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    A stochastic space-time model for intermittent precipitation occurrences

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    euclid.aoas.1453994194.pdf
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    Description:
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    Type
    Article
    Authors
    Sun, Ying cc
    Stein, Michael L.
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    Date
    2016-01-28
    Online Publication Date
    2016-01-28
    Print Publication Date
    2015-12
    Permanent link to this record
    http://hdl.handle.net/10754/602309
    
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    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.
    Citation
    A stochastic space-time model for intermittent precipitation occurrences 2015, 9 (4):2110 The Annals of Applied Statistics
    Sponsors
    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 Statistics
    Journal
    The Annals of Applied Statistics
    DOI
    10.1214/15-AOAS875
    arXiv
    1602.02902
    Additional Links
    http://projecteuclid.org/euclid.aoas/1453994194
    ae974a485f413a2113503eed53cd6c53
    10.1214/15-AOAS875
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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