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A Non-Stationary Spatio-Temporal Covariance Model with Dynamic Advection Effects for Rainfall Data
[Submitted on 23 Jun 2026] · 2026-06-24 · via stat updates on arXiv.org

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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.

Submission history

From: Guilherme Ludwig [view email]
[v1] Tue, 23 Jun 2026 02:42:00 UTC (2,927 KB)