Our warm congratulations to Rasmus Nüß for the successful defence of his PhD thesis with the title Inversion of short-lived pollutants in the global atmosphere using remote sensing data.

The short-lived pollutant in question in Rasmus' PhD is carbon monoxide, a trace gas with a relatively short atmospheric lifetime in the order of a few months. Carbon monoxide adversely affects climate and air quality, by limiting the availability of hydroxyl radicals and by being a precursor to carbon dioxide and tropospheric ozone. One method for estimating the sources and distribution of trace gases is inverse modeling, where elaborate atmospheric chemistry models are combined with a plethora of input data, including remote sensing observations and boundary conditions for the model. In his work, Rasmus improves such an inverse modeling system, TM5-4dvar, by testing, updating, and revising most of its components. Most prominently, observations from a new satellite instrument, the TROPOspheric Monitoring Instrument (TROPOMI), are introduced into the system. TROPOMI features improved data quality and resolution compared to older satellite instruments, at the cost of an increased amount of data to be handled, which can become problematic in inverse modeling systems due to computational constraints. In the scope of his PhD, Rasmus developed methods for handling such datasets. Further, through multiple inversion experiments, the capabilities and limitations of the new observations are investigated. The results suggest that the carbon monoxide emissions, especially in the southern hemisphere, are well constrained by the TROPOMI observations. However, the inversion experiments also reveal biases in the optimized emissions, especially in the northern tropics. These biases are linked to an imbalanced prior budget, i.e. to the boundary conditions of the model before the observations are considered. The budget and the biases are improved in multiple steps, most notably by revising the assumed hydroxyl radical distribution and the meteorology. Overall, the updated inverse modeling system is capable of more accurately estimating carbon monoxide emissions.

Congratulations, Rasmus!