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Abstract:
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In many environmental problems data arise from two sources: numerical models and monitoring networks. The first source provides predictions at the level of grid cells, while the second source gives measurements at points. Accommodating the spatial misalignment between the two types of data is of fundamental importance for both improved predictions of exposure as well as for evaluation and calibration of the numerical model. In this paper we propose a simple method to downscale the output from numerical models to point level. The model, specified within a Bayesian framework, regresses the observed data on the numerical model output using spatially varying coefficients, modeled as correlated spatial Gaussian processes. Moreover, the model can be easily extended to a dynamic implementation. As an example, we have applied our method to ozone concentration data for the Eastern US.
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