Modeling Spatio-Temporal Transport: From Rigid Advection to Realistic Dynamics

Abstract

Stochastic models for spatio-temporal transport face a critical trade-off between physical realism and interpretability. The advection model with a single constant velocity is interpretable but physically limited by its perfect correlation over time. This work aims to bridge the gap between this simple framework and its physically realistic extensions. Our guiding principle is to introduce a spatial correlation structure that vanishes over time. To achieve this, we present two distinct approaches. The first constructs complex velocity structures, either through superpositions of advection components or by allowing the velocity to vary locally. The second is a spectral technique that replaces the singular spectrum of rigid advection with a more flexible form, introducing temporal decorrelation controlled by parameters. We accompany these models with efficient simulation algorithms and demonstrate their success in replicating complex dynamics, such as tropical cyclones and the solutions of partial differential equations. Finally, we illustrate the practical utility of the proposed framework by comparing its simulations to real-world precipitation data from Hurricane Florence.

References in corpus (4)

• Stochastic partial differential equation based modelling of large space-time data sets
• Some covariance models based on normal scale mixtures
• Fractional Brownian motion, the Matern process, and stochastic modeling of turbulent dispersion
• Spatio-Temporal Cross-Covariance Functions under the Lagrangian Framework with Multiple Advections