An adaptive spatial model for precipitation data from multiple satellites over large regions
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ArticleAuthors
Chakraborty, AvishekDe, Swarup
Bowman, Kenneth P.
Sang, Huiyan
Genton, Marc G.

Mallick, Bani K.
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
Date
2013-12-04Online Publication Date
2013-12-04Print Publication Date
2015-03Permanent link to this record
http://hdl.handle.net/10754/552390
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Satellite measurements have of late become an important source of information for climate features such as precipitation due to their near-global coverage. In this article, we look at a precipitation dataset during a 3-hour window over tropical South America that has information from two satellites. We develop a flexible hierarchical model to combine instantaneous rainrate measurements from those satellites while accounting for their potential heterogeneity. Conceptually, we envision an underlying precipitation surface that influences the observed rain as well as absence of it. The surface is specified using a mean function centered at a set of knot locations, to capture the local patterns in the rainrate, combined with a residual Gaussian process to account for global correlation across sites. To improve over the commonly used pre-fixed knot choices, an efficient reversible jump scheme is used to allow the number of such knots as well as the order and support of associated polynomial terms to be chosen adaptively. To facilitate computation over a large region, a reduced rank approximation for the parent Gaussian process is employed.Citation
An adaptive spatial model for precipitation data from multiple satellites over large regions 2013, 25 (2):389 Statistics and ComputingPublisher
Springer NatureJournal
Statistics and ComputingAdditional Links
http://link.springer.com/10.1007/s11222-013-9439-8ae974a485f413a2113503eed53cd6c53
10.1007/s11222-013-9439-8