Data assimilation is the process of combining predictions from numerical models with observations of the system. Fully Lagrangian data assimilation seeks to directly assimilate trajectories from drifters into some circulation model. This talk will provide a brief overview of Lagrangian data assimilation, followed by tests of assimilating trajectories from surface drifters into a model of a small bay to improve the estimate of a spatially-dependent model parameter. We focus on the Manning’s n coefficient of friction, a parameter that generally must be tuned by hand, in the narrow, time-varying southern inlet of the bay. Synthetic experiments show that Lagrangian data assimilation can successfully estimate this parameter, regardless of the location of the drifters. Experiments with real data from 2013 show that assimilating drifter trajectories, released for a time period on the order of an hour, can improve upon the original tuned value of this parameter; however, this improvement seems to depend on the initial offset between modeled and observed velocities.
Joint work with: Larry Pratt, Irina Rypina, Mara Orescanin
Webinar presented on Monday, February 1, 2016, 10am EST
RENCI, UNC Chapel Hill