CASPO Seminar: Mikael Kuusela (SAMSI and UNC Chapel Hill) "Locally stationary spatio-temporal interpolation of Argo float data"


 
10/19/2017 - 10:00am

Locally stationary spatio-temporal interpolation of Argo float data

Argo floats measure sea water temperature and salinity in the upper 2,000 m of the global ocean. The statistical analysis of the resulting spatio-temporal dataset is challenging due to its non-stationary structure and large size. We propose mapping these data using locally stationary Gaussian process regression where covariance parameter estimation and spatio-temporal prediction are carried out in a moving-window fashion. This yields computationally tractable non-stationary anomaly fields without the need to explicitly model the non-stationary covariance structure. We also investigate Student-t distributed microscale variation as a means to account for non-Gaussian heavy tails in Argo data. We use cross-validation to study the point prediction and uncertainty quantification performance of the proposed approach. We demonstrate clear improvements in the point predictions and show that accounting for the non-Gaussianity is crucial for obtaining well-calibrated uncertainties. The approach also provides data-driven local estimates of the spatial and temporal dependence scales which are of scientific interest in their own right. Joint work with Michael L. Stein (UChicago).

For more information on this event, contact: 
Donata Giglio
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Location: 
NH 101