A semi-operational near-real-time Modelling Infrastructure for assessing GHG emissions in Munich using WRF-GHG

Since the establishment of the firstly fully-automatic urban greenhouse gas (GHG) measurement network in Munich in 2019 [1], we are building a high-resolution modeling infrastructure which will be the basis for a quantitative understanding of the processes responsible for the emission and consumption of CO₂, CH₄, and CO in Munich. The results of our near-real-time modeling are expected to also provide guidance for local emission reduction strategies.

The precision of our transport modeling framework is assessed through comparison with surface and column measurements in August and October 2018, and the contributions from different emissions tracers are quantified to understand the sources and sinks of atmospheric carbon in the Munich area. A differential column approach [3] is applied for comparing models to observations independently of the biases from background concentration fields (Zhao, X. et al, 2019). Various tracers are separately included in the simulation to further analyze the contribution from different emission processes (e.g., biogenic emissions from wetlands, fossil fuel emissions, and biofuel emissions). Surface emissions are taken from TNO-GHGco v1.1 emission inventory at a resolution of 1 km (cf. Super, I. et al, 2019). Biogenic fluxes of CO₂ are calculated online using the diagnostic VPRM model driven by MODIS indices. The initial and lateral tracer boundary conditions are implemented using Copernicus Atmosphere Monitoring Service (CAMS) data, with a spatial resolution of 0.8° on 137 vertical levels, with a temporal resolution of 6 hours.

The precision of our transport modeling framework is assessed through comparison with surface and column measurements in August and October 2018, and the contributions from different emissions tracers are quantified to understand the sources and sinks of atmospheric carbon in the Munich area. A differential column approach [3] is applied for comparing models to observations independently of the biases from background concentration fields (Zhao, X. et al, 2019).

[1] Dietrich, F. et al.: First fully-automated differential column network for measuring GHG emissions tested in Munich. In Geophysical Research Abstracts. 2019.

[2] Zhao, X. et al.: Analysis of total column CO₂ and CH₄ measurements in Berlin with WRF-GHG, Atmos. Chem. Phys., 19, 11279–11302, https://doi.org/10.5194/acp-19-11279-2019, 2019. 

[3] Chen, J. et al: Differential column measurements using compact solar-tracking spectrometers, Atmos. Chem. Phys., 16, 8479–8498, https://doi.org/10.5194/acp-16-8479-2016, 2016.