Locally stationary spatio-temporal interpolation of Argo profiling float data

TL;DR

This paper introduces a locally stationary Gaussian process regression method for interpolating Argo float ocean data, effectively handling nonstationarity and non-Gaussian features to improve prediction accuracy and uncertainty quantification.

Contribution

It proposes a novel moving-window approach for nonstationary spatio-temporal interpolation of ocean data, incorporating Student-t distributions for heavy tails, with demonstrated improvements over existing methods.

Findings

Enhanced prediction accuracy over state-of-the-art methods

Better uncertainty calibration by modeling nonstationarity and non-Gaussianity

Provides local estimates of ocean dependence scales

Abstract

Argo floats measure seawater temperature and salinity in the upper 2,000 m of the global ocean. Statistical analysis of the resulting spatio-temporal dataset is challenging due to its nonstationary 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 nonstationary anomaly fields without the need to explicitly model the nonstationary covariance structure. We also investigate Student-$t$ distributed fine-scale variation as a means to account for non-Gaussian heavy tails in ocean temperature data. Cross-validation studies comparing the proposed approach with the existing state-of-the-art demonstrate clear improvements in point predictions and show that accounting for the nonstationarity and non-Gaussianity is crucial for obtaining well-calibrated uncertainties. This approach also provides data-driven local estimates of the spatial and temporal dependence scales for the global ocean which are of scientific interest in their own right.