TY - JOUR
T1 - Prediction of particle trajectories in the adriatic sea using Lagrangian data assimilation
AU - Castellari, Sergio
AU - Griffa, Annalisa
AU - Özgökmen, Tamay M.
AU - Poulain, Pierre Marie
N1 - Funding Information:
The authors acknowledge the support of the Office of Naval Research under grants N00014-97-1-0620, N00014-99-1-0049 and N00014-99-WR-30014. We thank E. Ryan and the three anonymous reviewers for constructive comments, which led to an improved manuscript.
PY - 2001
Y1 - 2001
N2 - 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 NR 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 NR < 0.5 to NR ≥ 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 NR ≥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 NR < 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.
AB - 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 NR 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 NR < 0.5 to NR ≥ 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 NR ≥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 NR < 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.
KW - Adriatic Sea
KW - Assimilation
KW - Lagrangian data
KW - Prediction
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U2 - 10.1016/S0924-7963(01)00008-2
DO - 10.1016/S0924-7963(01)00008-2
M3 - Article
AN - SCOPUS:0034957308
VL - 29
SP - 33
EP - 50
JO - Journal of Marine Systems
JF - Journal of Marine Systems
SN - 0924-7963
IS - 1-4
ER -