A Non-Stationary Spatio-Temporal Covariance Model with Dynamic Advection Effects for Rainfall Data

Pedro Nasevicius Ramos; Guilherme Ludwig

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arXiv · 2606.24076

A Non-Stationary Spatio-Temporal Covariance Model with Dynamic Advection Effects for Rainfall Data

Pedro Nasevicius Ramos·Guilherme Ludwig

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Abstract

We propose a non-stationary model constructed using a mixture of spatio-temporal covariance models with advection effects; namely, models that have larger covariance values along an orientation vector in the spatio-temporal index set, that simulate wind direction and cloud movement. We show that a mixture of such models can allow for wind direction change in data during (estimated) time intervals, unlike traditional models that use rigid advection effects. We construct a MCMC procedure for Bayesian estimation, and illustrate the method with the analysis of a severe rainfall event from the southeastern region of Brazil.

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Pedro Nasevicius Ramos, Guilherme Ludwig. 2026-06-23. A Non-Stationary Spatio-Temporal Covariance Model with Dynamic Advection Effects for Rainfall Data. https://arxiv.org/abs/2606.24076

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