Statistical Downscaling with Spatial Misalignment: Application to Wildland Fire $$\hbox {PM}_{2.5}$$ PM 2.5 Concentration Forecasting

Suman Majumder (), Yawen Guan (), Brian J. Reich (), Susan O’Neill () and Ana G. Rappold ()
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Suman Majumder: North Carolina State University
Yawen Guan: University of Nebraska–Lincoln
Brian J. Reich: North Carolina State University
Susan O’Neill: United States Forest Service
Ana G. Rappold: United States Environmental protection Agency

Journal of Agricultural, Biological and Environmental Statistics, 2021, vol. 26, issue 1, No 2, 23-44

Abstract: Abstract Fine particulate matter, PM $$_{2.5}$$ 2.5 , has been documented to have adverse health effects, and wildland fires are a major contributor to $$\hbox {PM}_{2.5}$$ PM 2.5 air pollution in the USA. Forecasters use numerical models to predict PM $$_{2.5}$$ 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.

Keywords: Image registration; Public health; Smoothing; Warping (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s13253-020-00420-4

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