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 neighboring 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.

中文翻译：

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基于区域的降水建模：时空方法

在本文中，我们提出了一种新的基于制度的模型来描述降水数据的时空动态。降水是农业生产等多种人类相关活动最重要的因素之一。因此，需要对给定区域的降雨进行详细而准确的了解。受降水系统不同形成（对流、锋面和地形）的启发，我们提出了一种基于分层体系的降水数据时空模型。我们使用有关邻近站点的值的信息来识别此类政权，从而允许政权之间的空间和时间依赖性不同。使用贝叶斯方法和 R INLA，我们将模型与瓜纳华托州（墨西哥）降水数据案例研究进行拟合，以了解该地区降水的空间和时间依赖性。我们的研究结果显示了基于机制的模型的多功能性，并将其与截断高斯模型进行了比较。