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”)
Authors: Wu Dien , Lin John C. , Fasoli Benjamin , Oda Tomohiro , Ye Xinxin , Lauvaux Thomas , Yang Emily G. , Kort Eric A. 
Published: December 2018

Abstract. Urban regions are responsible for emitting significant amounts of fossil fuel
carbon dioxide (FFCO2), and emissions at the 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 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 have yet to be addressed,
including quantifying uncertainties in urban XCO2 signals due to
receptor configurations and errors in atmospheric transport and background
XCO2. 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 of urban XCO2 enhancements on
a per sounding basis. Several methods to initialize receptor/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 2-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. Error estimates show that the 68 % confidence
limit of XCO2 uncertainties due to transport (horizontal wind plus
vertical mixing) and emission uncertainties contribute to ∼33 %
and ∼20 % of the mean latitudinally integrated urban
signals, respectively, over the five overpasses, using meteorological fields
from the Global Data Assimilation System (GDAS). In addition, a sizeable
mean difference of −0.55 ppm in background derived from a previous study
employing simple statistics (regional daily median) leads to a ∼39 % higher mean
observed urban signal and a larger posterior
scaling factor. Based on our signal estimates and associated error impacts,
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.