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A SPATIO-TEMPORAL DOWNSCALER FOR OUTPUT FROM NUMERICAL MODELS
Citation:
Berrocal, V., A. Gelfand, AND D. M. HOLLAND. A SPATIO-TEMPORAL DOWNSCALER FOR OUTPUT FROM NUMERICAL MODELS. JOURNAL OF AGRICULTURAL, BIOLOGICAL, AND ENVIRONMENTAL STATISTICS. American Statistical Association, Alexandria, VA, 15(2):176-197, (2010).
Impact/Purpose:
Healthy Communities and Ecosystems - by providing new approaches to characterize landscape features, conditions, and change.
Description:
Often, in environmental data collection, 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. The first is characterized by full spatial coverage of the region of interest, high temporal resolution, no missing data, but consequential calibration concerns. The second tends to be sparsely collected in space with coarser temporal resolution, often with missing data but, where recorded, provides, essentially, the true value. 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, fully model-based strategy to downscale the output from numerical models to point level. The static spatial model, specified within a Bayesian framework, regresses the observed data on the numerical model output using spatially varying coefficients which are specified through a correlated spatial Gaussian process. As an example, we have applied our method to ozone concentration data for the Eastern US and we have compared it to Bayesian melding (Fuentes and Raftery 2005) and ordinary kriging (Cressie 1993; Chil`es and Delfiner 1999). Our results show that our method outperforms Bayesian melding in terms of computing speed and it is superior to both Bayesian melding and ordinary kriging in terms of predictive performance; predictions obtained with our method are better calibrated and predictive intervals have empirical coverage close to the nominal values. Moreover, our model can be easily extended to accommodate for the temporal dimension. In this regard, we consider several spatio-temporal versions of the static model. We compare them using out-of-sample predictions of ozone concentration for the eastern US for the period May 1–October 15, 2001. For the best choice, we present a summary of the analysis.