Inferring Ocean Transport Statistics With Probabilistic Neural Networks

Abstract

Using a probabilistic neural network and Lagrangian observations from the Global Drifter Program, we model the single particle transition probability density function (pdf) of ocean surface drifters. The transition pdf is represented by a Gaussian mixture whose parameters (weights, means, and covariances) are continuous functions of latitude and longitude determined to maximize the likelihood of observed drifter trajectories. This provides a comprehensive description of drifter dynamics allowing for the simulation of drifter trajectories and the estimation of a wealth of dynamical statistics without the need to revisit the raw data. As examples, we compute global estimates of mean displacements over 4 days and lateral diffusivity. We use a probabilistic scoring rule to compare our model to commonly used transition matrix models. Our model outperforms others globally and in three specific regions. A drifter release experiment simulated using our model shows the emergence of concentrated clusters in the subtropical gyres, in agreement with previous studies on the formation of garbage patches. An advantage of the neural network model is that it provides a continuous-in-space representation and avoids the need to discretize space, overcoming the challenges of dealing with nonuniform data. Our approach, which embraces data-driven probabilistic modeling, is applicable to many other problems in fluid dynamics and oceanography.