Articles | Volume 16, issue 12
https://doi.org/10.5194/amt-16-3039-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-16-3039-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jun 2023
Research article |
| 19 Jun 2023
| 19 Jun 2023
Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product and the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol optical depth (AOD) data fusion product and its proxy
Jincheol Park
Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX, USA
Jia Jung
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory (PNNL), Richland, WA, USA
Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX, USA
Hyunkwang Lim
National Institute for Environmental Studies, Tsukuba, Japan
Minseok Kim
Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea
Kyunghwa Lee
Environmental Satellite Center, Climate and Air Quality Research Department, National Institute of Environmental Research (NIER), Incheon, South Korea
Yun Gon Lee
Atmospheric Sciences, Department of Astronomy, Space Science, and Geology, Chungnam National University, Daejeon, South Korea
Jhoon Kim
Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea
Particulate Matter Research Institute, Samsung Advanced Institute of Technology, Suwon, South Korea
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Laura Hyesung Yang, Daniel J. Jacob, Ruijun Dang, Yujin J. Oak, Haipeng Lin, Jhoon Kim, Shixian Zhai, Nadia K. Colombi, Drew C. Pendergrass, Ellie Beaudry, Viral Shah, Xu Feng, Robert M. Yantosca, Heesung Chong, Junsung Park, Hanlim Lee, Won-Jin Lee, Soontae Kim, Eunhye Kim, Katherine R. Travis, James H. Crawford, and Hong Liao
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The Geostationary Environment Monitoring Spectrometer (GEMS) provides hourly measurements of NO2. We use the chemical transport model to find how emissions, chemistry, and transport drive the changes in NO2 observed by GEMS at different times of the day. In winter, the chemistry plays a minor role, and high daytime emissions dominate the diurnal variation in NO2, balanced by transport. In summer, emissions, chemistry, and transport play an important role in shaping the diurnal variation in NO2.
Drew C. Pendergrass, Daniel J. Jacob, Yujin J. Oak, Jeewoo Lee, Minseok Kim, Jhoon Kim, Seoyoung Lee, Shixian Zhai, Hitoshi Irie, and Hong Liao
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Preprint withdrawn
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Fine particles suspended in the atmosphere are a major form of air pollution and an important public health burden. However, measurements of particulate matter are sparse in space and in places like East Asia monitors are established after regulatory policies to improve pollution have changed. In this paper, we use machine learning to fill in the gaps. We train an algorithm to predict pollution at the surface from the atmosphere’s opacity, then produce high resolution maps of data without gaps.
Heesung Chong, Gonzalo González Abad, Caroline R. Nowlan, Christopher Chan Miller, Alfonso Saiz-Lopez, Rafael P. Fernandez, Hyeong-Ahn Kwon, Zolal Ayazpour, Huiqun Wang, Amir H. Souri, Xiong Liu, Kelly Chance, Ewan O'Sullivan, Jhoon Kim, Ja-Ho Koo, William R. Simpson, François Hendrick, Richard Querel, Glen Jaross, Colin Seftor, and Raid M. Suleiman
Atmos. Meas. Tech., 17, 2873–2916, https://doi.org/10.5194/amt-17-2873-2024, https://doi.org/10.5194/amt-17-2873-2024, 2024
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We present a new bromine monoxide (BrO) product derived using radiances measured from OMPS-NM on board the Suomi-NPP satellite. This product provides nearly a decade of global stratospheric and tropospheric column retrievals, a feature that is currently rare in publicly accessible datasets. Both stratospheric and tropospheric columns from OMPS-NM demonstrate robust performance, exhibiting good agreement with ground-based observations collected at three stations (Lauder, Utqiagvik, and Harestua).
Gitaek T. Lee, Rokjin J. Park, Hyeong-Ahn Kwon, Eunjo S. Ha, Sieun D. Lee, Seunga Shin, Myoung-Hwan Ahn, Mina Kang, Yong-Sang Choi, Gyuyeon Kim, Dong-Won Lee, Deok-Rae Kim, Hyunkee Hong, Bavo Langerock, Corinne Vigouroux, Christophe Lerot, Francois Hendrick, Gaia Pinardi, Isabelle De Smedt, Michel Van Roozendael, Pucai Wang, Heesung Chong, Yeseul Cho, and Jhoon Kim
Atmos. Chem. Phys., 24, 4733–4749, https://doi.org/10.5194/acp-24-4733-2024, https://doi.org/10.5194/acp-24-4733-2024, 2024
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This study evaluates the Geostationary Environment Monitoring Spectrometer (GEMS) HCHO product by comparing its vertical column densities (VCDs) with those of TROPOMI and ground-based observations. Based on some sensitivity tests, obtaining radiance references under clear-sky conditions significantly improves HCHO retrieval quality. GEMS HCHO VCDs captured seasonal and diurnal variations well during the first year of observation, showing consistency with TROPOMI and ground-based observations.
