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Abstract:
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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. In this paper, 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 first obtained via a semi-continuous adaptation of the Sliced Inverse Regression, a sufficient dimension reduction approach. These reduced sets are used subsequently in a modified Nadaraya-Watson method for prediction. We implement the method on a computing cluster, and demonstrate that it is scalable. We observe a signficant speedup in the runtime when implemented in parallel. This is an attractive alternative to the traditional spatio-temporal analysis of the entire region given the large number of locations and temporal instances.
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