A stochastic space-time model for intermittent precipitation occurrences

Handle URI:
http://hdl.handle.net/10754/602309
Title:
A stochastic space-time model for intermittent precipitation occurrences
Authors:
Sun, Ying ( 0000-0001-6703-4270 ) ; Stein, Michael L.
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
A stochastic space-time model for intermittent precipitation occurrences 2015, 9 (4):2110 The Annals of Applied Statistics
Publisher:
Institute of Mathematical Statistics
Journal:
The Annals of Applied Statistics
Issue Date:
28-Jan-2016
DOI:
10.1214/15-AOAS875
Type:
Article
ISSN:
1932-6157
Sponsors:
The authors thank Kenneth P. Bowman from the Department of Atmospheric Sciences at Texas A&M University for providing the rain gauge data.
Additional Links:
http://projecteuclid.org/euclid.aoas/1453994194
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSun, Yingen
dc.contributor.authorStein, Michael L.en
dc.date.accessioned2016-03-21T12:25:21Zen
dc.date.available2016-03-21T12:25:21Zen
dc.date.issued2016-01-28en
dc.identifier.citationA stochastic space-time model for intermittent precipitation occurrences 2015, 9 (4):2110 The Annals of Applied Statisticsen
dc.identifier.issn1932-6157en
dc.identifier.doi10.1214/15-AOAS875en
dc.identifier.urihttp://hdl.handle.net/10754/602309en
dc.description.abstractModeling 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.en
dc.description.sponsorshipThe authors thank Kenneth P. Bowman from the Department of Atmospheric Sciences at Texas A&M University for providing the rain gauge data.en
dc.language.isoenen
dc.publisherInstitute of Mathematical Statisticsen
dc.relation.urlhttp://projecteuclid.org/euclid.aoas/1453994194en
dc.rightsArchived with thanks to The Annals of Applied Statisticsen
dc.titleA stochastic space-time model for intermittent precipitation occurrencesen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalThe Annals of Applied Statisticsen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDEPARTMENT OF STATISTICS UNIVERSITY OF CHICAGO CHICAGO, ILLINOIS 60637 USAen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorSun, Yingen
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