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The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems: Part II - Performance and application to the California Current System |
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| Authors: | Andrew M Moore Hernan G ArangoGregoire Broquet Chris EdwardsMilena Veneziani Brian PowellDave Foley James D DoyleDan Costa Patrick Robinson |
| Institution: | a Department of Ocean Sciences, University of California, 1156 High Street, Santa Cruz, CA 95064, United States b Institute of Marine and Coastal Sciences, Rutgers University, 71 Dudley Road, New Brunswick, NJ 08901-8521, United States c Laboratoire des Sciences du Climat et de l’Environnement, CEA-Orme des Merisiers, F-91191 GIF-SUR-YVETTE CEDEX, France d Department of Oceanography, University of Hawai’i at Manoa, Honolulu, HI 96822, United States e Environmental Research Division, NOAA Southwest Fisheries Science Center, Pacific Grove, CA, United States f Naval Research Laboratory, Monterey, CA, United States g Department of Ecology and Evolutionary Biology, Long Marine Laboratory, University of California, Santa Cruz, CA 95064, United States |
| Abstract: | The Regional Ocean Modeling System (ROMS) 4-dimensional variational (4D-Var) data assimilation systems have been systematically applied to the mesoscale circulation environment of the California Current to demonstrate the performance and practical utility of the various components of ROMS 4D-Var. In particular, we present a comparison of three approaches to 4D-Var, namely: the primal formulation of the incremental strong constraint approach; the dual formulation “physical-space statistical analysis system”; and the dual formulation indirect representer approach. In agreement with theoretical considerations all three approaches converge to the same ocean circulation estimate when using the same observations and prior information. However, the rate of convergence of the dual formulation was found to be inferior to that of the primal formulation. Other aspects of the 4D-Var performance that relate to the use of multiple outer-loops, preconditioning, and the weak constraint are also explored. A systematic evaluation of the impact of the various components of the 4D-Var control vector (i.e. the initial conditions, surface forcing and open boundary conditions) is also presented. It is shown that correcting for uncertainties in the model initial conditions exerts the largest influence on the ability of the model to fit the available observations. Various important diagnostics of 4D-Var are also examined, including estimates of the posterior error, the information content of the observation array, and innovation-based consistency checks on the prior error assumptions. Using these diagnostic tools, we find that more than 90% of the observations assimilated into the model provide redundant information. This is a symptom of the large percentage of satellite data that are used and to some extent the nature of the data processing employed. This is the second in a series of three papers describing the ROMS 4D-Var systems. |
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