STAMPS@CMU presents:

Stochastic modeling of the ocean using drifters: The Lagrangian perspective

by Adam Sykulski (Department of Mathematics and Statistics, Lancaster University)

Online webinar July 10, 2020 at 1:30-2:30 PM EDT.
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Drifter deployments continue to be a popular observational method for understanding ocean currents and circulation, with numerous recent regional deployments, as well as the continued growth of the Global Drifter Program. Drifter data, however, is highly heterogenous, prone to measurement error, and captures an array of physical processes  that are difficult to disentangle. Moreover, the data is “Lagrangian” in that each drifter moves through space and time, thus posing a unique statistical and physical modelling challenge. In this talk I will start by overviewing some novel techniques for preprocessing and interpolating noisy GPS data using smoothing splines and non-Gaussian error structures. We then examine how the interpolated data can be uniquely visualised and interpreted using time-varying spectral densities. Finally we highlight some parametric stochastic models which separate physical processes such as diffusivity, inertial oscillations and tides from the background flow.


Adam is a Lecturer in Data Science at Lancaster University in the UK. Adam’s research interests are in time series analysis and spatial statistics, with a focus on spectral techniques using Fourier transforms. Adam’s main application area is in oceanography, but he also studies problems more broadly across geophysical and medical applications.