Articles | Volume 15, issue 15
https://doi.org/10.5194/amt-15-4489-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-4489-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Polarization performance simulation for the GeoXO atmospheric composition instrument: NO2 retrieval impacts
Aaron Pearlman
CORRESPONDING AUTHOR
GeoThinkTank LLC, Miami, FL, USA
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Monica Cook
GeoThinkTank LLC, Miami, FL, USA
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Boryana Efremova
GeoThinkTank LLC, Miami, FL, USA
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Francis Padula
GeoThinkTank LLC, Miami, FL, USA
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Lok Lamsal
Goddard Earth Sciences Technology and Research (GESTAR II), University of Maryland Baltimore County (UMBC), Baltimore, MD, USA
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Joel McCorkel
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Joanna Joiner
NASA Goddard Space Flight Center, Greenbelt, MD, USA
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Carley D. Fredrickson, Scott J. Janz, Lok N. Lamsal, Ursula A. Jongebloed, Joshua L. Laughner, and Joel A. Thornton
Atmos. Meas. Tech., 18, 3669–3689, https://doi.org/10.5194/amt-18-3669-2025, https://doi.org/10.5194/amt-18-3669-2025, 2025
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We present an analysis of high-resolution remote sensing measurements of nitrogen-containing trace gases emitted by wildfires. The measurements were made using an instrument on the NASA ER-2 aircraft in the summer of 2019. We find that time-resolved fire intensity is critical to quantify trace gas emissions over a fire's entire lifespan. These findings have implications for improving air pollution forecasts downwind of wildfires using computer models of atmospheric chemistry and meteorology.
Chris A. McLinden, Debora Griffin, Vitali Fioletov, Junhua Zhang, Enrico Dammers, Cristen Adams, Mallory Loria, Nickolay Krotkov, and Lok N. Lamsal
Atmos. Chem. Phys., 25, 6093–6120, https://doi.org/10.5194/acp-25-6093-2025, https://doi.org/10.5194/acp-25-6093-2025, 2025
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The Ozone Monitoring Instrument (OMI) was used to understand the evolution of NOx emissions from the Canadian oil sands. OMI NO2 combined with winds and reported stack emissions found emissions from the heavy-hauler mine fleet have remained flat since 2005, whereas the total oil sands mined have more than doubled. This difference is a result of emissions standards that limit NOx emissions becoming more stringent over this period, confirming the efficacy of the policy enacting these standards.
Bryan N. Duncan, Daniel C. Anderson, Arlene M. Fiore, Joanna Joiner, Nickolay A. Krotkov, Can Li, Dylan B. Millet, Julie M. Nicely, Luke D. Oman, Jason M. St. Clair, Joshua D. Shutter, Amir H. Souri, Sarah A. Strode, Brad Weir, Glenn M. Wolfe, Helen M. Worden, and Qindan Zhu
Atmos. Chem. Phys., 24, 13001–13023, https://doi.org/10.5194/acp-24-13001-2024, https://doi.org/10.5194/acp-24-13001-2024, 2024
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Trace gases emitted to or formed within the atmosphere may be chemically or physically removed from the atmosphere. One trace gas, the hydroxyl radical (OH), is responsible for initiating the chemical removal of many trace gases, including some greenhouse gases. Despite its importance, scientists have not been able to adequately measure OH. In this opinion piece, we discuss promising new methods to indirectly constrain OH using satellite data of trace gases that control the abundance of OH.
Deepangsu Chatterjee, Randall V. Martin, Chi Li, Dandan Zhang, Haihui Zhu, Daven K. Henze, James H. Crawford, Ronald C. Cohen, Lok N. Lamsal, and Alexander M. Cede
Atmos. Chem. Phys., 24, 12687–12706, https://doi.org/10.5194/acp-24-12687-2024, https://doi.org/10.5194/acp-24-12687-2024, 2024
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We investigate the hourly variation of NO2 columns and surface concentrations by applying the GEOS-Chem model to interpret aircraft and ground-based measurements over the US and Pandora sun photometer measurements over the US, Europe, and Asia. Corrections to the Pandora columns and finer model resolution improve the modeled representation of the summertime hourly variation of total NO2 columns to explain the weaker hourly variation in NO2 columns than at the surface.
Can Li, Nickolay A. Krotkov, Joanna Joiner, Vitali Fioletov, Chris McLinden, Debora Griffin, Peter J. T. Leonard, Simon Carn, Colin Seftor, and Alexander Vasilkov
Earth Syst. Sci. Data, 16, 4291–4309, https://doi.org/10.5194/essd-16-4291-2024, https://doi.org/10.5194/essd-16-4291-2024, 2024
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Sulfur dioxide (SO2), a poisonous gas from human activities and volcanoes, causes air pollution, acid rain, and changes to climate and the ozone layer. Satellites have been used to monitor SO2 globally, including remote areas. Here we describe a new satellite SO2 dataset from the OMPS instrument that flies on the N20 satellite. Results show that the new dataset agrees well with the existing ones from other satellites and can help to continue the global monitoring of SO2 from space.
