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

Abstract Number:

3363 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Damilya Saduakhas (1), David Bolin (1)

Institutions:

(1) King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

Co-Author:

David Bolin  
King Abdullah University of Science and Technology

First Author:

Damilya Saduakhas  
King Abdullah University of Science and Technology

Presenting Author:

Damilya Saduakhas  
N/A

Abstract Text:

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.

Keywords:

Multivariate random fields|Non-Gaussian models|Matérn covariances|Nugget effect|Stochastic partial differential equations|Spatial statistics

Sponsors:

Section on Statistics and the Environment

Tracks:

Spatio-temporal statistics

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