◽  
Author(s):  
Colette Kerry ◽  
Brian Powell ◽  
Moninya Roughan ◽  
Peter Oke
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Water Column ◽  
Marked Improvement ◽  
Variational Data Assimilation ◽  
Surface Velocity ◽  
East Australian Current ◽  
Strong Constraint ◽  
Ocean Modelling ◽  
Modelling System

Abstract. As with other western boundary currents globally, the East Australian Current (EAC) is inherently dynamic 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. This paper describes the data assimilative model configuration that achieves an optimised minimisation 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 RMS residuals with the observations of 7 cm for SSH and 0.4 °C for SST over the assimilation period. The time-mean RMS residual for subsurface temperature measured by Argo floats is a maximum of 1 °C between water depths of 100–300 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 0.8–1 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. Comparison with independent (non-assimilated) shipboard 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 determine covariance, such that the ocean state better fits and is in balance with the observations. 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.

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