Bo-Ram Kim, Gyuyeon Kim, Minjeong Cho, Yong-Sang Choi, and Jhoon Kim
Atmos. Meas. Tech., 17, 453–470, https://doi.org/10.5194/amt-17-453-2024, https://doi.org/10.5194/amt-17-453-2024, 2024
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This study introduces the GEMS cloud algorithm and validates its results using data from GEMS and other environmental satellites. The GEMS algorithm is able to detect the lowest cloud heights among the four satellites, and its effective cloud fraction and cloud centroid pressure are well reflected in the retrieval results. The study highlights the algorithm's usefulness in correcting errors in trace gases caused by clouds in the East Asian region.
Haklim Choi, Xiong Liu, Ukkyo Jeong, Heesung Chong, Jhoon Kim, Myung Hwan Ahn, Dai Ho Ko, Dong-Won Lee, Kyung-Jung Moon, and Kwang-Mog Lee
Atmos. Meas. Tech., 17, 145–164, https://doi.org/10.5194/amt-17-145-2024, https://doi.org/10.5194/amt-17-145-2024, 2024
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GEMS is the first geostationary satellite to measure the UV--Vis region, and this paper reports the polarization characteristics of GEMS and an algorithm. We develop a polarization correction algorithm optimized for GEMS based on a look-up-table approach that simultaneously considers the polarization of incoming light and polarization sensitivity characteristics of the instrument. Pre-launch polarization error was adjusted close to zero across the spectral range after polarization correction.
Yuhang Zhang, Jintai Lin, Jhoon Kim, Hanlim Lee, Junsung Park, Hyunkee Hong, Michel Van Roozendael, Francois Hendrick, Ting Wang, Pucai Wang, Qin He, Kai Qin, Yongjoo Choi, Yugo Kanaya, Jin Xu, Pinhua Xie, Xin Tian, Sanbao Zhang, Shanshan Wang, Siyang Cheng, Xinghong Cheng, Jianzhong Ma, Thomas Wagner, Robert Spurr, Lulu Chen, Hao Kong, and Mengyao Liu
Atmos. Meas. Tech., 16, 4643–4665, https://doi.org/10.5194/amt-16-4643-2023, https://doi.org/10.5194/amt-16-4643-2023, 2023
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Our tropospheric NO2 vertical column density product with high spatiotemporal resolution is based on the Geostationary Environment Monitoring Spectrometer (GEMS) and named POMINO–GEMS. Strong hotspot signals and NO2 diurnal variations are clearly seen. Validations with multiple satellite products and ground-based, mobile car and surface measurements exhibit the overall great performance of the POMINO–GEMS product, indicating its capability for application in environmental studies.
Serin Kim, Daewon Kim, Hyunkee Hong, Lim-Seok Chang, Hanlim Lee, Deok-Rae Kim, Donghee Kim, Jeong-Ah Yu, Dongwon Lee, Ukkyo Jeong, Chang-Kuen Song, Sang-Woo Kim, Sang Seo Park, Jhoon Kim, Thomas F. Hanisco, Junsung Park, Wonei Choi, and Kwangyul Lee
Atmos. Meas. Tech., 16, 3959–3972, https://doi.org/10.5194/amt-16-3959-2023, https://doi.org/10.5194/amt-16-3959-2023, 2023
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A first evaluation of the Geostationary Environmental Monitoring Spectrometer (GEMS) NO2 was carried out via comparison with the NO2 data obtained from the ground-based Pandora direct-sun measurements at four sites in Seosan, Republic of Korea. Comparisons between GEMS NO2 and Pandora NO2 were performed according to GEMS cloud fraction. GEMS NO2 showed good agreement with that of Pandora NO2 under less cloudy conditions.
Minseok Kim, Jhoon Kim, Hyunkwang Lim, Seoyoung Lee, Yeseul Cho, Huidong Yeo, and Sang-Woo Kim
Atmos. Meas. Tech., 16, 2673–2690, https://doi.org/10.5194/amt-16-2673-2023, https://doi.org/10.5194/amt-16-2673-2023, 2023
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Aerosol height information is important when seeking an understanding of the vertical structure of the aerosol layer and long-range transport. In this study, a geometrical aerosol top height (ATH) retrieval using a parallax of two geostationary satellites is investigated. With sufficient longitudinal separation between the two satellites, a decent ATH product could be retrieved.
