A stochastic space-time model for intermittent precipitation occurrences

TL;DR

This paper introduces a novel stochastic space-time t random field model for intermittent precipitation, capturing high-frequency rain occurrence data with improved joint occurrence characteristics.

Contribution

It develops a hierarchical space-time tRF model with Bayesian interpretation, extending previous spatial models to better represent precipitation intermittency.

Findings

Model produces realistic dry and rain conditional probabilities.

Shows noticeable improvements over previous models in joint rainfall characteristics.

Applicable to high-frequency precipitation data from rain gauge networks.

Abstract

Modeling a precipitation field is challenging due to its intermittent and highly scale-dependent nature. Motivated by the features of high-frequency precipitation data from a network of rain gauges, we propose a threshold space-time $t$ random field (tRF) model for 15-minute precipitation occurrences. This model is constructed through a space-time Gaussian random field (GRF) with random scaling varying along time or space and time. It can be viewed as a generalization of the purely spatial tRF, and has a hierarchical representation that allows for Bayesian interpretation. Developing appropriate tools for evaluating precipitation models is a crucial part of the model-building process, and we focus on evaluating whether models can produce the observed conditional dry and rain probabilities given that some set of neighboring sites all have rain or all have no rain. These conditional probabilities show that the proposed space-time model has noticeable improvements in some characteristics of joint rainfall occurrences for the data we have considered.