Assimilation of GPS-tracked drifter data to improve the Eulerian velocity fields in an estuary

Abstract

Numerical models are invaluable for the provision of real-time and forecasting information that can be used to examine estuarine hydrodynamics, particularly during times of flood or contaminant release. However, model outputs are associated with uncertainty; this necessitates the use of data assimilation (DA) techniques to improve model accuracy. We used an open-source DA tool to effectively assimilate Lagrangian drifter data into an estuarine hydrodynamic model using an ensemble Kalman filter (EnKF) algorithm. Our aims were to (i) evaluate the potential of drifter data for improving the accuracy of model estimates, and (ii) reduce the challenge and programming effort required for assimilation of such datasets, to make this technique accessible, for a broader range of users. We showed that assimilation of Lagrangian data obtained from prompt deployment of drifters in estuaries can lead to significant improvement (here, up to 54%) in modelled velocity fields.