  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.
