Allison, Ross FliegerMiller, LoisSykes, DanielleValle, PabloPopuri, Sai K.Wijekoon, NadeesriNeerchal, Nagaraj K.Mehta, Amita2018-09-192018-09-192016http://hdl.handle.net/11603/11327Prediction 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.10 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.Su cient Dimension ReductionSpatio-temporalMIROC5PrecipitationParallel ComputingUMBC High Performance Computing Facility (HPCF)Text