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Activity Number: 36 - ENVR Student Paper Awards
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistics and the Environment
Abstract #311127
Title: Statistical Downscaling with Spatial Misalignment: Application to Wildland Fire PM2.5 Concentration Forecasting
Author(s): Suman Majumder* and Yawen Guan and Brian Reich and Susan O'Neill and Ana G. Rappold
Companies: North Carolina State University and University of Nebraska-Lincoln and North Carolina State University and United States Forest Service and United States Enviromental Protection Agency
Keywords: Warping; Smoothing; Image Registration; Public Health

Fine particulate matter, PM2.5, has been documented to have adverse health effects and wildland fires are a major contributor to PM2.5 air pollution in the US. Forecasters use numerical models to predict PM2.5 concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce bias and quantify uncertainty. Typical model calibration techniques do not allow for errors due to misalignment of geographic locations. We propose a spatiotemporal downscaling methodology that uses image registration techniques to identify the spatial misalignment and accounts for and corrects the bias produced by such warping. Our model is fitted in a Bayesian framework to provide uncertainty quantification of the misalignment and other sources of error. We apply this method to different simulated data sets and show enhanced performance of the method in presence of spatial misalignment. Finally, we apply the method to a large fire in Washington state and show that the proposed method provides more realistic uncertainty quantification than standard methods.

Authors who are presenting talks have a * after their name.

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