Statistical Downscaling with Spatial Misalignment: Application to Wildland Fire PM2.5 Concentration Forecasting

Authors

Suman Majumder, North Carolina State UniversityFollow
Yawen Guan, University of Nebraska–LincolnFollow
Brian J. Reich, North Carolina State UniversityFollow
Susan O’Neill, PacificNorthwest Research StationFollow
Ana G. Rappold, United States Environmental protectionAgencyFollow

Document Type

Article

Date of this Version

2020

Citation

© 2020 International Biometric Society Journal of Agricultural, Biological, and Environmental Statistics, Volume 26, Number 1, Pages 23–44 https://doi.org/10.1007/s13253-020-00420-4

Comments

U.S. government works are not subject to copyright.

Abstract

Fine particulate matter, PM_2.5, has been documented to have adverse health effects, and wildland fires are a major contributor to PM_2.5 air pollution in the USA. Forecasters use numerical models to predict PM_2.5 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.