Alfa, EphraimChen, HuiyiHansen, KristenPrindle, MathewPopuri, SaiWijekoon, NadeesriAdragni, KofiMehta, Amita2018-09-192018-09-19http://hdl.handle.net/11603/11325The 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.11 pagesen-USThis 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.Missouri River Basin (MRB)a retrospective prediction of rainfallMinimum Average Deviance Estimation (MADE)Nadaraya-Watson predictioncross-validationUMBC High Performance Computing Facility (HPCF)Text