A Spectral Method for Spatial Downscaling

• Type of Material: Article
• Authors: Brain J. Reich, North Carolina State University; Howard Chang, Emory University; Kristen M. Foley, US Environmental Protection Agency
• Language: English
• Date: 2014-12-01
• Publisher: Wiley: Biometrics
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2014, The International Biometric Society.
• Final Published Version (URL): http://dx.doi.org/10.1111/biom.12196
• Title of Journal or Parent Work: Biometrics
• ISSN: 0006-341X
• Volume: 70
• Issue: 4
• Start Page: 932
• End Page: 942
• Grant/Funding Information: This study was partially supported by USEPA grants R835228 and R834799; NSF grant 1107046; and NIH grants R01ES014843, R01ES019897, and R21ES022795-01A1.
• Keywords: NUMERICAL-MODELS; OZONE; Data fusion; OUTPUT; COMPONENT; Science & Technology; Mathematics; Biology; Physical Sciences; SENSITIVITY; Statistics & Probability; Life Sciences & Biomedicine - Other Topics; Kriging; Multiscale analysis; Mathematical & Computational Biology; AIR-POLLUTION; Life Sciences & Biomedicine; Computer model output
• Research Categories: Biology, Biostatistics; Biology, Bioinformatics

Abstract

Complex computer models play a crucial role in air quality research. These models are used to evaluate potential regulatory impacts of emission control strategies and to estimate air quality in areas without monitoring data. For both of these purposes, it is important to calibrate model output with monitoring data to adjust for model biases and improve spatial prediction. In this article, we propose a new spectral method to study and exploit complex relationships between model output and monitoring data. Spectral methods allow us to estimate the relationship between model output and monitoring data separately at different spatial scales, and to use model output for prediction only at the appropriate scales. The proposed method is computationally efficient and can be implemented using standard software. We apply the method to compare Community Multiscale Air Quality (CMAQ) model output with ozone measurements in the United States in July 2005. We find that CMAQ captures large-scale spatial trends, but has low correlation with the monitoring data at small spatial scales.