Dimensionality Reduction Using Sliced Inverse Regression in Modeling Large Climate Data
Links to Fileshttps://userpages.umbc.edu/~gobbert/papers/REU2016Team3.pdf
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Type of Work10 pages
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SubjectsSu cient Dimension Reduction
High Performance Computing Facilty (HPCF)
Prediction 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.