Applications of Gaussian Processes in Oceanography
Invited Lecture to the Gaussian Process Summer School, The University of Manchester
Abstract
Oceanography has long sought to understand the spatial and temporal structure of many different oceanographic processes: turbulent decay, tides, surface waves, internal waves, internal tides, and the list goes on. Most commonly, oceanographers use spectral analysis as the main tool to infer any parameters of interest, relying on regularly sampled observations of the particular process. This works well, for example, for mooring data that records temporal measurements at a single location in space, or for satellite data that records spatial measurements at single locations in time. Increasingly, we are building large data-sets that combine many different observation platforms with the aim to resolve the 3D (and 4D) spatio-temporal characteristics. Gaussian processes provide a principled method to merge all these data inside of a coherent inferential framework and are increasingly being recognised in oceanography. In this presentation, I will cover varied applications of Gaussian processes in oceanography, from relatively simple examples of temporal forecasting of surface tides, to some nascent attempts of inferring flow properties from multiple observation platforms. Finally, I’ll highlight some methodological gaps that we are looking to resolve.