Tower-based greenhouse gas measurement network design—The National Institute of Standards and Technology North East Corridor Testbed

Abstract

The North–East Corridor (NEC) Testbed project is the 3rd of three NIST (National Institute of Standards and Technology) greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions (footprints). Unlike other network design algorithms, an iterative selection algorithm, based on a k-means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and, therefore, biasing the retrieved fluxes.

摘要

东北走廊(NEC, North-East Corridor)测试平台项目是美国国家标准与技术研究所(NIST, National Institute of Standards and Technology)的第三个温室气体排放源测试平台. 本项目旨在提高温室气体测量能力. 本文介绍了这个项目的与大气反演方法相结合的密集观测网络的设计方法. 这种方法应用 WRF(ARW 版本) 模式(Advanced ResearchWeather Research and Forecasting Model)与STILT模式(Stochastic Time-Inverted Lagrangian Transport model)相耦合来计算假定的观测对地表温室气体排放源的敏感性(足迹). 和其他观测网的设计算法的不同之处在于, 这个密集观测网络应用一个基于 k-means 聚类方法的迭代挑选算法, 以最小化每个站点的时间响应之间的相似性, 并最大化城市排放源贡献的敏感性. 一旦选择了某种配置的观测网, 将使用综合反演贝叶斯卡尔曼滤波来评估它的性能. 我们展示了由不同数量的塔和不同的位置的塔组成的不同配置的多个观测网的性能. 结果表明, 由于附加到每个站点的空间信息不能最理想的覆盖最大可能的区域, 过度密集的观测网的空间覆盖范围会减小, 而过度分散的观测网则会失去约束通量不确定性的能力. 此外, 我们还探讨了使用带有较低成本和具有较大不确定性和时间偏移的性能传感器的高密度网络的可能性. 当观测站点较多时, 分析收敛速度变快, 减少了滤波的响应时间. 观测中的较大不确定性意味着较少的不确定性的减少值. 另一方面, 自然界中存在的偏差被带入到观测中, 从而使得反演通量有偏差.