I am mainly interested in the emission and transport of atmospheric greenhouse gases throughout the first part of their life, before they become a large scale background signal driving the slow shift of climate on our planet. I'm interested in both measurements and modelling. For the first part I'm using primarily in-situ techniques, for the second - a set of broad set of powerful models including WRF, WRF-GHG, STILT and Hysplit. While trying to do both measurements and modelling is certainly challenging, I find that it gives an illuminative perspective when trying to either explain the observed signals or to validate the modelled ones.
Since the beginning of my scientific career, I have been involved in greenhouse gas (GHG) measurement and modelling. My earlier scientific positions included responsibility for in situ measurements and data analysis of GHGs at several European GHG observatories, including large pan-European projects (e.g. InGOS). I have also applied Eulerian and Lagrangian transport models to help interpret and analyse in situ observations.
Since late 2017 I have had the privilege of being a member of Christoph Gerbig’s ATM Group at the Max Planck Institute for Biogeochemistry in Jena. I joined the team as a member of the AIRSPACE project, which aims to improve regional estimates of CO2 and CH4 emissions from a range of source types, by means of applying a robust observational campaign that utilized some of the most advanced techniques of GHG observations (including in-situ and remote sensing instruments), coupled to state-ofthe-
art modelling systems in support. My role in the first part of the project was to prepare and conduct in situ measurements of greenhouse gases aboard HALO aircraft during the 2018 CoMet 1.0 campaign. In the second part, my research involved the setup, development, and robust testing of an analytical inverse modeling framework based on a high-resolution mesoscale WRF model that was extended to include the capability to perform tagged tracer simulations of greenhouse gases that enabled improved estimation of CH4 and CO2 emissions from isolated or clustered point sources spread over the study area.