Lightning-NOx Emissions, Impacts, and Evaluation Using Satellite Data Assimilation and Remote Observations

Lightning emissions are a major source of nitrogen oxides (NOx = NO + NO2) in the troposphere and contribute to ozone formation. Accurately simulating lightning NOx (LNOx) emissions in air quality models is important to ensure a more accurate representation of background tropospheric composition. However, there is disagreement among different LNOx emissions datasets commonly used in modeling, leading to differences in simulated tropospheric composition. To reconcile differences between available LNOx emissions datasets, we apply TROPOMI retrievals of NO2 in a chemical data assimilation system to constrain various LNOx emissions estimates. In the satellite data assimilation system, we assimilate NO2 retrievals in the CMAQ model over the Northern Hemisphere, and then scale LNOx emissions in a finite-difference mass-balance (FDMB) inversion based on the difference between CMAQ with and without assimilated NO2. The system produces inferences of LNOx emissions, constrained by satellite observations. We apply the system to four LNOx emissions cases: (1) climatological emissions from the Global Emissions InitiAtive (GEIA), (2) emissions derived from lightning flash observations in the World Wide Lightning Location Network (WWLLN), (3) WWLLN-derived emissions scaled using scaling factors derived from the National Lightning Detection Network, and (4) LNOx emissions produced in the GEOS-Chem modeling platform. In this presentation, we compare the satellite-based inferences to each LNOx dataset. Then, we focus on evaluating model simulated NO2 and O3 against sonde and aircraft campaign measurements, characterizing the effect of inferred LNOx emissions on model performance and on simulated air quality.