Observing system simulation experiments with ensemble Kalman filters in Nantucket Sound, Massachusetts

Abstract

Observing system simulation experiments (OSSEs) were performed for Nantucket Sound, Massachusetts, as a pilot study for the design of optimal monitoring networks in the coastal ocean. Experiments were carried out using the ensemble Kalman filter (EnKF) for data assimilation with ensemble transform Kalman filter (EnTKF) and proper orthogonal decomposition (POD) for selecting the optimal monitoring sites. The singular evolutive interpolated Kalman filter (SEIK) was compared with EnKF for the data assimilation efficiency. Running the unstructured grid Finite-Volume Community Ocean Model (FVCOM) with perturbed initial fields of currents, water temperature, and salinity show that in this shallow coastal system, the velocity and surface elevation are able to restore themselves back to the true state over an inertial time scale after perturbation without data assimilation, while the water temperature and salinity are not. This suggests that in this vertically well mixed region with strong tidal influence, monitoring should be targeted at water properties rather than velocities. By placing measurement sites at an entrance or exit or a location with the maximum signal variance (EnTKF) or at extrema of the dominant EOF spatial modes (POD), we evaluated the capability of EnTKF and POD in designing the optimal monitoring site for the forecast model system in this region. The results suggest that understanding the multiscale dynamical nature of the system is essential in designing an optimal monitoring network since “optimal” sites suggested by an assimilation method may only represent a local-scale feature that has little influence on a region-wide system. Comparing EnKF and SEIK simulations shows that SEIK can significantly improve the data assimilation efficiency by reducing the ensemble number and increasing the convergence rate.