Using Historical Data for Retrospective Prediction of Rainfall In the Midwest

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dc.contributor.authorAlfa, Ephraim
dc.contributor.authorChen, Huiyi
dc.contributor.authorHansen, Kristen
dc.contributor.authorPrindle, Mathew
dc.contributor.authorPopuri, Sai
dc.contributor.authorWijekoon, Nadeesri
dc.contributor.authorAdragni, Kofi
dc.contributor.authorMehta, Amita
dc.date.accessioned2018-09-19T20:16:26Z
dc.date.available2018-09-19T20:16:26Z
dc.description.abstractThe Missouri River Basin (MRB) is an important food-producing region in the United States and Canada. Climate variability and water availability affect crops production in this region. Past climate data have been recorded at various locations in the basin over a period of ten years. We use the data for a retrospective prediction of rainfall. As the dimension of the data is relatively large, a sufficient dimension reduction approach is used to reduce the dimensionality of the data while preserving the regression information pertinent to rainfall. We use the nascent dimension reduction methodology called Minimum Average Deviance Estimation or MADE to reduce the dimensionality of the climate data. Since MADE is still a tool in development, we explored two of its intrinsic prediction methods and compared them to the Nadaraya-Watson prediction approach by a cross-validation. A parallel implementation of MADE and its prediction methods on a high performance computer were carried out. A performance study was performed along with the application of the best prediction method to the MRB climate data.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 Depart- ment 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 Ephraim Alfa was supported, in part, by the UMBC National Security Agency (NSA) Scholars Program through a contract with the NSA. Graduate assistants Sai Popuri and Nadeesri Wijekoon were supported by UMBC.en_US
dc.description.urihttps://userpages.umbc.edu/~gobbert/papers/REU2016Team1.pdfen_US
dc.format.extent11 pagesen_US
dc.genretechnical reporten_US
dc.identifierdoi:10.13016/M2PK07539
dc.identifier.urihttp://hdl.handle.net/11603/11325
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 Geography and Environmental Systems Department
dc.relation.ispartofseriesHPCF Technical Report;HPCF-2016-11
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.subjectMissouri River Basin (MRB)en_US
dc.subjecta retrospective prediction of rainfallen_US
dc.subjectMinimum Average Deviance Estimation (MADE)en_US
dc.subjectNadaraya-Watson predictionen_US
dc.subjectcross-validationen_US
dc.subjectUMBC High Performance Computing Facility (HPCF)en_US
dc.titleUsing Historical Data for Retrospective Prediction of Rainfall In the Midwesten_US
dc.typeTexten_US

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