Statistical downscaling with spatial misalignment: Application to wildland fire PM$_{2.5}$ concentration forecasting

TL;DR

This paper introduces a Bayesian spatiotemporal downscaling method that uses image registration to correct spatial misalignment in PM$_{2.5}$ forecasting, improving calibration and uncertainty quantification for wildland fire pollution predictions.

Contribution

It develops a novel Bayesian downscaling approach incorporating image registration to address spatial misalignment in PM$_{2.5}$ forecasting models.

Findings

Enhanced performance in simulated data with spatial misalignment

More realistic uncertainty quantification in real fire case study

Improved calibration of PM$_{2.5}$ forecasts

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 US. 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 the 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.