Fei Liu, Steffen Beirle, Joanna Joiner, Sungyeon Choi, Zhining Tao, K. Emma Knowland, Steven J. Smith, Daniel Q. Tong, Siqi Ma, Zachary T. Fasnacht, and Thomas Wagner
Atmos. Chem. Phys., 24, 3717–3728, https://doi.org/10.5194/acp-24-3717-2024, https://doi.org/10.5194/acp-24-3717-2024, 2024
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Using satellite data, we developed a coupled method independent of the chemical transport model to map NOx emissions across US cities. After validating our technique with synthetic data, we charted NOx emissions from 2018–2021 in 39 cities. Our results closely matched EPA estimates but also highlighted some inconsistencies in both magnitude and spatial distribution. This research can help refine strategies for monitoring and managing air quality.
Tianle Yuan, Fei Liu, Lok N. Lamsal, and Hua Song
EGUsphere, https://doi.org/10.22541/essoar.168771101.14987378/v1, https://doi.org/10.22541/essoar.168771101.14987378/v1, 2023
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We train and apply a state-of-the-art deep learning model to detect NO2 plumes emitted by ships using NO2 retrievals from TROPOMI. By applying the model, we can detect individual plumes with excellent fidelity. The aggregated data show major shipping routes, but miss other routes. The missing routes are due to high cloudiness. Our method can be potentially useful for monitoring ship emissions of NOx and verifying compliance of emission standards.
Vitali E. Fioletov, Chris A. McLinden, Debora Griffin, Nickolay A. Krotkov, Can Li, Joanna Joiner, Nicolas Theys, and Simon Carn
Atmos. Meas. Tech., 16, 5575–5592, https://doi.org/10.5194/amt-16-5575-2023, https://doi.org/10.5194/amt-16-5575-2023, 2023
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Snow-covered terrain, with its high reflectance in the UV, typically enhances satellite sensitivity to boundary layer pollution. However, a significant fraction of high-quality cloud-free measurements over snow is currently excluded from analyses. In this study, we investigated how satellite SO2 measurements over snow-covered surfaces can be used to improve estimations of annual SO2 emissions.
Laura Hyesung Yang, Daniel J. Jacob, Nadia K. Colombi, Shixian Zhai, Kelvin H. Bates, Viral Shah, Ellie Beaudry, Robert M. Yantosca, Haipeng Lin, Jared F. Brewer, Heesung Chong, Katherine R. Travis, James H. Crawford, Lok N. Lamsal, Ja-Ho Koo, and Jhoon Kim
Atmos. Chem. Phys., 23, 2465–2481, https://doi.org/10.5194/acp-23-2465-2023, https://doi.org/10.5194/acp-23-2465-2023, 2023
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A geostationary satellite can now provide hourly NO2 vertical columns, and obtaining the NO2 vertical columns from space relies on NO2 vertical distribution from the chemical transport model (CTM). In this work, we update the CTM to better represent the chemistry environment so that the CTM can accurately provide NO2 vertical distribution. We also find that the changes in NO2 vertical distribution driven by a change in mixing depth play an important role in the NO2 column's diurnal variation.
Amir H. Souri, Matthew S. Johnson, Glenn M. Wolfe, James H. Crawford, Alan Fried, Armin Wisthaler, William H. Brune, Donald R. Blake, Andrew J. Weinheimer, Tijl Verhoelst, Steven Compernolle, Gaia Pinardi, Corinne Vigouroux, Bavo Langerock, Sungyeon Choi, Lok Lamsal, Lei Zhu, Shuai Sun, Ronald C. Cohen, Kyung-Eun Min, Changmin Cho, Sajeev Philip, Xiong Liu, and Kelly Chance
Atmos. Chem. Phys., 23, 1963–1986, https://doi.org/10.5194/acp-23-1963-2023, https://doi.org/10.5194/acp-23-1963-2023, 2023
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We have rigorously characterized different sources of error in satellite-based HCHO / NO2 tropospheric columns, a widely used metric for diagnosing near-surface ozone sensitivity. Specifically, the errors were categorized/quantified into (i) an inherent chemistry error, (ii) the decoupled relationship between columns and the near-surface concentration, (iii) the spatial representativeness error of ground satellite pixels, and (iv) the satellite retrieval errors.
Joanna Joiner, Sergey Marchenko, Zachary Fasnacht, Lok Lamsal, Can Li, Alexander Vasilkov, and Nickolay Krotkov
Atmos. Meas. Tech., 16, 481–500, https://doi.org/10.5194/amt-16-481-2023, https://doi.org/10.5194/amt-16-481-2023, 2023
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Nitrogen dioxide (NO2) is an important trace gas for both air quality and climate. NO2 affects satellite ocean color products. A new ocean color instrument – OCI (Ocean Color Instrument) – will be launched in 2024 on a NASA satellite. We show that it will be possible to measure NO2 from OCI even though it was not designed for this. The techniques developed here, based on machine learning, can also be applied to instruments already in space to speed up algorithms and reduce the effects of noise.
