Majumder Suman, Guan Yawen, Reich Brian J, O'Neill Susan, Rappold Ana G
Department of Statistics, North Carolina State University.
Department of Statistics, university of Nebraska-Lincoln.
J Agric Biol Environ Stat. 2021 Mar 1;26(1):23-44. doi: 10.1007/s13253-020-00420-4.
Fine particulate matter, PM, has been documented to have adverse health effects and wildland fires are a major contributor to PM air pollution in the US. Forecasters use numerical models to predict PM concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce bias and quantify uncertainty. Typical model calibration techniques do not allow for errors due to misalignment of geographic locations. We propose a spatiotemporal downscaling methodology that uses image registration techniques to identify the spatial misalignment and accounts for and corrects the bias produced by such warping. Our model is fitted in a Bayesian framework to provide uncertainty quantification of the misalignment and other sources of error. We apply this method to different simulated data sets and show enhanced performance of the method in presence of spatial misalignment. Finally, we apply the method to a large fire in Washington state and show that the proposed method provides more realistic uncertainty quantification than standard methods.
细颗粒物（PM）已被证明会对健康产生不利影响，而野火是美国PM空气污染的主要来源。预报员使用数值模型来预测PM浓度，以警告公众即将面临的健康风险。需要使用统计方法，利用监测数据校准数值模型预测，以减少偏差并量化不确定性。典型的模型校准技术无法处理由于地理位置未对齐而产生的误差。我们提出了一种时空降尺度方法，该方法使用图像配准技术来识别空间未对齐，并考虑并校正这种扭曲产生的偏差。我们的模型在贝叶斯框架中进行拟合，以提供未对齐和其他误差来源的不确定性量化。我们将此方法应用于不同的模拟数据集，并表明该方法在存在空间未对齐的情况下具有更好的性能。最后，我们将该方法应用于华盛顿州的一场大火，并表明所提出的方法比标准方法提供了更现实的不确定性量化。
