Assimilating Lagrangian data for parameter estimation in a multiple-inlet system

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Slivinski, Laura
Pratt, Lawrence J.
Rypina, Irina I.
Orescanin, Mara M.
Raubenheimer, Britt
MacMahan, Jamie
Elgar, Steve
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Data assimilation
Drag coefficient
Tidal inlets
Numerical models of ocean circulation often depend on parameters that must be tuned to match either results from laboratory experiments or field observations. This study demonstrates that an initial, suboptimal estimate of a parameter in a model of a small bay can be improved by assimilating observations of trajectories of passive drifters. The parameter of interest is the Manning's n coefficient of friction in a small inlet of the bay, which had been tuned to match velocity observations from 2011. In 2013, the geometry of the inlet had changed, and the friction parameter was no longer optimal. Results from synthetic experiments demonstrate that assimilation of drifter trajectories improves the estimate of n, both when the drifters are located in the same region as the parameter of interest and when the drifters are located in a different region of the bay. Real drifter trajectories from field experiments in 2013 also are assimilated, and results are compared with velocity observations. When the real drifters are located away from the region of interest, the results depend on the time interval (with respect to the full available trajectories) over which assimilation is performed. When the drifters are in the same region as the parameter of interest, the value of n estimated with assimilation yields improved estimates of velocity throughout the bay.
© The Author(s), 2017. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Ocean Modelling 113 (2017): 131-144, doi:10.1016/j.ocemod.2017.04.001.
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