Fine particulate matter, PM2.5, 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 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.
|Original language||English (US)|
|Number of pages||22|
|Journal||Journal of Agricultural, Biological and Environmental Statistics|
|State||Published - Oct 15 2020|
Bibliographical noteKAUST Repository Item: Exported on 2021-02-16
Acknowledged KAUST grant number(s): 3800.2
Acknowledgements: We would like to thank the United States Forest Service (USFS) for providing the data. The authors were partially supported by NSF DMS-1638521, NIH ES027892, DOI 14-1-04-9 and KAUST 3800.2. We are grateful for this support.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.