Browsing by Subject "Precipitation"
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Item Dimensionality Reduction Using Sliced Inverse Regression in Modeling Large Climate Data(2016) Allison, Ross Flieger; Miller, Lois; Sykes, Danielle; Valle, Pablo; Popuri, Sai K.; Wijekoon, Nadeesri; Neerchal, Nagaraj K.; Mehta, AmitaPrediction 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.Item Spatio-temporal analysis of precipitation data via a sufficient dimension reduction in parallel(American Statistical Association, 2016) Popuri, Sai K.; Allison, Ross Flieger; Miller, Lois; Sykes, Danielle; Valle, Pablo; Neerchal, Nagaraj K.; Adragni, Kofi P.; Mehta, Amita; Gobbert, Matthias K.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.