Dimensionality Reduction Using Sliced Inverse Regression in Modeling Large Climate Data

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dc.contributor.authorAllison, Ross Flieger
dc.contributor.authorMiller, Lois
dc.contributor.authorSykes, Danielle
dc.contributor.authorValle, Pablo
dc.contributor.authorPopuri, Sai K.
dc.contributor.authorWijekoon, Nadeesri
dc.contributor.authorNeerchal, Nagaraj K.
dc.contributor.authorMehta, Amita
dc.date.accessioned2018-09-19T20:19:52Z
dc.date.available2018-09-19T20:19:52Z
dc.date.issued2016
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. 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 rst obtained via a semi-continuous adaptation of the Sliced Inverse Regression, a su cient dimension reduction approach. These reduced sets are used subsequently in a modi ed Nadaraya-Watson method for prediction. We implement the method on a computing cluster and demonstrate that it is scalable. We observe a significant speedup in the runtime when implemented in parallel.en_US
dc.description.sponsorshipThese results were obtained as part of the REU Site: Interdisciplinary Program in High Performance Computing (hpcreu.umbc.edu) in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC) in Summer 2016. This program is funded by the National Science Foundation (NSF), the National Security Agency (NSA), and the Department of Defense (DOD), with additional support from UMBC, the Department of Mathematics and Statistics, the Center for Interdisciplinary Research and Consulting (CIRC), and the UMBC High Performance Computing Facility (HPCF). HPCF 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 UMBC. Co-author Danielle Sykes was supported, in part, by the UMBC National Security Agency (NSA) Scholars Program through a contract with the NSA. Graduate assistants Sai K. Popuri and Nadeesri Wijekoon was supported by UMBC.en_US
dc.description.urihttps://userpages.umbc.edu/~gobbert/papers/REU2016Team3.pdfen_US
dc.format.extent10 pagesen_US
dc.genretechnical reporten_US
dc.identifierdoi:10.13016/M2F47GX9Z
dc.identifier.urihttp://hdl.handle.net/11603/11327
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Mathematics and Statistics Department
dc.relation.ispartofseriesHPCF Technical Report;HPCF-2016-13
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.subjectSu cient 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.titleDimensionality Reduction Using Sliced Inverse Regression in Modeling Large Climate Dataen_US
dc.typeTexten_US

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