Locally stationary spatio-temporal interpolation of Argo profiling float data.

ABSTRACT: Argo floats measure seawater temperature and salinity in the upper 2000 m of the global ocean. Statistical analysis of the resulting spatio-temporal dataset is challenging owing 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 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

SUBMITTER: Kuusela M 

PROVIDER: S-EPMC6304028 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature