ESRL/PSL Seminar Series

Abstract

Measurements from passive Lagrangian ocean drifters provide a growing source of data in our oceans. These measurements are often treated as coming from a sequence of fixed positions, and the information that the data came from a continuous trajectory is often lost. Lagrangian data assimilation seeks to make the most of this information by assimilating the positions of passive drifters in an attempt to estimate the velocity field (along with any other relevant flow variables, such as elevation, temperature, or salinity.) However, these trajectories 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 such as the particle filter (PF). In this talk I will give an overview of Lagrangian data assimilation from a Bayesian viewpoint, discuss advantages and disadvantages of some traditional data assimilation algorithms, and present a hybrid particle-ensemble Kalman filter scheme applied to the linear shallow water equations.