An Observing System Simulation Experiment Analysis of How Well Geostationary Satellite Trace‐Gas Observations Constrain NOx Emissions in the US

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 (TROPO...

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Bibliographic Details
Published in:Journal of geophysical research. Atmospheres 2024-01, Vol.129 (2), p.n/a
Main Authors: Hsu, Chia‐Hua, Henze, Daven K., Mizzi, Arthur P., González Abad, Gonzalo, He, Jian, Harkins, Colin, Naeger, Aaron R., Lyu, Congmeng, Liu, Xiong, Chan Miller, Christopher, Pierce, R. Bradley, Johnson, Matthew S., McDonald, Brian C.
Format: Article
Language:English
Subjects:
Air pollution
Air quality
Anthropogenic factors
Atmospheric chemistry
Best practice
Best practices
Bias
Biological assimilation
Constraining
COVID-19
Data assimilation
Data collection
Earth orbit
Earth orbits
Emission measurements
Emissions
Estimates
Forecasting models
Geostationary satellites
Low earth orbits
Mean square errors
Modelling
Monitoring instruments
Nitrogen
Nitrogen compounds
Nitrogen dioxide
Nitrogen oxides
Nitrogen oxides emissions
Ozone
Photochemicals
Pollution monitoring
Root-mean-square errors
Satellite observation
Satellites
Secondary aerosols
Simulation
Synchronous satellites
Temporal resolution
Troposphere
Tropospheric ozone
Urban areas
Weather forecasting
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Summary: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 assimi
ISSN:2169-897X
2169-8996
DOI:10.1029/2023JD039323