Statistical Methods for the Physical Sciences Seminar

  • Remote Access Enabled - Webinar
  • Virtual Presentation
  • Lecturer in Data Science
  • Department of Mathematics and Statistics
  • Lancaster University

Stochastic modeling of the ocean using drifters: The Lagrangian perspective

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 Sykulski  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.

Zoom Participation Enabled. See announcement.

The STAMPS research group bringstogether statisticians, data scientists and physical scientists to work on statistical challenges arising in data analysis in the physical sciences. STAMPS has become a vibrant forum for interdisciplinary exchange at the intersection of statistics, machine learning, and the physical sciences. These webinars will feature colloquia-style talks for a broad audience of statisticians, data scientists and domain scientists interested in statistical challenges across astronomy, particle physics, climatology, environmental science, and other related fields.

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