| description abstract | Predictability of particle motion in the ocean over a timescale of one week is studied using three clusters of buoys consisting of 5?10 drifters deployed in the tropical Pacific Ocean. The analysis is conducted by using three techniques with increasing complexity: the center of mass of the cluster, advection by climatological currents, and a new technique that relies on the assimilation of both velocity and position data from the surrounding drifters into a Markov model for particle motion. A detailed mathematical description of the theory leading to this model is given. The predictability of drifter motion in these clusters is characterized using the data density Nd, defined as the number of drifters over an area scaled by the mean diameter of the cluster. The data density Nd decreases along the drifter trajectories due to the tendency of particles to disperse by turbulent fluid motion. In the first regime, which corresponds to the period after the release of drifters in a tight cluster when Nd ? 1 drifter/degree2, the center of mass and the data assimilation methods perform nearly equally well, and both methods yield very accurate predictions of drifter positions with rms prediction errors ≤15 km up to 7 days. When a cluster starts to disperse, that is, in the regime where Nd ≥ 1 drifter/degree2, the data assimilation technique is the only method that gives accurate results. Finally, when Nd ? 1 drifter/degree2, no method investigated in this study is effective. Therefore, the data assimilation method performs better than relatively crude approaches of center of mass and mean flow field evolution in the intermediate regime, in which predictability is still possible. It is also found that advection by the climatological mean flow field is generally not an accurate indicator of drifter motion. Uncertainties in the knowledge of initial release positions and the frequency of data assimilation are found to have a strong impact on the prediction accuracy. | |
