The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems

Moore, Andrew M.; Arango, Hernan G.; Broquet, Grégoire; Edwards, Christopher A.; Veneziani, Milena; Powell, Brian; Foley, Dave; Doyle, James D.; Costa, Daniel P.; Robinson, Patrick W.

doi:10.1016/j.pocean.2011.05.003

Cited by 199 publications

(184 citation statements)

References 38 publications

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“…, Jones et al, 2012 and variational methods, that minimize a cost function (e.g. Moore, 2011). Regardless of the approach used, assumptions are made about the spatial footprint of an observation, for which a key parameter is the decorrelation length scale.…”

Section: Data Assimilationmentioning

confidence: 99%

Physical and biogeochemical spatial scales of variability in the East Australian Current separation from shelf glider measurements

et al. 2016

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Abstract. In contrast to physical processes, biogeochemical processes are inherently patchy in the ocean, which affects both the observational sampling strategy and the representativeness of sparse measurements in data assimilating models. In situ observations from multiple glider deployments are analysed to characterize spatial scales of variability in both physical and biogeochemical properties, using an empirical statistical model. We find that decorrelation ranges are strongly dependent on the balance between local dynamics and mesoscale forcing. The shortest horizontal (5-10 km) and vertical (45 m) decorrelation ranges are for chlorophyll a fluorescence, whereas those variables that are a function of regional ocean and atmosphere dynamics (temperature and dissolved oxygen) result in anisotropic patterns with longer ranges along (28-37 km) than across the shelf (8-19 km). Variables affected by coastal processes (salinity and coloured dissolved organic matter) have an isotropic range similar to the baroclinic Rossby radius (10-15 km).

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Section: Data Assimilationmentioning

confidence: 99%

Physical and biogeochemical spatial scales of variability in the East Australian Current separation from shelf glider measurements

et al. 2016

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“…Operational weather forecast centers such as ECMWF (Rabier et al 2000), UK Met. office (Lorenc et al 2000) and the ocean model ROMS (Moore et al 2011) use incremental 4Dvar in their operational models.…”

Section: Incremental 4dvarmentioning

confidence: 99%

Adjoint free four-dimensional variational data assimilation for a storm surge model of the German North Sea

et al. 2016

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In this paper, a data assimilation scheme based on the adjoint free Four-Dimensional Variational(4DVar) method is applied to an existing storm surge model of the German North Sea. To avoid the need of an adjoint model, an ensemble-like method to explicitly represent the linear tangent equation is adopted. Results of twin experiments have shown that the method is able to recover the contaminated low dimension model parameters to their true values. The data assimilation scheme was applied to a severe storm surge event which occurred in the North Sea in December 5, 2013. By adjusting wind drag coefficient, the predictive ability of the model increased significantly. Preliminary experiments have shown that an increase in the predictive ability is attained by narrowing the data assimilation time window.

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“…Generally, there are two kinds of data assimilation schemes, sequential and variational data assimilation [5]- [7]. The formal combines the measurement data into the numerical model [8]- [11].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Tests of Direct Insertion Data Assimilation with Pseudo Measurements

Liu¹,

Hartnett²

2014

IJCCE

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Abstract-Data Assimilation (DA) was a technique to improve the modelling forecasting ability. The measured data was blended with model background states in the DA process. Whether the model was robust to synthesize the measured data needs to be studied. In our research, Direct Insertion (DI) data assimilation was applied to update the model background states. In order to test the sensitivity of Direct Insertion data assimilation, five kinds of data assimilation model with different time interval were utilized. The influence of Direct Insertion data assimilation on surface layer was also studied. A small test area was chosen for data assimilation. Constant pseudo measurements were used to update the surface velocity components in data assimilation domain.

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The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems

Cited by 199 publications

References 38 publications

Physical and biogeochemical spatial scales of variability in the East Australian Current separation from shelf glider measurements

Physical and biogeochemical spatial scales of variability in the East Australian Current separation from shelf glider measurements

Adjoint free four-dimensional variational data assimilation for a storm surge model of the German North Sea

Sensitivity Tests of Direct Insertion Data Assimilation with Pseudo Measurements

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