Viral Shah, Daniel J. Jacob, Ruijun Dang, Lok N. Lamsal, Sarah A. Strode, Stephen D. Steenrod, K. Folkert Boersma, Sebastian D. Eastham, Thibaud M. Fritz, Chelsea Thompson, Jeff Peischl, Ilann Bourgeois, Ilana B. Pollack, Benjamin A. Nault, Ronald C. Cohen, Pedro Campuzano-Jost, Jose L. Jimenez, Simone T. Andersen, Lucy J. Carpenter, Tomás Sherwen, and Mat J. Evans
Atmos. Chem. Phys., 23, 1227–1257, https://doi.org/10.5194/acp-23-1227-2023, https://doi.org/10.5194/acp-23-1227-2023, 2023
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NOx in the free troposphere (above 2 km) affects global tropospheric chemistry and the retrieval and interpretation of satellite NO2 measurements. We evaluate free tropospheric NOx in global atmospheric chemistry models and find that recycling NOx from its reservoirs over the oceans is faster than that simulated in the models, resulting in increases in simulated tropospheric ozone and OH. Over the U.S., free tropospheric NO2 contributes the majority of the tropospheric NO2 column in summer.
Vitali E. Fioletov, Chris A. McLinden, Debora Griffin, Ihab Abboud, Nickolay Krotkov, Peter J. T. Leonard, Can Li, Joanna Joiner, Nicolas Theys, and Simon Carn
Earth Syst. Sci. Data, 15, 75–93, https://doi.org/10.5194/essd-15-75-2023, https://doi.org/10.5194/essd-15-75-2023, 2023
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Sulfur dioxide (SO2) measurements from three satellite instruments were used to update and extend the previously developed global catalogue of large SO2 emission sources. This version 2 of the global catalogue covers the period of 2005–2021 and includes a total of 759 continuously emitting point sources. The catalogue data show an approximate 50 % decline in global SO2 emissions between 2005 and 2021, although emissions were relatively stable during the last 3 years.
Can Li, Joanna Joiner, Fei Liu, Nickolay A. Krotkov, Vitali Fioletov, and Chris McLinden
Atmos. Meas. Tech., 15, 5497–5514, https://doi.org/10.5194/amt-15-5497-2022, https://doi.org/10.5194/amt-15-5497-2022, 2022
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Satellite observations provide information on the sources of SO2, an important pollutant that affects both air quality and climate. However, these observations suffer from relatively poor data quality due to weak signals of SO2. Here, we use a machine learning technique to analyze satellite SO2 observations in order to reduce the noise and artifacts over relatively clean areas while keeping the signals near pollution sources. This leads to significant improvement in satellite SO2 data.
Fei Liu, Zhining Tao, Steffen Beirle, Joanna Joiner, Yasuko Yoshida, Steven J. Smith, K. Emma Knowland, and Thomas Wagner
Atmos. Chem. Phys., 22, 1333–1349, https://doi.org/10.5194/acp-22-1333-2022, https://doi.org/10.5194/acp-22-1333-2022, 2022
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In this work, we present a novel method to infer NOx emissions and lifetimes based on tropospheric NO2 observations together with reanalysis wind fields for cities located in polluted backgrounds. We evaluate the accuracy of the method using synthetic NO2 observations derived from a high-resolution model simulation. Our work provides an estimate for uncertainties in satellite-derived emissions inferred from chemical transport model (CTM)-independent approaches.
Nick Gorkavyi, Nickolay Krotkov, Can Li, Leslie Lait, Peter Colarco, Simon Carn, Matthew DeLand, Paul Newman, Mark Schoeberl, Ghassan Taha, Omar Torres, Alexander Vasilkov, and Joanna Joiner
Atmos. Meas. Tech., 14, 7545–7563, https://doi.org/10.5194/amt-14-7545-2021, https://doi.org/10.5194/amt-14-7545-2021, 2021
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The 21 June 2019 eruption of the Raikoke volcano produced significant amounts of volcanic aerosols (sulfate and ash) and sulfur dioxide (SO2) gas that penetrated into the lower stratosphere. We showed that the amount of SO2 decreases with a characteristic period of 8–18 d and the peak of sulfate aerosol lags the initial peak of SO2 by 1.5 months. We also examined the dynamics of an unusual stratospheric coherent circular cloud of SO2 and aerosol observed from 18 July to 22 September 2019.
Luis Guanter, Cédric Bacour, Andreas Schneider, Ilse Aben, Tim A. van Kempen, Fabienne Maignan, Christian Retscher, Philipp Köhler, Christian Frankenberg, Joanna Joiner, and Yongguang Zhang
Earth Syst. Sci. Data, 13, 5423–5440, https://doi.org/10.5194/essd-13-5423-2021, https://doi.org/10.5194/essd-13-5423-2021, 2021
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Sun-induced chlorophyll fluorescence (SIF) is an electromagnetic signal emitted by plants in the red and far-red parts of the spectrum. It has a functional link to photosynthesis and can be measured by satellite instruments, which makes it an important variable for the remote monitoring of the photosynthetic activity of vegetation ecosystems around the world. In this contribution we present a SIF dataset derived from the new Sentinel-5P TROPOMI missions.
Siqi Ma, Daniel Tong, Lok Lamsal, Julian Wang, Xuelei Zhang, Youhua Tang, Rick Saylor, Tianfeng Chai, Pius Lee, Patrick Campbell, Barry Baker, Shobha Kondragunta, Laura Judd, Timothy A. Berkoff, Scott J. Janz, and Ivanka Stajner
Atmos. Chem. Phys., 21, 16531–16553, https://doi.org/10.5194/acp-21-16531-2021, https://doi.org/10.5194/acp-21-16531-2021, 2021
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Predicting high ozone gets more challenging as urban emissions decrease. How can different techniques be used to foretell the quality of air to better protect human health? We tested four techniques with the CMAQ model against observations during a field campaign over New York City. The new system proves to better predict the magnitude and timing of high ozone. These approaches can be extended to other regions to improve the predictability of high-O3 episodes in contemporary urban environments.
