Ensemble Data Assimilation of Concentration Measurements Following the Accidental Release of a Contaminant in the Ocean: Method Testing in an Idealized Setting

Abstract

The capabilities of the ensemble Kalman filter (EKF) data assimilation (DA) method to reduce errors in simulations of concentration distributions following the accidental release of a contaminant in the ocean were evaluated. The method was tested in an idealized setting where the contaminant was released in the ocean described by a simple linear Stommel model that includes the main features of two-dimensional (2D) wind-driven circulation in the ocean on the β-plane. The wind stress curl in the right-hand side of the equation for stream function was randomly perturbed to generate an ensemble of the perturbed fields of currents. The velocity fields obtained from the ensemble of stream functions were then used for the calculation of the ensemble of concentration fields following short-duration point release. On day 1000 of the simulation, correlation coefficients of the members of the concentration ensemble and the unperturbed concentration distribution fell to 0.087. The ensemble member with the maximum deviation from the unperturbed concentration distribution was selected to be used as “truth” in data assimilation experiments. Due to the high inhomogeneity of the concentration fields, the free regularization parameter had to be defined and tuned using the L-curve approach. Different DA scenarios were considered with different topologies of measurement networks and different source locations. In all cases, data assimilation gradually brought ensemble-averaged concentration fields close to the true distribution. The root mean square errors of the analyzed concentrations on day 1000 decreased by the factors varying from 3 to 4 in different DA scenarios as compared to the simulation without DA. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.