Articles | Volume 16, issue 10
https://doi.org/10.5194/amt-16-2673-2023
© Author(s) 2023. 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-16-2673-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 May 2023
Research article |
| 31 May 2023
| 31 May 2023
Exploring geometrical stereoscopic aerosol top height retrieval from geostationary satellite imagery in East Asia
Minseok Kim
Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea
Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea
Hyunkwang Lim
National Institute for Environmental Studies, Tsukuba, Japan
Seoyoung Lee
Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea
Yeseul Cho
Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea
Huidong Yeo
School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea
Sang-Woo Kim
School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea
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Minseok Kim, Jhoon Kim, Hyunkwang Lim, Seoyoung Lee, Yeseul Cho, Yun-Gon Lee, Sujung Go, and Kyunghwa Lee
Atmos. Meas. Tech., 17, 4317–4335, https://doi.org/10.5194/amt-17-4317-2024, https://doi.org/10.5194/amt-17-4317-2024, 2024
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Information about aerosol loading in the atmosphere can be collected from various satellite instruments. Aerosol products from various satellite instruments have their own error characteristics. This study statistically merged aerosol optical depth datasets from multiple instruments aboard geostationary satellites considering uncertainties. Also, a deep neural network technique is adopted for aerosol data merging.
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
Atmos. Chem. Phys., 24, 7027–7039, https://doi.org/10.5194/acp-24-7027-2024, https://doi.org/10.5194/acp-24-7027-2024, 2024
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Drew C. Pendergrass, Daniel J. Jacob, Yujin J. Oak, Jeewoo Lee, Minseok Kim, Jhoon Kim, Seoyoung Lee, Shixian Zhai, Hitoshi Irie, and Hong Liao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-172, https://doi.org/10.5194/essd-2024-172, 2024
Preprint withdrawn
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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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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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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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Jong-Uk Park, Hyun-Jae Kim, Jin-Soo Park, Jinsoo Choi, Sang Seo Park, Kangho Bae, Jong-Jae Lee, Chang-Keun Song, Soojin Park, Kyuseok Shim, Yeonsoo Cho, and Sang-Woo Kim
Atmos. Meas. Tech., 17, 197–217, https://doi.org/10.5194/amt-17-197-2024, https://doi.org/10.5194/amt-17-197-2024, 2024
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The high-spatial-resolution NO2 vertical column densities (VCDs) were measured from airborne observations using the low-cost hyperspectral imaging sensor (HIS) at three industrial areas in South Korea with the newly developed versatile NO2 VCD retrieval algorithm apt to be applied to the instruments with volatile optical and radiometric properties. The airborne HIS observations emphasized the intensifying satellite sub-grid variability in NO2 VCDs near the emission sources.
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.
Jincheol Park, Jia Jung, Yunsoo Choi, Hyunkwang Lim, Minseok Kim, Kyunghwa Lee, Yun Gon Lee, and Jhoon Kim
Atmos. Meas. Tech., 16, 3039–3057, https://doi.org/10.5194/amt-16-3039-2023, https://doi.org/10.5194/amt-16-3039-2023, 2023
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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.
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.
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.
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.
Clémence Rose, Martine Collaud Coen, Elisabeth Andrews, Yong Lin, Isaline Bossert, Cathrine Lund Myhre, Thomas Tuch, Alfred Wiedensohler, Markus Fiebig, Pasi Aalto, Andrés Alastuey, Elisabeth Alonso-Blanco, Marcos Andrade, Begoña Artíñano, Todor Arsov, Urs Baltensperger, Susanne Bastian, Olaf Bath, Johan Paul Beukes, Benjamin T. Brem, Nicolas Bukowiecki, Juan Andrés Casquero-Vera, Sébastien Conil, Konstantinos Eleftheriadis, Olivier Favez, Harald Flentje, Maria I. Gini, Francisco Javier Gómez-Moreno, Martin Gysel-Beer, Anna Gannet Hallar, Ivo Kalapov, Nikos Kalivitis, Anne Kasper-Giebl, Melita Keywood, Jeong Eun Kim, Sang-Woo Kim, Adam Kristensson, Markku Kulmala, Heikki Lihavainen, Neng-Huei Lin, Hassan Lyamani, Angela Marinoni, Sebastiao Martins Dos Santos, Olga L. Mayol-Bracero, Frank Meinhardt, Maik Merkel, Jean-Marc Metzger, Nikolaos Mihalopoulos, Jakub Ondracek, Marco Pandolfi, Noemi Pérez, Tuukka Petäjä, Jean-Eudes Petit, David Picard, Jean-Marc Pichon, Veronique Pont, Jean-Philippe Putaud, Fabienne Reisen, Karine Sellegri, Sangeeta Sharma, Gerhard Schauer, Patrick Sheridan, James Patrick Sherman, Andreas Schwerin, Ralf Sohmer, Mar Sorribas, Junying Sun, Pierre Tulet, Ville Vakkari, Pieter Gideon van Zyl, Fernando Velarde, Paolo Villani, Stergios Vratolis, Zdenek Wagner, Sheng-Hsiang Wang, Kay Weinhold, Rolf Weller, Margarita Yela, Vladimir Zdimal, and Paolo Laj
Atmos. Chem. Phys., 21, 17185–17223, https://doi.org/10.5194/acp-21-17185-2021, https://doi.org/10.5194/acp-21-17185-2021, 2021
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Aerosol particles are a complex component of the atmospheric system the effects of which are among the most uncertain in climate change projections. Using data collected at 62 stations, this study provides the most up-to-date picture of the spatial distribution of particle number concentration and size distribution worldwide, with the aim of contributing to better representation of aerosols and their interactions with clouds in models and, therefore, better evaluation of their impact on climate.