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.
Gyo-Hwang Choo, Kyunghwa Lee, Hyunkee Hong, Ukkyo Jeong, Wonei Choi, and Scott J. Janz
Atmos. Meas. Tech., 16, 625–644, https://doi.org/10.5194/amt-16-625-2023, https://doi.org/10.5194/amt-16-625-2023, 2023
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This study discusses the morning and afternoon distribution of NO2 emissions in large cities and industrial areas in South Korea, one of the largest NO2 emitters around the world, using GeoTASO, an airborne remote sensing instrument developed to support geostationary satellite missions. NO2 measurements from GeoTASO were compared with those from ground-based remote sensing instruments including Pandora and in situ sensors.
Ukkyo Jeong, Si-Chee Tsay, N. Christina Hsu, David M. Giles, John W. Cooper, Jaehwa Lee, Robert J. Swap, Brent N. Holben, James J. Butler, Sheng-Hsiang Wang, Somporn Chantara, Hyunkee Hong, Donghee Kim, and Jhoon Kim
Atmos. Chem. Phys., 22, 11957–11986, https://doi.org/10.5194/acp-22-11957-2022, https://doi.org/10.5194/acp-22-11957-2022, 2022
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Ultraviolet (UV) measurements from satellite and ground are important for deriving information on several atmospheric trace and aerosol characteristics. Simultaneous retrievals of aerosol and trace gases in this study suggest that water uptake by aerosols is one of the important phenomena affecting aerosol properties over northern Thailand, which is important for regional air quality and climate. Obtained aerosol properties covering the UV are also important for various satellite algorithms.
Samuel E. LeBlanc, Michal Segal-Rozenhaimer, Jens Redemann, Connor Flynn, Roy R. Johnson, Stephen E. Dunagan, Robert Dahlgren, Jhoon Kim, Myungje Choi, Arlindo da Silva, Patricia Castellanos, Qian Tan, Luke Ziemba, Kenneth Lee Thornhill, and Meloë Kacenelenbogen
Atmos. Chem. Phys., 22, 11275–11304, https://doi.org/10.5194/acp-22-11275-2022, https://doi.org/10.5194/acp-22-11275-2022, 2022
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Airborne observations of atmospheric particles and pollution over Korea during a field campaign in May–June 2016 showed that the smallest atmospheric particles are present in the lowest 2 km of the atmosphere. The aerosol size is more spatially variable than optical thickness. We show this with remote sensing (4STAR), in situ (LARGE) observations, satellite measurements (GOCI), and modeled properties (MERRA-2), and it is contrary to the current understanding.
Seoung Soo Lee, Jinho Choi, Goun Kim, Kyung-Ja Ha, Kyong-Hwan Seo, Chang Hoon Jung, Junshik Um, Youtong Zheng, Jianping Guo, Sang-Keun Song, Yun Gon Lee, and Nobuyuki Utsumi
Atmos. Chem. Phys., 22, 9059–9081, https://doi.org/10.5194/acp-22-9059-2022, https://doi.org/10.5194/acp-22-9059-2022, 2022
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This study investigates how aerosols affect clouds and precipitation and how the aerosol effects vary with varying types of clouds that are characterized by cloud depth in two metropolitan areas in East Asia. As cloud depth increases, the enhancement of precipitation amount transitions to no changes in precipitation amount with increasing aerosol concentrations. This indicates that cloud depth needs to be considered for a comprehensive understanding of aerosol-cloud interactions.
Drew C. Pendergrass, Shixian Zhai, Jhoon Kim, Ja-Ho Koo, Seoyoung Lee, Minah Bae, Soontae Kim, Hong Liao, and Daniel J. Jacob
Atmos. Meas. Tech., 15, 1075–1091, https://doi.org/10.5194/amt-15-1075-2022, https://doi.org/10.5194/amt-15-1075-2022, 2022
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This paper uses a machine learning algorithm to infer high-resolution maps of particulate air quality in eastern China, Japan, and the Korean peninsula, using data from a geostationary satellite along with meteorology. We then perform an extensive evaluation of this inferred air quality and use it to diagnose trends in the region. We hope this paper and the associated data will be valuable to other scientists interested in epidemiology, air quality, remote sensing, and machine learning.
