Integrating Nugget Correlation in Bivariate Matérn-SPDE Models for Enhanced Oceanic Data Prediction

Oregon Convention Center 

This study presents an advancement in geostatistical modeling for environmental data, focusing specifically on oceanic temperature and salinity from the Argo project. By incorporating a correlation term in the nugget effect and employing a bivariate Matérn-SPDE model with both Gaussian and non-Gaussian driving noises, we effectively address the challenges of analyzing complex environmental datasets. This extension primarily tackles issues arising from correlated measurement errors and pronounced small-scale variability. Using both simulated and real-world Argo project data from 2007-2020 for temperature and salinity, we demonstrate how this enhanced correlation parameterization impacts variable estimation and spatial predictions in bivariate Matérn-SPDE models.
Relaxing the independent noise assumption, our approach shows significant shifts in dependence characterization. We validate our model with global temperature and salinity predictions, employing a combined approach of a Matérn-SPDE model and a moving-window model. This integration refines geostatistical analysis and underscores the merit of our methodology for environmental science.

Multivariate random fields

Non-Gaussian models

Matérn covariances

Nugget effect

Stochastic partial differential equations

Spatial statistics 

Section on Statistics and the Environment