@article{zhou_chang_fuentes_2012, title={Estimating the Health Impact of Climate Change With Calibrated Climate Model Output}, volume={17}, ISSN={["1537-2693"]}, DOI={10.1007/s13253-012-0105-y}, abstractNote={Studies on the health impacts of climate change routinely use climate model output as future exposure projection. Uncertainty quantification, usually in the form of sensitivity analysis, has focused predominantly on the variability arise from different emission scenarios or multi-model ensembles. This paper describes a Bayesian spatial quantile regression approach to calibrate climate model output for examining to the risks of future temperature on adverse health outcomes. Specifically, we first estimate the spatial quantile process for climate model output using nonlinear monotonic regression during a historical period. The quantile process is then calibrated using the quantile functions estimated from the observed monitoring data. Our model also down-scales the gridded climate model output to the point-level for projecting future exposure over a specific geographical region. The quantile regression approach is motivated by the need to better characterize the tails of future temperature distribution where the greatest health impacts are likely to occur. We applied the methodology to calibrate temperature projections from a regional climate model for the period 2041 to 2050. Accounting for calibration uncertainty, we calculated the number of of excess deaths attributed to future temperature for three cities in the US state of Alabama.}, number={3}, journal={JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS}, author={Zhou, Jingwen and Chang, Howard H. and Fuentes, Montserrat}, year={2012}, month={Sep}, pages={377–394} }
 @article{zhou_fuentes_davis_2011, title={Calibration of Numerical Model Output Using Nonparametric Spatial Density Functions}, volume={16}, ISSN={["1085-7117"]}, DOI={10.1007/s13253-011-0076-4}, abstractNote={The evaluation of physically based computer models for air quality applications is crucial to assist in control strategy selection. Selecting the wrong control strategy has costly economic and social consequences. The objective comparison of the means and variances of modeled air pollution concentrations with the ones obtained from observed field data is the common approach for the assessment of model performance. One drawback of this strategy is that it fails to calibrate properly the tails of the modeled air pollution distribution. Improving the ability of these numerical models to characterize high pollution events is of critical interest for air quality management. In this work we introduce an innovative framework to assess model performance, not only based on the two first moments of the model outputs and field data, but also on their entire distributions. Our approach also compares the spatial dependence and variability in two data sources. More specifically, we estimate the spatial-quantile functions for both the model output and field data, and we apply a nonlinear monotonic regression approach to the quantile functions taking into account the spatial dependence to compare the density functions of numerical models and field data. We use a Bayesian approach for estimation and fitting to characterize uncertainties in data and statistical models. We apply our methodology to assess the performance of the US Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model to characterize ozone ambient concentrations. Our approach shows a 50.23% reduction in the root mean square error (RMSE) compared to the default approach based on the first 2 moments of the model output and field data.}, number={4}, journal={JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS}, author={Zhou, Jingwen and Fuentes, Montserrat and Davis, Jerry}, year={2011}, month={Dec}, pages={531–553} }
 @article{chang_zhou_fuentes_2010, title={Impact of climate change on ambient ozone level and mortality in Southeastern United States}, volume={7}, number={7}, journal={International Journal of Environmental Research and Public Health}, author={Chang, H. H. and Zhou, J. W. and Fuentes, M.}, year={2010}, pages={2866–2880} }