Capability demonstration of a JEDI-based system for TEMPO assimilation: system description and evaluation

The launch of the Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission in 2023 marked a new era in air quality monitoring by providing high-frequency, geostationary observations of column NO2 across most of North America. In this study, we present the first implementation of a TEMPO NO2 data assimilation system using the Joint Effort for Data assimilation Integration (JEDI) framework. Leveraging a four-dimensional ensemble variational (4DEnVar) approach and an Ensemble of Data Assimilations (EDA), we demonstrate a novel capability to assimilate hourly NO2 retrievals from TEMPO alongside polar-orbiting TROPOspheric Monitoring Instrument (TROPOMI) data into NASA’s GEOS Composition Forecast (GEOS-CF) model. The system is evaluated over the CONUS region for August 2023, using a suite of independent measurements including Pandora spectrometers, AirNow surface stations, and aircraft-based observations from Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and Synergistic TEMPO Air Quality Science (STAQS) field campaigns. Results show that the assimilation system successfully integrates geostationary NO2 observations, improves model performance in the column, and captures diurnal variability. However, assimilation also leads to systematic reductions in NO2 levels, which improves agreement with some datasets (e.g., Pandora, AEROMMA) but degrades comparisons with others (e.g., STAQS). These findings highlight the importance of joint evaluation across platforms and motivate further development of dual-concentration emission assimilation schemes. While the system imposes high computational costs, primarily from the forecast model, ongoing efforts to integrate AI-based model emulators offer a promising path toward scalable, real-time assimilation of geostationary atmospheric composition data.

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Published
2025-09-12
DOI
https://doi.org/10.22541/essoar.175767264.40932217/v1
Primary Topic
Atmospheric and Environmental Gas Dynamics
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article
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article

Capability demonstration of a JEDI-based system for TEMPO assimilation: system description and evaluation

Gonzalo González Abad, Benjamin T. Johnson, François Hébert, Maryam Abdi‐Oskouei et al.
Atmospheric and Environmental Gas Dynamics
article

Capability demonstration of a JEDI-based system for TEMPO assimilation: system description and evaluation

Gonzalo González Abad, Benjamin T. Johnson, François Hébert, Maryam Abdi‐Oskouei, Steven Vahl, Yannick Trémolet, Ronald Gelaro, Shih-Wei Wei, Laura Judd, Caroline R. Nowlan, Cory Martin, Kristen Zuraski, Benjamin Menetrier, Anna Shlyaeva, Steven Pawson, Eric J. Lingerfelt, K. Emma Knowland, Benjamin Ruston, Viral P. Shah, Ricardo Todling, Adam Griffin, Brian McDonald, Jeff Peischl, Clementine Gas, Fábio L.R. Diniz, Stephen Herbener, Christian Sampson, Cheng Dang, Cheng‐Hsuan Lu, Andrew W. Rollins, Dominikus Heinzeller, Daniel Holdaway, Thomas Frost Hanisco, Xiong Liu, Eleanor M. Waxman, Jérôme Barré, Evan Parker, Thomas Auligné
article en

Abstract

The launch of the Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission in 2023 marked a new era in air quality monitoring by providing high-frequency, geostationary observations of column NO2 across most of North America. In this study, we present the first implementation of a TEMPO NO2 data assimilation system using the Joint Effort for Data assimilation Integration (JEDI) framework. Leveraging a four-dimensional ensemble variational (4DEnVar) approach and an Ensemble of Data Assimilations (EDA), we demonstrate a novel capability to assimilate hourly NO2 retrievals from TEMPO alongside polar-orbiting TROPOspheric Monitoring Instrument (TROPOMI) data into NASA’s GEOS Composition Forecast (GEOS-CF) model. The system is evaluated over the CONUS region for August 2023, using a suite of independent measurements including Pandora spectrometers, AirNow surface stations, and aircraft-based observations from Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and Synergistic TEMPO Air Quality Science (STAQS) field campaigns. Results show that the assimilation system successfully integrates geostationary NO2 observations, improves model performance in the column, and captures diurnal variability. However, assimilation also leads to systematic reductions in NO2 levels, which improves agreement with some datasets (e.g., Pandora, AEROMMA) but degrades comparisons with others (e.g., STAQS). These findings highlight the importance of joint evaluation across platforms and motivate further development of dual-concentration emission assimilation schemes. While the system imposes high computational costs, primarily from the forecast model, ongoing efforts to integrate AI-based model emulators offer a promising path toward scalable, real-time assimilation of geostationary atmospheric composition data.

Goddard Space Flight Center (US), National Oceanic and Atmospheric Administration (US), Langley Research Center (US), University Corporation for Atmospheric Research (US), University at Albany, State University of New York (US), United Community Center (US), Center for Astrophysics Harvard & Smithsonian (US), National Aeronautics and Space Administration (US), NOAA Chemical Sciences Laboratory (US), NOAA Center for Satellite Applications and Research (US), Science Systems and Applications (United States) (US), NOAA Environmental Modeling Center, NOAA Earth System Research Laboratory (US), Morgan State University (US)
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Atmospheric and Environmental Gas Dynamics
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