A Lagrangian Approach Towards Extracting Signals of Urban CO2
Emissions from Satellite Observations of Atmospheric Column CO2
(XCO2): X-Stochastic Time-Inverted Lagrangian Transport model ("X-STILT v1.1")
Authors: Wu Dien , Lin John C. , Oda Tomohiro , Ye Xinxin , Lauvaux Thomas , Yang Emily G. , Kort Eric A. 
Published: June 2018

Abstract. Urban regions are responsible for emitting significant amounts of fossil fuel carbon dioxide (FFCO2), whose emissions at finer, city scales are more uncertain than those aggregated at the global scale. Carbon-observing satellites may provide independent top-down emission evaluations and compensate for the sparseness of surface CO2 observing networks, especially in urban areas. Although some previous studies have attempted to derive urban CO2 signals from satellite column-averaged CO2 data (XCO2) using simple statistical measures, less work has been carried out to link upwind emission sources to downwind atmospheric columns using atmospheric models. In addition to Eulerian atmospheric models that have been customized for emission estimates over specific cities, the Lagrangian modeling approach – in particular, the Lagrangian Particle Dispersion Model (LPDM) approach – has the potential to efficiently determine the sensitivity of downwind concentration changes to upwind sources. However, when applying LPDMs to interpret satellite XCO2, several issues – namely, uncertainties in anthropogenic XCO2 signals due to receptor configurations and errors in atmospheric transport and background XCO2 – have yet to be addressed. In this study, we present a modified version of the Stochastic Time-Inverted Lagrangian Transport (STILT) model, "X-STILT", for extracting urban XCO2 signals from NASA's Orbiting Carbon Observatory 2 (OCO-2) XCO2 data. X-STILT incorporates satellite profiles and provides comprehensive uncertainty estimates towards urban XCO2 enhancements for selected satellite soundings. Several methods to initialize receptors/particle setups and determine background XCO2 are presented and discussed via sensitivity analyses and comparisons. To illustrate X-STILT's utilities and applications, we examined five OCO-2 overpasses over Riyadh, Saudi Arabia, during a two-year time period and performed a simple scaling factor-based inverse analysis. As a result, the model is able to reproduce most observed XCO2 enhancements. Conservative error estimates show that the 68 % confidence limit of XCO2 uncertainties due to transport and emission uncertainties contribute to ~ 29 % and ~ 25 % of the averaged latitudinally-integrated urban signals, respectively, over five overpasses, given meteorological fields from the Global Data Assimilation System (GDAS). Additionally, a sizable negative "bias" of 0.56  ppm in background derived from a previous study employing simple statistics (daily median over a regional domain) leads to positive biases of 43 % in mean observed urban signal and 68 % in posterior scaling factor, from a simple inversion analysis. Based on these results, we foresee X-STILT serving as a tool for interpreting column measurements, estimating urban enhancement signals, and carrying out inverse modeling to improve quantification of urban emissions.