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.
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
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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.
Saehee Lim, Meehye Lee, Paolo Laj, Sang-Woo Kim, Kang-Ho Ahn, Junsoo Gil, Xiaona Shang, Marco Zanatta, and Kyeong-Sik Kang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-1247, https://doi.org/10.5194/acp-2020-1247, 2021
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This study identifies the main drivers of the formation and transformation processes of submicron particles and highlights that the thick coating of rBC was a result of active conversion of hygroscopic inorganic salts leading to fine aerosol pollution. Consequently, we suggest BC particles as a key contributor to PM2.5 mass increase, which implies that BC reduction is an effective mitigation against haze pollution as well as climate change in Northeast Asia.
Yongjoo Choi, Yugo Kanaya, Masayuki Takigawa, Chunmao Zhu, Seung-Myung Park, Atsushi Matsuki, Yasuhiro Sadanaga, Sang-Woo Kim, Xiaole Pan, and Ignacio Pisso
Atmos. Chem. Phys., 20, 13655–13670, https://doi.org/10.5194/acp-20-13655-2020, https://doi.org/10.5194/acp-20-13655-2020, 2020
Cited articles
Chang, K.-E., Hsiao, T.-C., Tsay, S.-C., Lin, T.-H., Griffith, S. M., Liu, C.-Y., and Chou, C. C.-K.:
Embedded information of aerosol type, hygroscopicity and scattering enhancement factor revealed by the relationship between PM2.5 and aerosol optical depth, Sci. Total Environ., 867, 161471, https://doi.org/10.1016/j.scitotenv.2023.161471, 2023.
Chen, X., Wang, J., Xu, X. G., Zhou, M., Zhang, H. X., Garcia, L. C., Colarco, P. R., Janz, S. J., Yorks, J., McGill, M., Reid, J. S., de Graaf, M., and Kondragunta, S.:
First retrieval of absorbing aerosol height over dark target using TROPOMI oxygen B band: Algorithm development and application for surface particulate matter estimates, Remote Sens. Environ., 265, 112674, https://doi.org/10.1016/j.rse.2021.112674, 2021.
Chimot, J., Veefkind, J. P., Vlemmix, T., de Haan, J. F., Amiridis, V., Proestakis, E., Marinou, E., and Levelt, P. F.:
An exploratory study on the aerosol height retrieval from OMI measurements of the 477 nm O2 – O2 spectral band using a neural network approach, Atmos. Meas. Tech., 10, 783–809, https://doi.org/10.5194/amt-10-783-2017, 2017.
Choi, M., Sander, S. P., Spurr, R. J. D., Pongetti, T. J., van Harten, G., Drouin, B. J., Crisp, D., Eldering, A., Kalashnikova, O. V., Jiang, J. H., Hyon, J. J., and Fu, D.:
Aerosol profiling using radiometric and polarimetric spectral measurements in the O2 near infrared bands: Estimation of information content and measurement uncertainties, Remote Sens. Environ., 253, 112179, https://doi.org/10.1016/j.rse.2020.112179, 2021.
Chu, D. A., Szykman, J., Kittaka, C., Chin, M., Liu, H. C., Remer, L., Al-Saadi, J., and Winker, D.:
Developing aerosol height product from MODIS and synergy of MODIS and CALIPSO measurement for global application, IGARSS 2008– 2008 IEEE Int. Geos. & Rem. Sens. Symposium, Boston, MA, US, 7–11 July 2008, IEEE, IV-303–IV-306, https://doi.org/10.1109/IGARSS.2008.4779718, 2008.
Gupta, G., Ratnam, M. V., Madhavan, B. L., Prasad, P., and Narayanamurthy, C. S.:
Vertical and spatial distribution of elevated aerosol layers obtained using long-term ground-based and space-borne lidar observations, Atmos. Environ., 246, 118172, https://doi.org/10.1016/j.atmosenv.2020.118172, 2021.
