Prediction of particle trajectories in the Adriatic Sea using Lagrangian data assimilation

Abstract

The predictability of Lagrangian particle trajectories in the Adriatic Sea (a semi-enclosed sub-basin of the Mediterranean Sea) over a period of 1–2 weeks is investigated using three clusters consisting of 5–7 drifters. The analysis is conducted using a Gauss–Markov Lagrangian particle model, which relies on the estimate of climatological mean flow field, persistence of turbulence, and assimilation of velocity data from the surrounding drifters through a Kalman filtering technique. The results are described using the data density N R defined as the number of drifters within a distance on the order of the Rossby radius of deformation from the particle to be predicted. The clusters are inherently different with respect to this characteristic property with values ranging from N R<0.5 to N R≥2.0 over the analysis period, depending on the initial launch pattern of the clusters and the dispersion processes. The results indicate that during the period when N R≥1, the assimilation of surrounding drifter data leads to an improvement of predicted trajectories with respect to those based on advecting the drifters with the mean flow. When N R<1, the drifters are too far apart to exhibit correlated motion, and the assimilation method does not lead to an improvement. The effects of uncertainties in the mean flow field and initial release position are discussed. The results are also compared to simple estimates of particle location by calculating the center of mass of the cluster.