NASA - National Aeronautics and Space Administration

We investigate the benefit of assimilating high spatial-temporal resolution nitrogen dioxide (NO2) measurements from a geostationary (GEO) instrument such as Tropospheric Emissions: Monitoring of Pollution (TEMPO) versus a low-earth orbit (LEO) platform like TROPOspheric Monitoring Instrument (TROPOMI) on the inverse modeling of nitrogen oxides (NOx) emissions. We generated synthetic TEMPO and TROPOMI NO2 measurements based on emissions from the COVID-19 lockdown period. Starting with emissions levels prior to the lockdown, we use the Weather Research and Forecasting Model coupled with Chemistry/Data Assimilation Research Testbed (WRF-Chem/DART) to assimilate these pseudo-observations in Observing System Simulation Experiments to adjust NOx emissions and quantify how well the assimilation of TEMPO versus TROPOMI measurements recovers the lockdown-induced emissions changes. We find that NOx emission biases can be ameliorated using half as many simulation days when assimilating GEO observations, and the estimated NOx emissions in 23 out of 29 major urban regions in the US are more accurate. The root mean square error and coefficient of determination of posterior NOx emissions are reduced by 12.5%–41.5% and 1.5%–17.1%, respectively, across different regions. We conduct sensitivity experiments that use different data assimilation (DA) configurations to assimilate synthetic GEO observations. Results demonstrate that the temporal width of the DA window introduces −10% to −20% biases in the emissions inversion and constraining both NOx concentrations and emissions simultaneously yields the most accurate NOx emissions estimates. Our work serves as a valuable reference on how to appropriately assimilate GEO observations for constraining NOx emissions in future studies. Plain Language Summary Nitrogen oxides (NOx) are major air pollutants and precursors to tropospheric ozone and secondary inorganic aerosols. The diverse natural and anthropogenic sources of NOx pose a challenge for NOx emissions estimates. Inverse modeling techniques which use observations to infer emissions can be applied to improve our understanding of anthropogenic NOx emissions. This study aims to compare the ability of the new geostationary (GEO) instrument Tropospheric Emissions: Monitoring of Pollution (TEMPO) and the existing low-earth orbit instrument TROPOspheric Monitoring Instrument (TROPOMI) to constrain NOx emissions. Synthetic TEMPO and TROPOMI NO2 measurements are generated and assimilated to constrain NOx emissions in an idealized experiment in which the “true” emissions are known. The results show the true NOx emissions can be retrieved using half as many simulation days when assimilating GEO NO2 observations. Moreover, the experiment that assimilates GEO NO2 observations improves the accuracy of estimated NOx emissions by 12.5%–41.5% and 1.5%–17.1% in terms of root mean square error and coefficient of determination, respectively, across different air quality regions. The NOx emissions in most urban regions are better constrained when assimilating GEO NO2 data. We also propose best practices for assimilating GEO NO2 observations, which can serve as reference for future research.