2024 journal article
Regime-based precipitation modeling: A spatio-temporal approach
SPATIAL STATISTICS, 60.
author keywords: Spatio-temporal statistics; Precipitation model; TAR models; Hierarchical spatiotemporal models; INLA
doi.org (publisher)
UN Sustainable Development Goal Categories
13. Climate Action
(OpenAlex)
Source: Web Of Science
Added: May 28, 2024
In this paper, we propose a new regime-based model to describe spatio-temporal dynamics of precipitation data. Precipitation is one of the most essential factors for multiple human-related activities such as agriculture production. Therefore, a detailed and accurate understanding of the rain for a given region is needed. Motivated by the different formations of precipitation systems (convective, frontal, and orographic), we proposed a hierarchical regime-based spatio-temporal model for precipitation data. We use information about the values of neighbouring sites to identify such regimes, allowing spatial and temporal dependence to be different among regimes. Using the Bayesian approach with R INLA, we fit our model to the Guanajuato state (Mexico) precipitation data case study to understand the spatial and temporal dependencies of precipitation in this region. Our findings show the regime-based model's versatility and compare it with the truncated Gaussian model.