Dimensionality Reduction Using Sliced Inverse Regression in Modeling Large Climate Data
Loading...
Links to Files
Permanent Link
Author/Creator ORCID
Date
2016
Type of Work
Department
Program
Citation of Original Publication
Rights
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
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. 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.