A Lagrangian approach towards extracting signals of urban CO<sub>2</sub> emissions from satellite observations of atmospheric column CO<sub>2</sub> (XCO<sub>2</sub>): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”)
<p>Urban regions are responsible for emitting significant amounts of fossil fuel carbon dioxide (FFCO<sub>2</sub>), 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...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2018-12-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://www.geosci-model-dev.net/11/4843/2018/gmd-11-4843-2018.pdf |
| _version_ | 1818274140833972224 |
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| author | D. Wu J. C. Lin B. Fasoli T. Oda X. Ye T. Lauvaux E. G. Yang E. A. Kort |
| author_facet | D. Wu J. C. Lin B. Fasoli T. Oda X. Ye T. Lauvaux E. G. Yang E. A. Kort |
| author_sort | D. Wu |
| collection | DOAJ |
| description | <p>Urban regions are responsible for emitting significant amounts of fossil fuel
carbon dioxide (FFCO<sub>2</sub>), 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 CO<sub>2</sub> observing networks in urban
areas. Although some previous studies have attempted to derive urban CO<sub>2</sub>
signals from satellite column-averaged CO<sub>2</sub> data (XCO<sub>2</sub>) 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 XCO<sub>2</sub>, several issues have yet to be addressed,
including quantifying uncertainties in urban XCO<sub>2</sub> signals due to
receptor configurations and errors in atmospheric transport and background
XCO<sub>2</sub>.</p><p>In this study, we present a modified version of the Stochastic Time-Inverted
Lagrangian Transport (STILT) model, <q>X-STILT</q>, for extracting urban
XCO<sub>2</sub> signals from NASA's Orbiting Carbon Observatory 2 (OCO-2)
XCO<sub>2</sub> data. X-STILT incorporates satellite profiles and provides
comprehensive uncertainty estimates of urban XCO<sub>2</sub> enhancements on
a per sounding basis. Several methods to initialize receptor/particle
setups and determine background XCO<sub>2</sub> 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
XCO<sub>2</sub> enhancements. Error estimates show that the 68 % confidence
limit of XCO<sub>2</sub> 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.</p> |
| first_indexed | 2024-12-12T22:09:08Z |
| format | Article |
| id | doaj.art-9b4a5b7dde7f4bfc9d6abf5d6f65438b |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-12-12T22:09:08Z |
| publishDate | 2018-12-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-9b4a5b7dde7f4bfc9d6abf5d6f65438b2022-12-22T00:10:18ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032018-12-01114843487110.5194/gmd-11-4843-2018A Lagrangian approach towards extracting signals of urban CO<sub>2</sub> emissions from satellite observations of atmospheric column CO<sub>2</sub> (XCO<sub>2</sub>): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”)D. Wu0J. C. Lin1B. Fasoli2T. Oda3X. Ye4T. Lauvaux5E. G. Yang6E. A. Kort7Department of Atmospheric Sciences, University of Utah, Salt Lake City, USADepartment of Atmospheric Sciences, University of Utah, Salt Lake City, USADepartment of Atmospheric Sciences, University of Utah, Salt Lake City, USAGoddard Earth Sciences Technology and Research, Universities Space Research Association, Columbia, Maryland/Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland, USADepartment of Meteorology and Atmospheric Science, Pennsylvania State University, USADepartment of Meteorology and Atmospheric Science, Pennsylvania State University, USAClimate and Space Sciences and Engineering, University of Michigan, Ann Arbor, USAClimate and Space Sciences and Engineering, University of Michigan, Ann Arbor, USA<p>Urban regions are responsible for emitting significant amounts of fossil fuel carbon dioxide (FFCO<sub>2</sub>), 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 CO<sub>2</sub> observing networks in urban areas. Although some previous studies have attempted to derive urban CO<sub>2</sub> signals from satellite column-averaged CO<sub>2</sub> data (XCO<sub>2</sub>) 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 XCO<sub>2</sub>, several issues have yet to be addressed, including quantifying uncertainties in urban XCO<sub>2</sub> signals due to receptor configurations and errors in atmospheric transport and background XCO<sub>2</sub>.</p><p>In this study, we present a modified version of the Stochastic Time-Inverted Lagrangian Transport (STILT) model, <q>X-STILT</q>, for extracting urban XCO<sub>2</sub> signals from NASA's Orbiting Carbon Observatory 2 (OCO-2) XCO<sub>2</sub> data. X-STILT incorporates satellite profiles and provides comprehensive uncertainty estimates of urban XCO<sub>2</sub> enhancements on a per sounding basis. Several methods to initialize receptor/particle setups and determine background XCO<sub>2</sub> 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 XCO<sub>2</sub> enhancements. Error estimates show that the 68 % confidence limit of XCO<sub>2</sub> 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.</p>https://www.geosci-model-dev.net/11/4843/2018/gmd-11-4843-2018.pdf |
| spellingShingle | D. Wu J. C. Lin B. Fasoli T. Oda X. Ye T. Lauvaux E. G. Yang E. A. Kort A Lagrangian approach towards extracting signals of urban CO<sub>2</sub> emissions from satellite observations of atmospheric column CO<sub>2</sub> (XCO<sub>2</sub>): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”) Geoscientific Model Development |
| title | A Lagrangian approach towards extracting signals of urban CO<sub>2</sub> emissions from satellite observations of atmospheric column CO<sub>2</sub> (XCO<sub>2</sub>): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”) |
| title_full | A Lagrangian approach towards extracting signals of urban CO<sub>2</sub> emissions from satellite observations of atmospheric column CO<sub>2</sub> (XCO<sub>2</sub>): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”) |
| title_fullStr | A Lagrangian approach towards extracting signals of urban CO<sub>2</sub> emissions from satellite observations of atmospheric column CO<sub>2</sub> (XCO<sub>2</sub>): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”) |
| title_full_unstemmed | A Lagrangian approach towards extracting signals of urban CO<sub>2</sub> emissions from satellite observations of atmospheric column CO<sub>2</sub> (XCO<sub>2</sub>): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”) |
| title_short | A Lagrangian approach towards extracting signals of urban CO<sub>2</sub> emissions from satellite observations of atmospheric column CO<sub>2</sub> (XCO<sub>2</sub>): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”) |
| title_sort | lagrangian approach towards extracting signals of urban co sub 2 sub emissions from satellite observations of atmospheric column co sub 2 sub xco sub 2 sub x stochastic time inverted lagrangian transport model x stilt v1 |
| url | https://www.geosci-model-dev.net/11/4843/2018/gmd-11-4843-2018.pdf |
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