Jianfeng Li, Yuhang Wang, Ruixiong Zhang, Charles Smeltzer, Andrew Weinheimer, Jay Herman, K. Folkert Boersma, Edward A. Celarier, Russell W. Long, James J. Szykman, Ruben Delgado, Anne M. Thompson, Travis N. Knepp, Lok N. Lamsal, Scott J. Janz, Matthew G. Kowalewski, Xiong Liu, and Caroline R. Nowlan
Atmos. Chem. Phys., 21, 11133–11160, https://doi.org/10.5194/acp-21-11133-2021, https://doi.org/10.5194/acp-21-11133-2021, 2021
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Comprehensive evaluations of simulated diurnal cycles of NO2 and NOy concentrations, vertical profiles, and tropospheric vertical column densities at two different resolutions with various measurements during the DISCOVER-AQ 2011 campaign show potential distribution biases of NOx emissions in the National Emissions Inventory 2011 at both 36 and 4 km resolutions, providing another possible explanation for the overestimation of model results.
Anam M. Khan, Paul C. Stoy, James T. Douglas, Martha Anderson, George Diak, Jason A. Otkin, Christopher Hain, Elizabeth M. Rehbein, and Joel McCorkel
Biogeosciences, 18, 4117–4141, https://doi.org/10.5194/bg-18-4117-2021, https://doi.org/10.5194/bg-18-4117-2021, 2021
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Remote sensing has played an important role in the study of land surface processes. Geostationary satellites, such as the GOES-R series, can observe the Earth every 5–15 min, providing us with more observations than widely used polar-orbiting satellites. Here, we outline current efforts utilizing geostationary observations in environmental science and look towards the future of GOES observations in the carbon cycle, ecosystem disturbance, and other areas of application in environmental science.
Wenfu Tang, David P. Edwards, Louisa K. Emmons, Helen M. Worden, Laura M. Judd, Lok N. Lamsal, Jassim A. Al-Saadi, Scott J. Janz, James H. Crawford, Merritt N. Deeter, Gabriele Pfister, Rebecca R. Buchholz, Benjamin Gaubert, and Caroline R. Nowlan
Atmos. Meas. Tech., 14, 4639–4655, https://doi.org/10.5194/amt-14-4639-2021, https://doi.org/10.5194/amt-14-4639-2021, 2021
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We use high-resolution airborne mapping spectrometer measurements to assess sub-grid variability within satellite pixels over urban regions. The sub-grid variability within satellite pixels increases with increasing satellite pixel sizes. Temporal variability within satellite pixels decreases with increasing satellite pixel sizes. This work is particularly relevant and useful for future satellite design, satellite data interpretation, and point-grid data comparisons.
Alexander Vasilkov, Nickolay Krotkov, Eun-Su Yang, Lok Lamsal, Joanna Joiner, Patricia Castellanos, Zachary Fasnacht, and Robert Spurr
Atmos. Meas. Tech., 14, 2857–2871, https://doi.org/10.5194/amt-14-2857-2021, https://doi.org/10.5194/amt-14-2857-2021, 2021
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To explicitly account for aerosol effects in the OMI cloud and nitrogen dioxide algorithms, we use a model of aerosol optical properties from a global aerosol assimilation system and radiative transfer computations. Accounting for anisotropic reflection of Earth's surface is an important feature of the approach. Comparisons of the cloud and tropospheric nitrogen dioxide retrievals with implicit and explicit aerosol corrections are carried out for a selected area with high pollution.
Eloise A. Marais, John F. Roberts, Robert G. Ryan, Henk Eskes, K. Folkert Boersma, Sungyeon Choi, Joanna Joiner, Nader Abuhassan, Alberto Redondas, Michel Grutter, Alexander Cede, Laura Gomez, and Monica Navarro-Comas
Atmos. Meas. Tech., 14, 2389–2408, https://doi.org/10.5194/amt-14-2389-2021, https://doi.org/10.5194/amt-14-2389-2021, 2021
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Nitrogen oxides in the upper troposphere have a profound influence on the global troposphere, but routine reliable observations there are exceedingly rare. We apply cloud-slicing to TROPOMI total columns of nitrogen dioxide (NO2) at high spatial resolution to derive near-global observations of NO2 in the upper troposphere and show consistency with existing datasets. These data offer tremendous potential to address knowledge gaps in this oft underappreciated portion of the atmosphere.
Junjie Liu, Latha Baskaran, Kevin Bowman, David Schimel, A. Anthony Bloom, Nicholas C. Parazoo, Tomohiro Oda, Dustin Carroll, Dimitris Menemenlis, Joanna Joiner, Roisin Commane, Bruce Daube, Lucianna V. Gatti, Kathryn McKain, John Miller, Britton B. Stephens, Colm Sweeney, and Steven Wofsy
Earth Syst. Sci. Data, 13, 299–330, https://doi.org/10.5194/essd-13-299-2021, https://doi.org/10.5194/essd-13-299-2021, 2021
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On average, the terrestrial biosphere carbon sink is equivalent to ~ 20 % of fossil fuel emissions. Understanding where and why the terrestrial biosphere absorbs carbon from the atmosphere is pivotal to any mitigation policy. Here we present a regionally resolved satellite-constrained net biosphere exchange (NBE) dataset with corresponding uncertainties between 2010–2018: CMS-Flux NBE 2020. The dataset provides a unique perspective on monitoring regional contributions to the CO2 growth rate.
