CO2 Network Design for Washington DC/Baltimore

Abstract:

The North-East Corridor project aims to use a top-down inversion method to quantify sources of Greenhouse Gas (GHG) emissions in the urban areas of Washington DC and Baltimore at approximately 1km2 resolutions. The aim of this project is to help establish reliable measurement methods for quantifying and validating GHG emissions independently of the inventory methods typically used to guide mitigation efforts. Since inversion methods depend on atmospheric observations of GHG, deploying a suitable network of ground-based measurement stations is a fundamental step in estimating emissions from the perspective of the atmosphere with reasonable levels of uncertainty.

The purpose of this work is to design a tower based network of measurement stations that can reduce the uncertainty in emissions by 50% in the central areas of DC and Baltimore. To this end, the Weather Research and Forecasting Model (WRF-ARW) was used along with the Stochastic Time-Inverted Lagrangian Transport model (STILT) to derive the sensitivity of hypothetical observations to surface emissions (footprints) for the months of February and July 2013. An iterative selection algorithm, based on k-means clustering method, was applied in order to minimize the similarities between the temporal response of each site and maximize the urban contribution. Afterwards, a synthetic inversion Kalman Filter was used to evaluate the performances of the observing system based on the merit of the retrieval over time and the amount of a priori uncertainty reduced by the network.

We present the performances of various measurement networks that consist of different number of towers and where the location of these towers vary. Results show that too compact networks lose spatial coverage whilst too spread networks lose capabilities of constraining uncertainties in the fluxes. In addition, we explore the possibility of using a very high density network of low-cost, low-accuracy sensors characterized by larger uncertainties and drift over time. The convergence to the true values is faster with a large number of towers, 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.