446 – Contributed Poster Presentations: Section on Statistics and the Environment
Spatio-Temporal Analysis of Precipitation Data via a Sufficient Dimension Reduction in Parallel
Sai K. Popuri
University of Maryland
Ross Flieger-Allison
Williams College
Lois Miller
DePauw University
Danielle Sykes
University of Maryland
Pablo Valle
Kean University
Nagaraj K. Neerchal
University of Maryland
Kofi P. Adragni
University of Maryland
Amita Mehta
Joint Center for Earth Systems Technology
Matthias K. Gobbert
University of Maryland
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 reductio metho to anlyze 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 signficanspeedup 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.