Nick Gorkavyi, Zachary Fasnacht, David Haffner, Sergey Marchenko, Joanna Joiner, and Alexander Vasilkov
Atmos. Meas. Tech., 14, 961–974, https://doi.org/10.5194/amt-14-961-2021, https://doi.org/10.5194/amt-14-961-2021, 2021
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Various instrumental or geophysical artifacts, such as saturation, stray light or obstruction of light, negatively impact satellite measured ultraviolet and visible Earthshine radiance spectra. Here, we introduce a straightforward detection method that is based on the correlation, r, between the observed Earthshine radiance and solar irradiance spectra over a 10 nm spectral range; our decorrelation index (DI for brevity) is simply defined as DI of 1–r.
Lok N. Lamsal, Nickolay A. Krotkov, Alexander Vasilkov, Sergey Marchenko, Wenhan Qin, Eun-Su Yang, Zachary Fasnacht, Joanna Joiner, Sungyeon Choi, David Haffner, William H. Swartz, Bradford Fisher, and Eric Bucsela
Atmos. Meas. Tech., 14, 455–479, https://doi.org/10.5194/amt-14-455-2021, https://doi.org/10.5194/amt-14-455-2021, 2021
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The NASA standard nitrogen dioxide (NO2) version 4.0 product for OMI Aura incorporates the most salient improvements. It represents the first global satellite trace gas retrieval with OMI–MODIS synergy accounting for surface reflectance anisotropy in cloud and NO2 retrievals. Improved spectral fitting procedures for NO2 and oxygen dimer (for cloud) retrievals and reliance on high-resolution field-of-view-specific input information for NO2 and cloud retrievals help enhance the NO2 data quality.
Can Li, Nickolay A. Krotkov, Peter J. T. Leonard, Simon Carn, Joanna Joiner, Robert J. D. Spurr, and Alexander Vasilkov
Atmos. Meas. Tech., 13, 6175–6191, https://doi.org/10.5194/amt-13-6175-2020, https://doi.org/10.5194/amt-13-6175-2020, 2020
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Sulfur dioxide (SO2) is an important pollutant that causes haze and acid rain. The Ozone Monitoring Instrument (OMI) has been providing global observation of SO2 from space for over 15 years. In this paper, we introduce a new OMI SO2 dataset for global pollution monitoring. The dataset better accounts for the influences of different factors such as location and sun and satellite angles, leading to improved data quality. The new OMI SO2 dataset is publicly available through NASA's data center.
Cited articles
Acarreta, J. R., Haan, J. F. D., and Stammes, P.: Cloud pressure retrieval using the O2–O2 absorption band at 477 nm, J. Geophys. Res., 109, D05204, https://doi.org/10.1029/2003jd003915, 2004. a
Bak, J., Liu, X., Kim, J.-H., Haffner, D. P., Chance, K., Yang, K., and Sun, K.: Characterization and correction of OMPS nadir mapper measurements for ozone profile retrievals, Atmos. Meas. Tech., 10, 4373–4388, https://doi.org/10.5194/amt-10-4373-2017, 2017. a
Baldridge, A., Hook, S., Grove, C., and Rivera, G.: The ASTER spectral library version 2.0, Remote Sens. Environ., 113, 711–715, https://doi.org/10.1016/j.rse.2008.11.007, 2009. a
Bézy, J.-L., Bazalgette, G., Sierk, B., Meynart, R., Caron, J. C., Richert, M., and Loiseaux, D.: Polarization scramblers in Earth observing spectrometers: lessons learned from Sentinel-4 and 5 phases A/B1, in: International Conference on Space Optics – ICSO 2012, edited by: Armandillo, E., Karafolas, N., and Cugny, B., SPIE, https://doi.org/10.1117/12.2309044, 2017. a
Boersma, K. F., Eskes, H. J., Veefkind, J. P., Brinksma, E. J., van der A, R. J., Sneep, M., van den Oord, G. H. J., L
evelt, P. F., Stammes, P., Gleason, J. F., and Bucsela, E. J.: Near-real time retrieval of tropospheric NO2 from OMI, Atmos. Chem. Phys., 7, 2103–2118, https://doi.org/10.5194/acp-7-2103-2007, 2007. a
Bovensmann, H., Burrows, J. P., Buchwitz, M., Frerick, J., Noël, S., Rozanov, V. V., Chance, K. V., and Goede, A. P. H.: SCIAMACHY: Mission objectives and measurement modes, J. Atmos. Sci., 56, 127–150, https://doi.org/10.1175/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2, 1999. a
