Abstract ID: 3.10898 | Accepted as Poster | Poster | TBA | TBA

The increasing concentration of methane (CH₄) in the atmosphere represents one of the most pressing challenges for climate change mitigation due to its high global warming potential and significant contribution to the greenhouse effect. The Alpine chain, characterized by its complex orography and significant climatic variability, serves as a key region for the atmospheric transport, accumulation, and redistribution of CH₄ across Europe. To enhance our understanding of the spatial and temporal distribution of CH₄ in this area, we performed numerical simulations using the WRF-Chem model with the WRF-GHG chemical module. The WRF-GHG module is specifically designed for greenhouse gas modeling and treats gases such as CH₄ as passive tracers. This approach assumes that CH₄ does not undergo chemical reactions within the atmosphere, focusing on its transport and dispersion influenced by meteorological conditions, thereby reducing computational complexity while maintaining accuracy in capturing spatial and temporal dynamics. The model combined the Emissions Database for Global Atmospheric Research (EDGAR-2024_GHG) inventory, background data provided by the Whole Atmosphere Community Climate Model (WACCM) database, and biomass burning emissions from the Fire INventory from NCAR (FINN) database. The simulation was conducted for the entire year 2022, with a 7-day spin-up run, to properly initialize the atmospheric conditions. We employed two nested domains: the outer domain with a spatial resolution of 10 km and the inner domain with a resolution of 5 km. This nesting strategy allowed for a detailed representation of atmospheric processes within the Alpine chain while accounting for broader regional influences, with the innermost domain also including part of central Europe. Model outputs were compared with satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI) sensor onboard Sentinel-5P, and ground-based measurements from Integrated Carbon Observation System (ICOS) stations for further validation, assessing the reliability of simulated CH₄ concentrations and their seasonal trends. As part of the PRIN-MVP project, the annual dataset generated through these simulations will serve as a critical input for validating a physics-informed neural network-based retrieval system for CH₄, further advancing the capability to monitor and mitigate greenhouse gas emissions.