Spatio-temporal analysis of precipitation data via a sufficient dimension reduction in parallel

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dc.contributor.authorPopuri, Sai K.
dc.contributor.authorAllison, Ross Flieger
dc.contributor.authorMiller, Lois
dc.contributor.authorSykes, Danielle
dc.contributor.authorValle, Pablo
dc.contributor.authorNeerchal, Nagaraj K.
dc.contributor.authorAdragni, Kofi P.
dc.contributor.authorMehta, Amita
dc.contributor.authorGobbert, Matthias K.
dc.date.accessioned2018-09-19T20:06:34Z
dc.date.available2018-09-19T20:06:34Z
dc.date.issued2016
dc.descriptionJoint Statistical Meeting 2016
dc.description.abstractPrediction of precipitation using simulations on various climate variables provided by Global Climate Models (GCM) as covariates is often required for regional hydrological assessment studies. In this paper, we use a sufficient dimension reduction method to analyze monthly precipitation data over the Missouri River Basin (MRB). At each location, effective reduced sets of monthly historical simulated data from a neighborhood provided by MIROC5, a Global Climate Model, are first obtained via a semi-continuous adaptation of the Sliced Inverse Regression, a sufficient dimension reduction approach. These reduced sets are used subsequently in a modified Nadaraya-Watson method for prediction. We implement the method on a computing cluster, and demonstrate that it is scalable. We observe a signficant speedup in the runtime when implemented in parallel. This is an attractive alternative to the traditional spatio-temporal analysis of the entire region given the large number of locations and temporal instances.en_US
dc.description.sponsorshipFirst author would like to thank Joint Center for Earth Systems Technology (JCET) for funding. We gratefully acknowledge The Center For Research on the Changing Earth System (CRCES) for providing us the data. The hardware used in the computational studies is part of the UMBC High Performance Computing Facility (HPCF). The facility is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS-0821258 and CNS-1228778) and the SCREMS program (grant no. DMS- 0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCF and the projects using its resources.en_US
dc.description.urihttps://userpages.umbc.edu/~gobbert/papers/REU2016Team3_JSM.pdfen_US
dc.format.extent11 pagesen_US
dc.genreconference paper pre-printen_US
dc.identifierdoi:10.13016/M2BC3T15G
dc.identifier.citationSai K. Popuri, Ross Flieger-Allison, Lois Miller, Danielle Sykes, Pablo Valle, Nagaraj K. Neerchal, Kofi P. Adragni, Amita Mehta, and Matthias K. Gobbert, Spatio-temporal analysis of precipitation data via a sufficient dimension reduction in parallel, JSM Proceedings, Section on Statistics and the Environment, American Statistical Association, pages 3805-3815, 2016.en_US
dc.identifier.urihttp://hdl.handle.net/11603/11320
dc.language.isoen_USen_US
dc.publisherAmerican Statistical Associationen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Mathematics and Statistics Department
dc.rightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
dc.subjectSufficient Dimension Reductionen_US
dc.subjectSpatio-temporalen_US
dc.subjectMIROC5en_US
dc.subjectPrecipitationen_US
dc.subjectParallel Computingen_US
dc.subjectUMBC High Performance Computing Facility (HPCF)en_US
dc.titleSpatio-temporal analysis of precipitation data via a sufficient dimension reduction in parallelen_US
dc.typeTexten_US

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