3D Bivariate Spatial Modelling of Argo Ocean Temperature and Salinity Profiles
Presented by Mary Lai Salvana, Postdoctoral Fellow, Department of Mathematics, University of Houston
Monday, January 23, 2023
3:30 PM-4:30 PM ET
Webex Meeting Link
Variables contained within the global oceans can detect and reveal the effects of the warming climate as the oceans absorb huge amounts of solar energy. Hence, information regarding the joint spatial distribution of ocean variables is critical for climate monitoring. In this paper, we investigate the spatial correlation structure between ocean temperature and salinity using data harvested from the Argo program and construct a model to capture their bivariate spatial dependence from the surface to the ocean's interior. We develop a flexible class of multivariate nonstationary covariance models defined in 3-dimensional (3D) space (longitude x latitude x depth) that allows for the variances and correlation to change along the vertical pressure dimension. These models are able to describe the joint spatial distribution of the two variables while incorporating the underlying vertical structure of the ocean. We demonstrate that the proposed cross-covariance models describe the complex vertical cross-covariance structure well, while existing cross-covariance models including bivariate Matérn models poorly fit empirical cross-covariance structure. Furthermore, the results show that using one more variable significantly enhances the prediction of the other variable and that the estimated spatial dependence structures are consistent with the ocean stratification.
Mary is a Postdoctoral Fellow at the Department of Mathematics, University of Houston. She got her PhD in Statistics at the King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Her research interests include environmental data science, high performance computing, and modeling extreme events, climate risks, and cascading disasters.