Incorporating Correlated Nugget Effects in Multivariate Spatial Models: An Application to Argo Ocean Data

TL;DR

This paper develops advanced multivariate spatial models with correlated nugget effects to better capture small-scale variability in oceanographic data, improving parameter estimation and spatial predictions.

Contribution

It introduces Gaussian and non-Gaussian hierarchical multivariate Matérn-SPDE models with correlated nugget effects, a novel approach for modeling complex spatial dependencies in ocean data.

Findings

Correlated nugget effects improve parameter estimation accuracy.

Models with correlated noise yield lower field correlation estimates.

Enhanced modeling of fine-scale oceanic patterns.

Abstract

Accurate analysis of global oceanographic data, such as temperature and salinity profiles from the Argo program, requires geostatistical models capable of capturing complex spatial dependencies. This study introduces Gaussian and non-Gaussian hierarchical multivariate Mat\'ern-SPDE models with correlated nugget effects to account for small-scale variability and measurement error correlations. Using simulations and Argo data, we demonstrate that incorporating correlated nugget effects significantly improves the accuracy of parameter estimation and spatial prediction in both Gaussian and non-Gaussian multivariate spatial processes. When applied to global ocean temperature and salinity data, our model yields lower correlation estimates between fields compared to models that assume independent noise. This suggests that traditional models may overestimate the underlying field correlation. By separating these effects, our approach captures fine-scale oceanic patterns more effectively. These findings show the importance of relaxing the assumption of independent measurement errors in multivariate hierarchical models.