A Spatio-Temporal Downscaler for Output From Numerical Models

By Veronica J. Berrocal, Alan E. Gelfand, D. Holland. Journal of Agricultural Biological and Environmental Statistics, 2010

298 citations recorded by the source corpus.

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 article 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 apply our method to ozone concentration data for the eastern U.S. and compare it to Bayesian melding (Fuentes and Raftery 2005) and ordinary kriging (Cressie 1993; Chilès 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 closer 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 U.S. for the period May 1–October 15, 2001. For the best choice, we present a summary of the analysis. Supplemental material, including color versions of Figures 4, 5, 6, 7, and 8, and MCMC diagnostic plots, are available online.

Subfields: Economics and Econometrics, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/7b5a8c34cef7551ae00b82340ffe8e8a444d14f0

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Citations in the corpus

Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging (2011, methodology): “perience of Raftery et al. (2005). The length of training period for Local BMA was chosen as that which minimized the domain aggregated mean absolute error, and we refer to Mass et al.”
Geostatistical Modelling Using Non‐Gaussian Matérn Fields (2015, background): “…is commonly referred to as trans-Gaussian Kriging (Cressie, 1993) and common transformations include the square-root transform (Cressie, 1993; Huerta et al., 2004; Berrocal et al., 2010; Sahu & Mardia, 2005) and the log transform (Cressie, 1993; Cameletti et al., 2013; Bolin & Lindgren, 2011).”
Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution (2016, methodology): “In downscaling models, it is assumed that the parameters β0s and β1s arise from a continuous spatial process which allows within-grid cell variation (see Berrocal et al. (2010) for an example using a continuous bivariate spatial process).”