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Capability Demonstration of a JEDI‐Based System for TEMPO Assimilation: System Description and Evaluation.

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Title: Capability Demonstration of a JEDI‐Based System for TEMPO Assimilation: System Description and Evaluation.
Authors: Abdi‐Oskouei, Maryam1,2,3 (AUTHOR) maryam.abdi.oskouei@gmail.com, Barré, Jérôme1,2,3 (AUTHOR), Wei, Shih‐Wei3,4,5 (AUTHOR), Lu, Sarah3,4 (AUTHOR), Griffin, Ashley3 (AUTHOR), Hardy Gas, Clementine3 (AUTHOR), Hebert, Francois3 (AUTHOR), Herbener, Stephen3 (AUTHOR), Lingerfelt, Eric3 (AUTHOR), Parker, Evan3 (AUTHOR), Sampson, Christian3 (AUTHOR), Vahl, Steve3 (AUTHOR), Diniz, Fabio3 (AUTHOR), Johnson, Ben3 (AUTHOR), Dang, Cheng3 (AUTHOR), Tremolet, Yannick3 (AUTHOR), Ruston, Benjamin3 (AUTHOR), Shah, Viral1,2 (AUTHOR), Knowland, Emma1,6 (AUTHOR), Todling, Ricardo1 (AUTHOR)
Source: Journal of Advances in Modeling Earth Systems. May2026, Vol. 18 Issue 5, p1-31. 31p.
Subject Terms: *Air quality monitoring, *Atmospheric chemistry, Data assimilation
Geographic Terms: North America
Company/Entity: United States. National Aeronautics & Space Administration
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 data sets (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. Plain Language Summary: The TEMPO instrument, launched in 2023, monitors air pollution over North America from Space. TEMPO is onboard a geostationary satellite and measures air pollutants, such as NO2, hourly during the day over North America. This high‐frequency data can help scientists better understand how air pollution changes throughout the day due to variations in emissions (such as traffic emissions) and weather conditions. In this study, we used advanced data assimilation (DA) techniques to enhance air quality model predictions based on TEMPO's hourly measurements of NO2. We used the Joint Effort for Data assimilation Integration (JEDI) framework, establishing its application in the atmospheric chemistry field. Since JEDI is generic, it allowed us to utilize an advanced DA technique with different observations with minimal changes to the base software. We then assessed the performance of the model after DA using independent ground‐based and airborne observations during two field experiments. We found that using TEMPO data helped the model better track NO2 levels, especially during the morning. This improvement happened on a smaller scale near the ground and in the afternoon. This highlights the complexity of blending different types of air pollution data and the need to run this experiment at a higher resolution. Key Points: Joint Effort for Data assimilation Integration was utilized to combine TEMPO, TROPOMI, ground‐based, and airborne measurements for assimilation and validation of GEOS‐CF NO2Assimilation introduced morning negative NO2 increments, which improved performance in rural regions and degraded it in urban areasAssimilation of NO2 indirectly improved the representation of ozone in GEOS‐CF [ABSTRACT FROM AUTHOR]
Copyright of Journal of Advances in Modeling Earth Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Capability Demonstration of a JEDI‐Based System for TEMPO Assimilation: System Description and Evaluation.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Advances+in+Modeling+Earth+Systems%22">Journal of Advances in Modeling Earth Systems</searchLink>. May2026, Vol. 18 Issue 5, p1-31. 31p.
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  Data: 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 data sets (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. Plain Language Summary: The TEMPO instrument, launched in 2023, monitors air pollution over North America from Space. TEMPO is onboard a geostationary satellite and measures air pollutants, such as NO2, hourly during the day over North America. This high‐frequency data can help scientists better understand how air pollution changes throughout the day due to variations in emissions (such as traffic emissions) and weather conditions. In this study, we used advanced data assimilation (DA) techniques to enhance air quality model predictions based on TEMPO's hourly measurements of NO2. We used the Joint Effort for Data assimilation Integration (JEDI) framework, establishing its application in the atmospheric chemistry field. Since JEDI is generic, it allowed us to utilize an advanced DA technique with different observations with minimal changes to the base software. We then assessed the performance of the model after DA using independent ground‐based and airborne observations during two field experiments. We found that using TEMPO data helped the model better track NO2 levels, especially during the morning. This improvement happened on a smaller scale near the ground and in the afternoon. This highlights the complexity of blending different types of air pollution data and the need to run this experiment at a higher resolution. Key Points: Joint Effort for Data assimilation Integration was utilized to combine TEMPO, TROPOMI, ground‐based, and airborne measurements for assimilation and validation of GEOS‐CF NO2Assimilation introduced morning negative NO2 increments, which improved performance in rural regions and degraded it in urban areasAssimilation of NO2 indirectly improved the representation of ozone in GEOS‐CF [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Advances in Modeling Earth Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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