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”)

Wu, Dien; Lin, John C.; Fasoli, Benjamin; Oda, Tomohiro; Ye, Xinxin; Lauvaux, Thomas; Yang, Emily G.; Kort, Eric A.

doi:https://doi.org/10.5194/gmd-11-4843-2018

Articles | Volume 11, issue 12

https://doi.org/10.5194/gmd-11-4843-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-11-4843-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 12

Development and technical paper

|

04 Dec 2018

Development and technical paper |

| 04 Dec 2018

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”)

Dien Wu, John C. Lin, Benjamin Fasoli, Tomohiro Oda, Xinxin Ye, Thomas Lauvaux, Emily G. Yang, and Eric A. Kort

Supplement

https://doi.org/10.5194/gmd-11-4843-2018-supplement

Model code and software

X-Stochastic Time-Inverted Lagrangian Transport model ("X-STILT" v1) D. Wu, J. Lin, and B. Fasoli https://doi.org/10.5281/zenodo.1241514

Short summary

Urban CO₂ enhancement signals can be derived using satellite column CO₂ concentrations and atmospheric transport models. However, uncertainties due to model configurations, atmospheric transport, and defined background values can potentially impact the derived urban signals. In this paper, we present a modified Lagrangian model framework that extracts urban CO₂ signals from satellite observations and determines potential error impacts.