Sujung Go, Alexei Lyapustin, Gregory L. Schuster, Myungje Choi, Paul Ginoux, Mian Chin, Olga Kalashnikova, Oleg Dubovik, Jhoon Kim, Arlindo da Silva, Brent Holben, and Jeffrey S. Reid
Atmos. Chem. Phys., 22, 1395–1423, https://doi.org/10.5194/acp-22-1395-2022, https://doi.org/10.5194/acp-22-1395-2022, 2022
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This paper presents a retrieval algorithm of iron-oxide species (hematite, goethite) content in the atmosphere from DSCOVR EPIC observations. Our results display variations within the published range of hematite and goethite over the main dust-source regions but show significant seasonal and spatial variability. This implies a single-viewing satellite instrument with UV–visible channels may provide essential information on shortwave dust direct radiative effects for climate modeling.
Shixian Zhai, Daniel J. Jacob, Jared F. Brewer, Ke Li, Jonathan M. Moch, Jhoon Kim, Seoyoung Lee, Hyunkwang Lim, Hyun Chul Lee, Su Keun Kuk, Rokjin J. Park, Jaein I. Jeong, Xuan Wang, Pengfei Liu, Gan Luo, Fangqun Yu, Jun Meng, Randall V. Martin, Katherine R. Travis, Johnathan W. Hair, Bruce E. Anderson, Jack E. Dibb, Jose L. Jimenez, Pedro Campuzano-Jost, Benjamin A. Nault, Jung-Hun Woo, Younha Kim, Qiang Zhang, and Hong Liao
Atmos. Chem. Phys., 21, 16775–16791, https://doi.org/10.5194/acp-21-16775-2021, https://doi.org/10.5194/acp-21-16775-2021, 2021
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Geostationary satellite aerosol optical depth (AOD) has tremendous potential for monitoring surface fine particulate matter (PM2.5). Our study explored the physical relationship between AOD and PM2.5 by integrating data from surface networks, aircraft, and satellites with the GEOS-Chem chemical transport model. We quantitatively showed that accurate simulation of aerosol size distributions, boundary layer depths, relative humidity, coarse particles, and diurnal variations in PM2.5 are essential.
Bavand Sadeghi, Arman Pouyaei, Yunsoo Choi, and Bernhard Rappenglueck
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-565, https://doi.org/10.5194/acp-2021-565, 2021
Revised manuscript not accepted
Short summary
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The most significant contributions of VOCs over the Houston Ship Channel came from alkanes. Light alkanes were dominant sources in both seasons. We explored the photochemical reaction of organic compounds and studied their contributions to ozone formation. Ethylene and propylene have the highest. Through weighted trajectory, VOCs at Lynchburg Ferry site was influenced by petrochemical sectors of Baytown and Galveston Bay refineries and industrial facilities of the Bayport industrial district.
Hyunkwang Lim, Sujung Go, Jhoon Kim, Myungje Choi, Seoyoung Lee, Chang-Keun Song, and Yasuko Kasai
Atmos. Meas. Tech., 14, 4575–4592, https://doi.org/10.5194/amt-14-4575-2021, https://doi.org/10.5194/amt-14-4575-2021, 2021
Short summary
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Aerosol property observations by satellites from geostationary Earth orbit (GEO) in particular have advantages of frequent sampling better than 1 h in addition to broader spatial coverage. This study provides data fusion products of aerosol optical properties from four different algorithms for two different GEO satellites: GOCI and AHI. The fused aerosol products adopted ensemble-mean and maximum-likelihood estimation methods. The data fusion provides improved results with better accuracy.
Ebrahim Eslami, Yunsoo Choi, Yannic Lops, Alqamah Sayeed, and Ahmed Khan Salman
Geosci. Model Dev., 13, 6237–6251, https://doi.org/10.5194/gmd-13-6237-2020, https://doi.org/10.5194/gmd-13-6237-2020, 2020
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As using deep learning algorithms has become a popular data analytic technique, atmospheric scientists should have a balanced perception of their strengths and limitations so that they can provide a powerful analysis of complex data with well-established procedures. This study addresses significant limitations of an advanced deep learning algorithm, the convolutional neural network.
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Short summary
In response to the recent release of new geostationary platform-derived observational data generated by the Geostationary Environment Monitoring Spectrometer (GEMS) and its sister instruments, this study utilized the GEMS data fusion product and its proxy data in adjusting aerosol precursor emissions over East Asia. The use of spatiotemporally more complete observation references in updating the emissions resulted in more promising model performances in estimating aerosol loadings in East Asia.
In response to the recent release of new geostationary platform-derived observational data...
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