Spatio-temporal analysis of precipitation data via a sufficient dimension reduction in parallel
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Date
2016
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Citation of Original Publication
Sai K. Popuri, Ross Flieger-Allison, Lois Miller, Danielle Sykes, Pablo Valle, Nagaraj K. Neerchal, Kofi P. Adragni, Amita Mehta, and Matthias K. Gobbert, Spatio-temporal analysis of precipitation data via a sufficient dimension reduction in parallel, JSM Proceedings, Section on Statistics and the Environment, American Statistical Association, pages 3805-3815, 2016.
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Abstract
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.