Statistical Oceanography of Profiling Floats and Surface Drifters

2026-10-07

AI Summary

This study addresses the inference and prediction challenges arising from sparse and irregularly sampled data collected by Argo profiling floats and surface drifters within the global ocean observing system. To this end, we construct spatiotemporal statistical models that integrate modern physical oceanography. Methodologically, this work innovatively proposes a differentiated modeling strategy that distinguishes between Eulerian (moored) and Lagrangian (drifting) reference frames, while incorporating processing techniques for multi-source in situ observational data. The research systematically elucidates the statistical challenges inherent in handling these two critical types of ocean observations. By providing a robust methodological foundation for climate analysis, it significantly advances our understanding and predictive capability of ocean variability.

Abstract

The Global Ocean Observing System is key to understanding oceanic variability and climate change. The ocean is vast and often sparsely and irregularly sampled in time and space by various instruments and systems. Statistical models, especially spatio-temporal ones, are useful for enabling inferences, forecasts and decisions from sparse oceanic observations. This article focuses on the statistical treatment of two types of in situ observations in the Global Ocean Observing System: from profiling floats and surface drifters, focusing on the Argo and Global Drifter Programs. We describe the spatio-temporal models that have been developed in recent years for these data, to give a picture of the statistical challenges faced in modern physical oceanography. We will discuss in detail two types of reference frames when modeling such data: Eulerian and Lagrangian, the former of which is more appropriate for profiling floats and the latter for surface drifters.

Problem

• Statistical Oceanography
• Spatio-temporal modeling
• Profiling floats
• Surface drifters
• Sparse oceanic observations

Innovation

• Spatio-temporal models
• Profiling floats
• Surface drifters
• Eulerian reference frame
• Lagrangian reference frame