Bovensmann, H., Aben, I., Van Roozendael, M., Kühl, S., Gottwald, M., von Savigny, C., Buchwitz, M., Richter, A., Frankenberg, C., Stammes, P., de Graaf, M., Wittrock, F., Sinnhuber, M., Sinnhuber, B. M., Schönhardt, A., Beirle, S., Gloudemans, A., Schrijver, H., Bracher, A., Rozanov, A. V., Weber, M., and Burrows, J. P.: SCIAMACHY's view of the changing Earth's environment, in: SCIAMACHY – Exploring the changing Earth's atmosphere, edited by: Gottwald, M. and Bovensmann, H., Springer Netherlands, Dordrecht, 175–216, https://doi.org/10.1007/978-90-481-9896-2_10, 2011. a
Bucsela, E., Celarier, E., Wenig, M., Gleason, J., Veefkind, J., Boersma, K., and Brinksma, E.: Algorithm for NO/sub 2/ vertical column retrieval from the ozone monitoring instrument, IEEE T. Geosci. Remote, 44, 1245–1258, https://doi.org/10.1109/tgrs.2005.863715, 2006. a
Burrows, J. P., Weber, M., Buchwitz, M., Rozanov, V., Ladstätter-Weißenmayer, A., Richter, A., DeBeek, R., Hoogen, R., Bramstedt, K., Eichmann, K.-U., Eisinger, M., and Perner, D.: The Global Ozone Monitoring Experiment (GOME): Mission concept and first scientific results, J. Atmos. Sci., 56, 151–175, https://doi.org/10.1175/1520-0469(1999)056<0151:TGOMEG>2.0.CO;2, 1999. a
Cooper, M. J., Martin, R. V., Hammer, M. S., Levelt, P. F., Veefkind, P., Lamsal, L. N., Krotkov, N. A., Brook, J. R., and McLinden, C. A.: Global fine-scale changes in ambient NO2 during COVID-19 lockdowns, Nature, 601, 380–387, https://doi.org/10.1038/s41586-021-04229-0, 2022. a
Crutzen, P. J.: The role of NO and NO2 in the chemistry of the troposphere and stratosphere, Annu. Rev. Earth Pl. Sc., 7, 443–472, 1979. a
Farrand, W.: Mapping the distribution of mine tailings in the Coeur d'Alene River Valley, Idaho, through the use of a constrained energy minimization technique, Remote Sens. Environ., 59, 64–76, https://doi.org/10.1016/s0034-4257(96)00080-6, 1997. a
Goldin, D., Xiong, X., Shea, Y., and Lukashin, C.: CLARREO Pathfinder/VIIRS Intercalibration: Quantifying the Polarization Effects on Reflectance and the Intercalibration Uncertainty, Remote Sens., 11, 1914, https://doi.org/10.3390/rs11161914, 2019. a
Hollingsworth, A., Engelen, R. J., Textor, C., Benedetti, A., Boucher, O., Chevallier, F., Dethof, A., Elbern, H., Eskes, H., Flemming, J., Granier, C., Kaiser, J. W., Morcrette, J.-J., Rayner, P., Peuch, V.-H., Rouil, L., Schultz, M. G., Simmons, A. J., and The Gems Consortium: TOWARD A MONITORING AND FORECASTING SYSTEM FOR ATMOSPHERIC COMPOSITION, B. Am. Meteorol. Soc., 89, 1147–1164, https://doi.org/10.1175/2008bams2355.1, 2008. a
Huangfu, P. and Atkinson, R.: Long-term exposure to NO2 and O3 and all-cause and respiratory mortality: A systematic review and meta-analysis, Environ. Int., 144, 105998, https://doi.org/10.1016/j.envint.2020.105998, 2020. a
Joiner, J.: Retrieval of cloud pressure and oceanic chlorophyll content using Raman scattering in GOME ultraviolet spectra, J. Geophys. Res., 109, D01109, https://doi.org/10.1029/2003jd003698, 2004. a
Kim, J., Jeong, U., Ahn, M.-H., Kim, J. H., Park, R. J., Lee, H., Song, C. H., Choi, Y.-S., Lee, K.-H., Yoo, J.-M., Jeong, M.-J., Park, S. K., Lee, K.-M., Song, C.-K., Kim, S.-W., Kim, Y. J., Kim, S.-W., Kim, M., Go, S., Liu, X., Chance, K., Miller, C. C., Al-Saadi, J., Veihelmann, B., Bhartia, P. K., Torres, O., Abad, G. G., Haffner, D. P., Ko, D. H., Lee, S. H., Woo, J.-H., Chong, H., Park, S. S., Nicks, D., Choi, W. J., Moon, K.-J., Cho, A., Yoon, J., kyun Kim, S., Hong, H., Lee, K., Lee, H., Lee, S., Choi, M., Veefkind, P., Levelt, P. F., Edwards, D. P., Kang, M., Eo, M., Bak, J., Baek, K., Kwon, H.-A., Yang, J., Park, J., Han, K. M., Kim, B.-R., Shin, H.-W., Choi, H., Lee, E., Chong, J., Cha, Y., Koo, J.-H., Irie, H., Hayashida, S., Kasai, Y., Kanaya, Y., Liu, C., Lin, J., Crawford, J. H., Carmichael, G. R., Newchurch, M. J., Lefer, B. L., Herman, J. R., Swap, R. J., Lau, A. K. H., Kurosu, T. P., Jaross, G., Ahlers, B., Dobber, M., McElroy, C. T., and Choi, Y.: New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS), B. Am. Meteorol. Soc., 101, E1–E22, https://doi.org/10.1175/bams-d-18-0013.1, 2020. a
Kokaly, R. F., Clark, R. N., Swayze, G. A., Livo, K. E., Hoefen, T. M., Pearson, N. C., Wise, R. A., Benzel, W. M., Lowers, H. A., Driscoll, R. L., and Klein, A. J.: USGS Spectral Library Version 7, https://doi.org/10.3133/ds1035, 2017. a
