A Weak Constraint 4D-Var Assimilation System for the Navy Coastal Ocean Model Using the Representer Method (original) (raw)
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Http Dx Doi Org 10 1175 Mwr D 13 00220 1, 2014
A four-dimensional variational data assimilation (4DVAR) system was recently developed for the Navy Coastal Ocean Model (NCOM). The system was tested in the first part of this study using synthetic surface and subsurface data. Here, a full range of real surface and subsurface data is considered following encouraging results from the preliminary test. The data include sea surface temperature and sea surface height from satellite, as well as subsurface observations from gliders deployed during the second Autonomous Ocean Sampling Network field experiment in California's Monterey Bay. Data assimilation is carried out with strong and weak constraints, and results are compared against independent observations. This study clearly shows that the 4DVAR approach improves the free-running model simulation and that the weak constraint experiment has lower analysis errors than does the strong constraint version. * Naval Research Laboratory Contribution Number JA/7320-13-13-1822.
2014
A 4D variational data assimilation system was developed for assimilating ocean observations with the Navy Coastal Ocean Model. It is described in this paper, along with initial assimilation experiments in Monterey Bay using synthetic observations. The assimilation system is tested in a series of twin data experiments to assess its ability to fit assimilated and independent observations by controlling the initial conditions and/or the external forcing while assimilating surface and/or subsurface observations. In all strong and weak constraint experiments, the minimization of the cost function is done with both the gradient descent method (in the control space) and the representer method (observation space). The accuracy of the forecasts following the analysis and the relevance of the retrieved forcing correction in the case of weak constraints are evaluated. It is shown that the assimilation system generally fits the assimilated and nonassimilated observations well in all experiments, yielding lower forecast errors. * Naval Research Laboratory Contribution Number JA/7320-13-1821.
Monthly Weather Review, 2014
A four-dimensional variational data assimilation (4DVAR) system was recently developed for the Navy Coastal Ocean Model (NCOM). The system was tested in the first part of this study using synthetic surface and subsurface data. Here, a full range of real surface and subsurface data is considered following encouraging results from the preliminary test. The data include sea surface temperature and sea surface height from satellite, as well as subsurface observations from gliders deployed during the second Autonomous Ocean Sampling Network field experiment in California's Monterey Bay. Data assimilation is carried out with strong and weak constraints, and results are compared against independent observations. This study clearly shows that the 4DVAR approach improves the free-running model simulation and that the weak constraint experiment has lower analysis errors than does the strong constraint version. * Naval Research Laboratory Contribution Number JA/7320-13-13-1822.
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The characterization of model errors is an essential step for effective data assimilation into openocean and shelf-seas models. In this paper, we propose an experimental protocol to properly estimate the error statistics generated by imperfect atmospheric forcings in a regional model of the Bay of Biscay, nested in a basin-scale North Atlantic configuration. The model used is the Hybrid Coordinate Ocean Model (HYCOM), and the experimental protocol involves Monte Carlo (or ensemble) simulations. The spatial structure of the model error is analyzed using the representer technique, which allows us to anticipate the subsequent impact in data assimilation systems. The results show that the error is essentially anisotropic and inhomogeneous, affecting mainly the model layers close to the surface. Even when the forcings errors are centered around zero, a divergence is observed between the central forecast and the mean forecast of the Monte Carlo simulations as a result of nonlinearities. The 3D structure of the representers characterizes the capacity of different types of measurement (sea level, sea surface temperature, surface velocities, subsurface temperature, and salinity) to control the circulation.
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4D-variational assimilation (4DVAR) is used to combine ADCP velocity observations with the Navy Coastal Ocean model (NCOM) to obtain an optimal solution that minimizes a cost function containing the weighted squared errors of velocity measurements, initial conditions, boundary conditions, and model dynamics. However, in order to converge to the global minimum of this cost function, the ocean model (and its adjoint) must be linear. Ocean models, especially those that are designed to resolve baroclinic and mesoscale processes, are typically highly-nonlinear and must be linearized. Tangent linearization is a linearization method that is performed by expanding the nonlinear dynamics about a background field using the first order approximation of Taylor's expansion. The accuracy and stability of this tangent linearized model (TLM) is a very sensitive function of the background accuracy, the level of nonlinearity of the model, complexity of the bathymetry, and the complexity of the flow field. Therefore, in high-resolution coastal domains, the TLM is only going to be stable for a relatively short period of time.