EGU24-9996, updated on 08 Mar 2024
https://doi.org/10.5194/egusphere-egu24-9996
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

High-resolution inversion of Berlin city emissions – A synthetic study using FLEXPART-WRF for network optimization within ITMS

Christopher Lüken-Winkels1, Lukas Pilz1, Massimo Cassiani2, Ignacio Pisso2, and Sanam N. Vardag1,3
Christopher Lüken-Winkels et al.
  • 1Institute of Environmental Physics, Heidelberg University, Heidelberg, Germany
  • 2NILU, Kjeller, Norway
  • 3Heidelberg Center for the Environment, Heidelberg University, Heidelberg, Germany

Urban areas are significant contributors to anthropogenic carbon dioxide (CO2) emissions, responsible for approximately 70% of the total anthropogenic CO2 emissions. In the years to come, it is expected that urban areas will increase their efforts to mitigate CO2 emissions. To independently verify these reductions, atmospheric measurements of CO2 and other tracers can be used within an inversion framework to estimate emissions. While there are some cities which have established measurement networks for this purpose, many urban centers are still lacking the necessary measurement infrastructure for high-resolution inverse modeling. It is an open question how a measurement network should be designed to maximize the information content of the urban emissions. 

In our study, we conduct Observing System Simulation Experiments (OSSEs) to evaluate the potential of different measurement network configurations for the city of Berlin, Germany. The approach involves utilizing meteorological data at a spatial resolution of 1 km, computed using the Weather Research & Forecasting Model (WRF), to model the relationship between emissions and measured concentrations (footprints). The footprints are calculated using the Lagrangian Particle Dispersion Model FLEXPART-WRF. Concentration enhancements of WRF and FLEXPART-WRF are compared throughout a year. 

We assess various in-situ network configurations, considering both preexisting meteorological networks and a gridded approach for potential measurement locations. Using a Bayesian inversion for the prediction of emissions, different subsets of these networks are selected to constrain total emissions as well as anthropogenic and biogenic CO2 fluxes. The tested measurement configurations encompass variations in the number and quality of stations, allowing for the identification of both efficient and effective networks.  

In conclusion, our findings provide insights into the strategic deployment of CO2 measurement networks in Berlin, supporting ongoing efforts to refine greenhouse gas monitoring. The available meteorological data will additionally enable comparable studies for further German metropolitan areas as planned in the German project “Intergiertes Treibhausgas Monitoring System (ITMS)”. 

How to cite: Lüken-Winkels, C., Pilz, L., Cassiani, M., Pisso, I., and Vardag, S. N.: High-resolution inversion of Berlin city emissions – A synthetic study using FLEXPART-WRF for network optimization within ITMS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9996, https://doi.org/10.5194/egusphere-egu24-9996, 2024.

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