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An Observing System Simulation Experiment Analysis of How Well Geostationary Satellite Trace‐Gas Observations Constrain NO x Emissions in the US
Abstract We investigate the benefit of assimilating high spatial‐temporal resolution nitrogen dioxide (NO 2 ) measurements from a geostationary (GEO) instrument such as Tropospheric Emissions: Monitoring of Pollution (TEMPO) versus a low‐earth orbit (LEO) platform like TROPOspheric Monitoring Instru...
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| Published in: | Journal of geophysical research. Atmospheres 2024-01, Vol.129 (2) |
|---|---|
| Main Authors: | , , , , , , , , , , , , |
| Format: | Article |
| Language: | English |
| Citations: | Items that this one cites |
| Online Access: | Get full text |
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| Summary: | Abstract
We investigate the benefit of assimilating high spatial‐temporal resolution nitrogen dioxide (NO
2
) 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 (NO
x
) emissions. We generated synthetic TEMPO and TROPOMI NO
2
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 NO
x
emissions and quantify how well the assimilation of TEMPO versus TROPOMI measurements recovers the lockdown‐induced emissions changes. We find that NO
x
emission biases can be ameliorated using half as many simulation days when assimilating GEO observations, and the estimated NO
x
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 NO
x
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 NO
x
concentrations and emissions simultaneously yields the most accurate NO
x
emissions estimates. Our work serves as a valuable reference on how to appropriately assimilate GEO observations for constraining NO
x
emissions in future studies.
Plain Language Summary
Nitrogen oxides (NO
x
) are major air pollutants and precursors to tropospheric ozone and secondary inorganic aerosols. The diverse natural and anthropogenic sources of NO
x
pose a challenge for NO
x
emissions estimates. Inverse modeling techniques which use observations to infer emissions can be applied to improve our understanding of anthropogenic NO
x
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 NO
x
emissions. Synthetic TEMPO and TROPOMI NO
2
measureme |
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| ISSN: | 2169-897X 2169-8996 |
| DOI: | 10.1029/2023JD039323 |