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.

Siting Background Towers to Characterize Incoming Air for Urban Greenhouse Gas Estimation: A Case Study in the Washington, DC/Baltimore Area

“…Finally, although atmospheric transport and dispersion modeling errors are not included in the synthetic observations for this study, we do not expect that adding Gaussian noise, as is typically done (e.g., Lauvaux et al, 2016;Lopez-Coto et al, 2017) to account for these errors in synthetic studies, will impact the selection of background tower locations. The addition of Gaussian noise would largely reduce the significance of estimated scaling factors,β, associated with the selected background tower time series.…”

“…Unlike y continental , these contributions are not considered to be spatially or temporally homogeneous or well mixed. The 550 km domain was chosen to be consistent with the Lopez‐Coto et al () urban tower selection study.…”

“…To create the synthetic observations ( y ex and y urb ), Weather Research and Forecast (WRF) modeled output (details in Lopez‐Coto et al, ) is used in conjunction with the Stochastic Time Inverted Lagrangian Transport (STILT) model (Lin et al, ), which is based on NOAA's HYSPLIT algorithm (Stein et al, ). These transport and dispersion models are used to estimate sensitivities of hourly observations to surface fluxes (units of ), referred to as footprints or the matrix H i = 1..12 , where i designates one of the 12 NEC‐B/W towers.…”

“…We are particularly focused on how this variability manifests itself in mole fraction measurements from urban towers in the NEC‐B/W. We employed a simulation analysis of candidate background observational sites located on communications towers outside the NEC‐B/W along with urban tower locations identified by Lopez‐Coto et al (). The method presented in this paper is not specific to CO 2 and thus can be applied to other trace gases as well, if meteorological information and realistic prior flux information is available.…”

Detecting Urban Emissions Changes and Events With a Near‐Real‐Time‐Capable Inversion System

“…The density and placement of sensors within a network, together with the local meteorology and the spatiotemporal pattern of emissions, determines the extent to which the network is reliably sensitive to emissions over the whole region of interest and within the relevant time scale. Prospective network design studies (e.g., Kort et al, 2013;Lopez-Coto et al, 2017;Turner et al, 2016) have attempted to ensure adequate sensitivity, but the standard of adequacy is necessarily relative to some particular purpose or question.…”