A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is
Lagrangian measurements from passive ocean instruments provide a useful source of data for estimating and forecasting the ocean's state (velocity field, salinity field, etc.). However, trajectories from these instruments are often highly nonlinear, leading to difficulties with widely used data assimilation algorithms such as the ensemble Kalman filter (EnKF). Additionally, the velocity field is often modeled as a high-dimensional variable, which precludes the use of more accurate methods suchdoi:10.1175/mwr-d-14-00051.1 fatcat:nh5fdijzgfem3bp53rrargb4we