We present and discuss the use of Bayesian modeling and computational methods for atmospheric chemistry inverse analyses that incorporate evaluation of spatial structure in model-data residuals. Motivated by problems of refining bottom-up estimates of source/sink fluxes of trace gas and aerosols based on satellite retrievals of atmospheric chemical concentrations, we address the need for formal modeling of spatial residual error structure in global scale inversion models. We do this using analytically and computationally tractable conditional autoregressive (CAR) spatial models as components of a global inversion framework. We develop Markov chain Monte Carlo methods to explore and fit these spatial structures in an overall statistical framework that simultaneously estimates source fluxes. Additional aspects of the study extend the statistical framework to utilize priors on source fluxes in a physically realistic manner, and to formally address and deal with missing data in satellite retrievals. We demonstrate the analysis in the context of inferring carbon monoxide (CO) sources constrained by satellite retrievals of column CO from the Measurement of Pollution in the Troposphere (MOPITT) instrument on the TERRA satellite, paying special attention to evaluating performance of the inverse approach using various statistical diagnostic metrics. This is developed using synthetic data generated to resemble MOPITT data to define a proof-of-concept and model assessment, and then in analysis of real MOPITT data. These studies demonstrate the ability of these simple spatial models to substantially improve over standard non-spatial models in terms of statistical fit, ability to recover sources in synthetic examples, and predictive match with real data.
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机译：我们展示并探讨了贝叶斯建模和差分化学逆分析的计算方法，其在模型 - 数据残留中掺入了空间结构的评估。基于大气化学浓度的卫星检索，通过精制痕量气体和气溶胶源的自下而上估计的问题，我们解决了全球尺度反转模型中空间残余误差结构正式建模的需要。我们使用分析和计算的贸易条件自回归（CAR）空间模型作为全局反转框架的组件来完成此操作。我们开发Markov Chain Monte Carlo方法来探索和符合同时估计源通量的整体统计框架中的这些空间结构。该研究的其他方面扩展了统计框架，以以物理上现实的方式利用源通量的前瞻，并正式地解决和处理缺失的卫星检索数据。我们证明了在Terra卫星上污染污染污染污染污染的卫星检索的推断出来的上下文中的分析，从而特别注意了使用反向方法的性能各种统计诊断指标。这是使用生成的合成数据开发的，以类似于Mopitt数据来定义概念验证和模型评估，然后在分析实际MOPITT数据中。这些研究证明了这些简单的空间模型在统计拟合方面大大改善了标准的非空间模型，能够恢复合成示例中的源，以及与实际数据的预测匹配。
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