Kolm, M. G., Maurer, R., Sallusti, M., Bagnasco, G., Gulde, S. T., Smith, D. J., and Courrèges-Lacoste, G. B.: Sentinel 4: a geostationary imaging UVN spectrometer for air quality monitoring: status of design, performance and development, in: International Conference on Space Optics – ICSO 2014, edited by: Cugny, B., Sodnik, Z., and Karafolas, N., SPIE, https://doi.org/10.1117/12.2304099, 2017. a
Kuhlmann, G., Lam, Y. F., Cheung, H. M., Hartl, A., Fung, J. C. H., Chan, P. W., and Wenig, M. O.: Development of a custom OMI NO2 data product for evaluating biases in a regional chemistry transport model, Atmos. Chem. Phys., 15, 5627–5644, https://doi.org/10.5194/acp-15-5627-2015, 2015. a
Lamsal, L. N., Krotkov, N. A., Vasilkov, A., Marchenko, S., Qin, W., Yang, E.-S., Fasnacht, Z., Joiner, J., Choi, S., Haffner, D., Swartz, W. H., Fisher, B., and Bucsela, E.: Ozone Monitoring Instrument (OMI) Aura nitrogen dioxide standard product version 4.0 with improved surface and cloud treatments, Atmos. Meas. Tech., 14, 455–479, https://doi.org/10.5194/amt-14-455-2021, 2021. a
Levelt, P., van den Oord, G., Dobber, M., Malkki, A., Visser, H., de Vries, J., Stammes, P., Lundell, J., and Saari, H.: The ozone monitoring instrument, IEEE T. Geosci. Remote, 44, 1093–1101, https://doi.org/10.1109/TGRS.2006.872333, 2006. a, b
Levelt, P. F., Joiner, J., Tamminen, J., Veefkind, J. P., Bhartia, P. K., Stein Zweers, D. C., Duncan, B. N., Streets, D. G., Eskes, H., van der A, R., McLinden, C., Fioletov, V., Carn, S., de Laat, J., DeLand, M., Marchenko, S., McPeters, R., Ziemke, J., Fu, D., Liu, X., Pickering, K., Apituley, A., González Abad, G., Arola, A., Boersma, F., Chan Miller, C., Chance, K., de Graaf, M., Hakkarainen, J., Hassinen, S., Ialongo, I., Kleipool, Q., Krotkov, N., Li, C., Lamsal, L., Newman, P., Nowlan, C., Suleiman, R., Tilstra, L. G., Torres, O., Wang, H., and Wargan, K.: The Ozone Monitoring Instrument: overview of 14 years in space, Atmos. Chem. Phys., 18, 5699–5745, https://doi.org/10.5194/acp-18-5699-2018, 2018. a, b
Marchenko, S., Krotkov, N. A., Lamsal, L. N., Celarier, E. A., Swartz, W. H., and Bucsela, E. J.: Revising the slant column density retrieval of nitrogen dioxide observed by the Ozone Monitoring Instrument, J. Geophys. Res.-Atmos., 120, 5670–5692, https://doi.org/10.1002/2014JD022913, 2015. a
Martin, R. V.: An improved retrieval of tropospheric nitrogen dioxide from GOME, J. Geophys. Res., 107, 4437, https://doi.org/10.1029/2001jd001027, 2002. a
Meerdink, S. K., Hook, S. J., Roberts, D. A., and Abbott, E. A.: The ECOSTRESS spectral library version 1.0, Remote Sens. Environ., 230, 111196, https://doi.org/10.1016/j.rse.2019.05.015, 2019. a
Meister, G. and Franz, B. A.: Adjustments to the MODIS Terra radiometric calibration and polarization sensitivity in the 2010 reprocessing, in: Earth Observing Systems XVI, edited by: Butler, J. J., Xiong, X., and Gu, X., SPIE, https://doi.org/10.1117/12.891787, 2011. a
Molod, A., Takacs, L., Suarez, M., Bacmeister, J., Song, I.-S., and Eichmann, A.: The GEOS-5 atmospheric general circulation model: Mean climate and development from MERRA to Fortuna, Tech. rep., NASA Goddard Space Flight Center, Report Number: NASA-TM-2012-104606/Vol. 28, 2012. a
Munro, R., Lang, R., Klaes, D., Poli, G., Retscher, C., Lindstrot, R., Huckle, R., Lacan, A., Grzegorski, M., Holdak, A., Kokhanovsky, A., Livschitz, J., and Eisinger, M.: The GOME-2 instrument on the Metop series of satellites: instrument design, calibration, and level 1 data processing – an overview, Atmos. Meas. Tech., 9, 1279–1301, https://doi.org/10.5194/amt-9-1279-2016, 2016. a
Pearlman, A. J., Cao, C., and Wu, X.: The GOES-R Advanced Baseline Imager: polarization sensitivity and potential impacts, in: Polarization Science and Remote Sensing VII, edited by: Shaw, J. A. and LeMaster, D. A., SPIE, https://doi.org/10.1117/12.2188508, 2015. a
Richter, A. and Burrows, J.: Tropospheric NO2 from GOME measurements, Adv. Space Res., 29, 1673–1683, https://doi.org/10.1016/S0273-1177(02)00100-X, 2002. a
Shettle, E. and Fenn, R.: Models for the Aerosols of the Lower Atmosphere and the Effects of Humidity Variations on Their Optical Properties, AFGL-TR, Air Force Geophysics Laboratory, Air Force Systems Command, United States Air Force, https://books.google.com/books?id=UoXkXweSrQEC (last access: 20 June 2022), 1979. a
