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Locally-stationary modelling of oceanographic spatiotemporal data

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If you have a question about this talk, please contact Mustapha Amrani.

Inference for Change-Point and Related Processes

Stochastic modelling of oceanographic spatiotemporal data provides useful summaries of key physical characteristics observed from the ocean surface. Such summaries are useful in developing global climate models and our ability to respond to environmental disasters such as oil spills. Ocean surface data is typically collected in the form of Lagrangian time series, where freely-drifting instruments (or drifters) repeatedly report their position to passing satellites. In this talk we first demonstrate that appropriate stationary models can accurately describe short intervals of the data. Over longer periods however, drifters visit regions with different spatial characteristics, which translates to time series that are nonstationary. We demonstrate how to account for this nonstationarity semi-parametrically, where we allow underlying parameters of the stochastic models to vary in time and be estimated using rolling windows. We also employ semi-parametric techniques to account for sampling issues and model misspecification. The time-varying parameter estimates can then be interpreted spatially, by aggregating output from drifters that visit similar locations. We demonstrate the effectiveness of our approach with data from the Global Drifter Programme, where re gional (as well as global) effects can be efficiently extracted using our simple statistical modelling techniques.

This talk is part of the Isaac Newton Institute Seminar Series series.

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