A comparison of regional oxidant model (ROM) output with observed ozone data

Davis, Joseph E.; Nychka, Doug; Bailey, Bruce S.

doi:10.1016/s1352-2310(99)00424-0

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…Another approach to model evaluation is to use spatiotemporal models for monitoring data to provide estimates of average concentrations over grid cells corresponding to model prediction (Dennis et al, 1990; Chu, 1996; Sampson and Guttorp, 1998; Davis, Nychka, and Barley, 2000). This approach is reasonable when the monitoring data are dense enough that we can fit an appropriate spatiotemporal model to the data.…”

Section: Discussionmentioning

confidence: 99%

Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models

2005

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Constructing maps of dry deposition pollution levels is vital for air quality management, and presents statistical problems typical of many environmental and spatial applications. Ideally, such maps would be based on a dense network of monitoring stations, but this does not exist. Instead, there are two main sources of information for dry deposition levels in the United States: one is pollution measurements at a sparse set of about 50 monitoring stations called CASTNet, and the other is the output of the regional scale air quality models, called Models-3. A related problem is the evaluation of these numerical models for air quality applications, which is crucial for control strategy selection. We develop formal methods for combining sources of information with different spatial resolutions and for the evaluation of numerical models. We specify a simple model for both the Models-3 output and the CASTNet observations in terms of the unobserved ground truth, and we estimate the model in a Bayesian way. This provides improved spatial prediction via the posterior distribution of the ground truth, allows us to validate Models-3 via the posterior predictive distribution of the CASTNet observations, and enables us to remove the bias in the Models-3 output. We apply our methods to data on SO2 concentrations, and we obtain high-resolution SO2 distributions by combining observed data with model output. We also conclude that the numerical models perform worse in areas closer to power plants, where the SO2 values are overestimated by the models.

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Section: Discussionmentioning

confidence: 99%

Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models

2005

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“…Output is available for 79 days using meteorology from June to August 1987. For background on this model and its comparison to data see Davis et al (2000). Ambient average daily ozone has a correla tion range of the order of 300 miles and so it is reasonable to start with a 3 £ 3 grid of father wavelets as the coarsest level of resolution.…”

Section: Sample Estimates Of Hmentioning

confidence: 99%

Multiresolution models for nonstationary spatial covariance functions

Nychka

Wikle

Royle

2002

Statistical Modelling

172

121

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Many geophysical and environmental problems depend on estimating a spatial process that has nonstationary structure. A nonstationary model is proposed based on the spatial eld being a linear combination of multiresolution (wavelet) basis functions and random coef cients. The key is to allow for a limited number of correlations among coef cients and also to use a wavelet basis that is smooth. When approximately 6% nonzero correlations are enforced, this representation gives a good approximation to a family of Matern covariance functions. This sparseness is important not only for model parsimony but also has implications for the ef cient analysis of large spatial data sets. The covariance model is successfully applied to ozone model output and results in a nonstationary but smooth estimate.

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“…The model output is reported on a grid with a spacing of 1/4 0 latitude by 1/6 0 longitude (18.5 km x 18.5 km) and hourly values for ozone concentrations are available for each grid cell. Model output was validated against observational data (Davis et al 1998) and the covariance structure was compared over a larger region. The positive results of this comparison were encouraging enough to use the ROM output for modeling.…”

Section: Model Outputmentioning

confidence: 99%

“…A section of northern Illinois was used for testing the methodology of estimating nonstationary covariance functions and generating designs (Nychka et al 1996b). This region coincides with an 18 X 6 rectangle of ROM grid cells and is part of the validation region in Davis et al (1998) and was chosen because there was already some familiarity with the ozone field in this area. For this work, only the 8-hour average daily summary was used.…”

Section: Adding Rural Stations To Northern Illinoismentioning

confidence: 99%