A stochastic space-time model for intermittent precipitation occurrences
Type
ArticleAuthors
Sun, Ying
Stein, Michael L.
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
Date
2016-01-28Online Publication Date
2016-01-28Print Publication Date
2015-12Permanent link to this record
http://hdl.handle.net/10754/602309
Metadata
Show full item recordAbstract
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.Citation
A stochastic space-time model for intermittent precipitation occurrences 2015, 9 (4):2110 The Annals of Applied StatisticsSponsors
The authors thank Kenneth P. Bowman from the Department of Atmospheric Sciences at Texas A&M University for providing the rain gauge data.Publisher
Institute of Mathematical StatisticsJournal
The Annals of Applied StatisticsarXiv
1602.02902Additional Links
http://projecteuclid.org/euclid.aoas/1453994194ae974a485f413a2113503eed53cd6c53
10.1214/15-AOAS875