A spectral method for spatial downscaling.

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 stand

SUBMITTER: Reich BJ 

PROVIDER: S-EPMC4276454 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature