We present and discuss the use of a high-dimensional computational method for atmospheric inversions that incorporates the space-time structure of transport and dispersion errors. In urban environments, transport and dispersion errors are largely the result of our inability to capture the true underlying transport of greenhouse gas (GHG) emissions to observational sites. Motivated by the impact of transport model error on estimates of fluxes of GHGs using in situ tower-based mole-fraction observations, we specifically address the need to characterize transport error structures in high-resolution large-scale inversion models. We do this using parametric covariance functions combined with shrinkage-based regularization methods within an Ensemble Transform Kalman Filter inversion setup. We devise a synthetic data experiment to compare the impact of transport and dispersion error component of the model-data mismatch covariance choices on flux retrievals and study the robustness of the method with respect to fewer observational constraints. We demonstrate the analysis in the context of inferring CO₂ fluxes starting with a hypothesized prior in the Washington D.C. /Baltimore area constrained by a synthetic set of tower-based CO₂ measurements within an observing system simulation experiment framework. This study demonstrates the ability of these simple covariance structures to substantially improve the estimation of fluxes over standard covariance models in flux estimation from urban regions.

中文翻译：

---

考虑反演中的传输误差：城市综合数据实验


我们提出并讨论了大气反演的高维计算方法的使用，该方法结合了传输和色散误差的时空结构。在城市环境中，传输和分散误差很大程度上是由于我们无法捕获温室气体 (GHG) 排放到观测地点的真正潜在传输而造成的。由于传输模型误差对使用基于塔的摩尔分数观测的温室气体通量估计的影响，我们特别解决了在高分辨率大规模反演模型中表征传输误差结构的需要。我们在集成变换卡尔曼滤波器反演设置中使用参数协方差函数与基于收缩的正则化方法相结合来实现此目的。我们设计了一个合成数据实验来比较模型数据失配协方差选择的传输和色散误差分量对通量检索的影响，并研究该方法在较少观测约束下的稳健性。我们在推断 CO ₂通量的背景下演示了分析，从华盛顿特区/巴尔的摩地区的假设先验开始，该假设受到观测系统模拟实验框架内一组基于塔的合成 CO ₂测量的限制。这项研究证明了这些简单的协方差结构能够比标准协方差模型显着改善城市地区通量估计中的通量估计。