Understanding greenhouse gas (GHG) column concentrations in Munich using the Weather Research and Forecasting (WRF) model

Zhao, Xinxu; Chen, Jia; Marshall, Julia; Gałkowski​​​​​​​, Michal; Hachinger, Stephan; Dietrich, Florian; Shekhar, Ankit; Gensheimer, Johannes; Wenzel, Adrian; Gerbig, Christoph

doi:https://doi.org/10.5194/acp-23-14325-2023

Articles | Volume 23, issue 22

https://doi.org/10.5194/acp-23-14325-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-23-14325-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 22

Research article

|

20 Nov 2023

Research article |

| 20 Nov 2023

Understanding greenhouse gas (GHG) column concentrations in Munich using the Weather Research and Forecasting (WRF) model

Xinxu Zhao, Jia Chen, Julia Marshall, Michal Gałkowski, Stephan Hachinger, Florian Dietrich, Ankit Shekhar, Johannes Gensheimer, Adrian Wenzel, and Christoph Gerbig

Supplement

https://doi.org/10.5194/acp-23-14325-2023-supplement

Data sets

Column-averaged dry-air mole fractions of CO2, CH4 and CO recorded during an urban measurement campaign in Munich in August 2018 with five solar-tracking Fourier transform spectrometers (EM27/SUN) Florian Dietrich, Jia Chen, Andreas Forstmaier, Xiao Bi, Björn Reger, Andreas Luther, Jared Matzke, Mohammad Ramezani, Frank Hase, and André Butz https://doi.org/10.1594/PANGAEA.962966

Model code and software

uataq/stilt: Geoscientific Model Development 2018 (v1.1) Ben Fasoli https://doi.org/10.5281/zenodo.1238047

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Short summary

We develop a modeling framework using the Weather Research and Forecasting model at a high spatial resolution (up to 400 m) to simulate atmospheric transport of greenhouse gases and interpret column observations. Output is validated against weather stations and column measurements in August 2018. The differential column method is applied, aided by air-mass transport tracing with the Stochastic Time-Inverted Lagrangian Transport (STILT) model, also for an exploratory measurement interpretation.