Shindell, D. T., Faluvegi, G., Koch, D. M., Schmidt, G. A., Unger, N., and Bauer, S. E.: Improved attribution of climate forcing to emissions, Science, 326, 716–718, https://doi.org/10.1126/science.1174760, 2009. a
Stammes, P., Sneep, M., de Haan, J. F., Veefkind, J. P., Wang, P., and Levelt, P. F.: Effective cloud fractions from the Ozone Monitoring Instrument: Theoretical framework and validation, J. Geophys. Res., 113, D16S38, https://doi.org/10.1029/2007jd008820, 2008. a, b
Valks, P., Pinardi, G., Richter, A., Lambert, J.-C., Hao, N., Loyola, D., Van Roozendael, M., and Emmadi, S.: Operational total and tropospheric NO2 column retrieval for GOME-2, Atmos. Meas. Tech., 4, 1491–1514, https://doi.org/10.5194/amt-4-1491-2011, 2011. a
Vasilkov, A., Joiner, J., Spurr, R., Bhartia, P. K., Levelt, P., and Stephens, G.: Evaluation of the OMI cloud pressures derived from rotational Raman scattering by comparisons with other satellite data and radiative transfer simulations, J. Geophys. Res., 113, D15S19, https://doi.org/10.1029/2007jd008689, 2008. a, b
Veefkind, J., Aben, I., McMullan, K., Förster, H., de Vries, J., Otter, G., Claas, J., Eskes, H., de Haan, J., Kleipool, Q., van Weele, M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann, P., Voors, R., Kruizinga, B., Vink, R., Visser, H., and Levelt, P.: TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications, Remote Sens. Environ., 120, 70–83, https://doi.org/10.1016/j.rse.2011.09.027, 2012. a, b
Voors, R., Bhatti, I. S., Wood, T., Aben, I., Veefkind, P., de Vries, J., Lobb, D., and van der Valk, N.: TROPOMI, the Sentinel 5 precursor instrument for air quality and climate observations: status of the current design, in: International Conference on Space Optics – ICSO 2012, edited by: Armandillo, E., Karafolas, N., and Cugny, B., SPIE, https://doi.org/10.1117/12.2309017, 2017. a
World Health Organization: WHO global air quality guidelines particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide, WHO European Centre for Environment and Health, Bonn, Germany, ISBN 9789240034228, 2021. a
Wu, A., Geng, X., Wald, A., Angal, A., and Xiong, X.: Assessment of Terra MODIS On-Orbit Polarization Sensitivity Using Pseudoinvariant Desert Sites, IEEE T. Geosci. Remote, 55, 4168–4176, https://doi.org/10.1109/tgrs.2017.2689719, 2017. a
Xu, X. and Wang, J.: UNL-VRTM, A Testbed for Aerosol Remote Sensing: Model Developments and Applications, in: Springer Series in Light Scattering,
Springer International Publishing, 1–69,https://doi.org/10.1007/978-3-030-20587-4_1, 2019.
a
Yan, X., Zang, Z., Zhao, C., and Husi, L.: Understanding global changes in fine-mode aerosols during 2008–2017 using statistical methods and deep learning approach, Environ. Int., 149, 106392, https://doi.org/10.1016/j.envint.2021.106392, 2021. a
Zoogman, P., Liu, X., Suleiman, R., Pennington, W., Flittner, D., Al-Saadi, J., Hilton, B., Nicks, D., Newchurch, M., Carr, J., Janz, S., Andraschko, M., Arola, A., Baker, B., Canova, B., Miller, C. C., Cohen, R., Davis, J., Dussault, M., Edwards, D., Fishman, J., Ghulam, A., Abad, G. G., Grutter, M., Herman, J., Houck, J., Jacob, D., Joiner, J., Kerridge, B., Kim, J., Krotkov, N., Lamsal, L., Li, C., Lindfors, A., Martin, R., McElroy, C., McLinden, C., Natraj, V., Neil, D., Nowlan, C., O'Sullivan, E., Palmer, P., Pierce, R., Pippin, M., Saiz-Lopez, A., Spurr, R., Szykman, J., Torres, O., Veefkind, J., Veihelmann, B., Wang, H., Wang, J., and Chance, K.: Tropospheric emissions: Monitoring of pollution (TEMPO), J. Quant. Spectrosc. Ra., 186, 17–39, https://doi.org/10.1016/j.jqsrt.2016.05.008, 2017. a, b, c, d
Short summary
NOAA’s Geostationary Extended Observations (GeoXO) constellation is planned to consist of an atmospheric composition instrument (ACX) to support air quality forecasting and monitoring. As design trade-offs are being studied, we investigated one parameter, the polarization sensitivity, which has yet to be fully documented for NO2 retrievals. Our simulation study explores these impacts to inform the ACX’s development and better understand polarization’s role in trace gas retrievals.
NOAA’s Geostationary Extended Observations (GeoXO) constellation is planned to consist of an...