A Stochastic Space-Time Model for Intermittent Precipitation Occurrences

• Activity Number: 14
• Type: Topic Contributed
• Date/Time: Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Ying Sun*+ and Michael Stein
• Companies: Ohio State University and University of Chicago
• Keywords: Conditional probabilities ; Gaussian random fields ; Monte Carlo methods ; Random scaling ; Spatio-temporal dependence ; t random fields

Abstract

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. The randomness of the scaling process increases the variability across realizations from the GRF, and is shown to capture the variability of the precipitation occurrence better than the threshold GRF model. For model comparisons and diagnostics, we focus on evaluating whether models can produce the observed conditional dry and rain probabilities given the neighboring sites have rain or no rain. The conditional probabilities, along with the marginal rainfall probabilities, are used to summarize the variability of the precipitation occurrence in space and time, and useful graphical tools are developed for visualization purpose. Model fitting and validation are conducted by Monte Carlo simulation-based approaches, where model uncertainties are presented by visualizing a set of the conditional probabilities calculated from simulations of the fitted models.