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

Abstract

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.

Estimation of Bottom Friction Coefficient in Multi‐Constituent Tidal Models Using the Adjoint Method: Temporal Variations and Spatial Distributions

“…(2014), Demissie and Bacopoulos (2017), Slivinski et al. (2017), and Siripatana et al. (2018).…”

“…Qian et al (2021) estimated the spatially varying BFCs in the BYECS using the adjoint method, and anzlyzed the characteristics and possible mechanisms of the estimated BFCs, especially the occurrence of large values. Other data assimilation methods, including the singular evolutive interpolated Kalman filter (Mayo et al, 2014), Bayesian inference (Sraj et al, 2014), nudging analysis (Demissie & Bacopoulos, 2017) and ensemble Kalman filter (Siripatana et al, 2018;Slivinski et al, 2017), have also been used to estimate BFCs in numerical models.…”

Massively parallel implicit equal-weights particle filter for ocean drift trajectory forecasting

“…The SOAR function in (24) with c SOAR = 2 consists of a stencil operation depending on 5 × 5 input values centered on the target cell. We use one GPU thread per output element.…”

“…They introduce backtracking particle filters and show that the filters outperform EKF for the two-point vortex system. Other methods than particle filters and smoothers have also been successfully implemented [20,21,22,23,24].…”

Analytic reconstruction of a two-dimensional velocity field from an observed diffusive scalar

“…An example is the inviscid shallow-water model with a rigid lid (Pedlosky 2013;Batchelor 1967), as we shall discuss next. This shallow-water approximation is often employed in physical oceanography to describe ocean flows in shallow tidally-driven bays, such as, for example, the Katama Bay located on the island of Martha's Vineyard, MA (Slivinski et al 2017;Orescanin et al 2016;Luettich Jr & Westerink 1991).…”