Top‐Down Estimates of U.S. NO x Emissions Using TEMPO and TROPOMI NO 2 Remote Sensing Observations With WRF‐Chem/Chem‐DART

作者：Chia‐Hua Hsu, D. K. Henze, Arthur P. Mizzi, Colin Harkins, Congmeng Lyu, Owen R. Cooper, Rebecca H. Schwantes, Jian He, Meng Li, Siyuan Wang, Chelsea E. Stockwell, Carsten Warneke, Andrew W. Rollins, Eleanor M. Waxman, Kristen Zuraski, Jeff Peischl, Shobha Kondragunta, Fangjun Li, Chuanyu Xu, R. Bradley Pierce, Gonzalo González Abad, Caroline R. Nowlan, Xiong Liu, Brian McDonald · 发表于：Journal of Geophysical Research Atmospheres · 年份：2026 · DOI：10.1029/2025jd044223 · 被引用次数：3 · 研究领域：Atmospheric and Environmental Gas Dynamics、Atmospheric Ozone and Climate、Atmospheric chemistry and aerosols

Abstract The operation of geostationary (GEO) instruments such as the Tropospheric Emissions: Monitoring of Pollution (TEMPO) provides unprecedented hourly nitrogen dioxide (NO 2 ) observations compared to the once‐daily data from a low‐Earth orbit (LEO) platform like the TROPOspheric Monitoring Instrument (TROPOMI). This study investigates the performance and challenges of using TEMPO versus TROPOMI measurements to constrain anthropogenic nitrogen oxides (NO x ) emissions. The accuracy of TEMPO and TROPOMI NO 2 tropospheric columns are assessed using Pandora observations, finding a low bias of 9%–12.3% in TEMPO, and TROPOMI data during August 2023, while TEMPO midday and late afternoon observations are less of low bias. Top‐down NO x emissions derived by midday TEMPO and TROPOMI data are generally consistent over urban areas, being 5%–20% lower than bottom‐up emissions provided by the 2021 GReenhouse gas And Air Pollutants Emissions System (GRA 2 PES), and align with 2023 GRA 2 PES emissions, demonstrating the reliability of using satellite data for timely updates of bottom‐up inventories. However, assimilating additional morning/late afternoon TEMPO data leads to the poorest top‐down NO x emissions, likely resulting from larger negative measurement biases. NO x emission inversions effectively mitigate NO x overprediction, though the top‐down NO x emissions might be over‐corrected in urban cores. NO x emissions optimization also improves ozone forecasts by reducing the model...