Hasler, A. F.:
Stereographic observations from geosynchronous satellites: An important new tool for the atmospheric sciences, B. Am. Meteorol. Soc., 62, 194–212, https://doi.org/10.1175/1520-0477(1981)062<0194:SOFGSA>2.0.CO;2, 1981.
Hollstein, A. and Fischer, J.:
Retrieving aerosol height from the oxygen A band: a fast forward operator and sensitivity study concerning spectral resolution, instrumental noise, and surface inhomogeneity, Atmos. Meas. Tech., 7, 1429–1441, https://doi.org/10.5194/amt-7-1429-2014, 2014.
Huang, L., Jiang, J. H., Tackett, J. L., Su, H., and Fu, R.:
Seasonal and diurnal variations of aerosol extinction profile and type distribution from CALIPSO 5-year observations, J. Geophys. Res.-Atmos., 118, 4572–4596, https://doi.org/10.1002/jgrd.50407, 2013.
Ingmann, P., Veihelmann, B., Langen, J., Lamarre, D., Stark, H., and Courreges-Lacoste, G. B.:
Requirements for the GMES Atmosphere Service and ESA's implementation concept: Sentinels-4/-5 and-5p, Remote Sens. Environ., 120, 58–69, https://doi.org/10.1016/j.rse.2012.01.023, 2012.
Kim, D., Gu, M., Oh, T. H., Kim, E. K., and Yang, H. J.:
Introduction of the Advanced Meteorological Imager of Geo-Kompsat-2a: In-orbit tests and performance validation, Remote Sens.-Basel, 13, 1303, https://doi.org/10.3390/rs13071303, 2021.
Kim, J., Yoon, J. M., Ahn, M. H., Sohn, B. J., and Lim, H. S.:
Retrieving aerosol optical depth using visible and mid-IR channels from geostationary satellite MTSAT-1R, Int. J. Remote Sens., 29, 6181–6192, https://doi.org/10.1080/01431160802175553, 2008.
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., Chan Miller, 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., Kim, S. K., 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.
Kim, M., Kim, J., Torres, O., Ahn, C., Kim, W., Jeong, U., Go, S., Liu, X., Moon, K. J., and Kim, D. R.:
Optimal estimation-based algorithm to retrieve aerosol optical properties for GEMS measurements over Asia, Remote Sens.-Basel, 10, 162, https://doi.org/10.3390/rs10020162, 2018.
Kim, M., Kim, J., Jeong, U., Kim, W., Hong, H., Holben, B., Eck, T. F., Lim, J. H., Song, C. K., Lee, S., and Chung, C.-Y.:
Aerosol optical properties derived from the DRAGON-NE Asia campaign, and implications for a single-channel algorithm to retrieve aerosol optical depth in spring from Meteorological Imager (MI) on-board the Communication, Ocean, and Meteorological Satellite (COMS), Atmos. Chem. Phys., 16, 1789–1808, https://doi.org/10.5194/acp-16-1789-2016, 2016.
Kim, M.-H., Omar, A. H., Tackett, J. L., Vaughan, M. A., Winker, D. M., Trepte, C. R., Hu, Y., Liu, Z., Poole, L. R., Pitts, M. C., Kar, J., and Magill, B. E.:
The CALIPSO version 4 automated aerosol classification and lidar ratio selection algorithm, Atmos. Meas. Tech., 11, 6107–6135, https://doi.org/10.5194/amt-11-6107-2018, 2018.
Koffi, B., Schulz, M., Breon, F. M., Griesfeller, J., Winker, D., Balkanski, Y., Bauer, S., Berntsen, T., Chin, M. A., Collins, W. D., Dentener, F., Diehl, T., Easter, R., Ghan, S., Ginoux, P., Gong, S. L., Horowitz, L. W., Iversen, T., Kirkevag, A., Koch, D., Krol, M., Myhre, G., Stier, P., and Takemura, T.:
Application of the CALIOP layer product to evaluate the vertical distribution of aerosols estimated by global models: AeroCom phase I results, J. Geophys. Res.-Atmos., 117, D10201, https://doi.org/10.1029/2011jd016858, 2012.
Lee, J., Hsu, N. C., Bettenhausen, C., Sayer, A. M., Seftor, C. J., and Jeong, M. J.:
Retrieving the height of smoke and dust aerosols by synergistic use of VIIRS, OMPS, and CALIOP observations, J. Geophys. Res.-Atmos., 120, 8372–8388, https://doi.org/10.1002/2015jd023567, 2015.
Lee, J., Shin, D. B., Chung, C. Y., and Kim, J.: A Cloud Top-Height Retrieval Algorithm Using Simultaneous Observations from the Himawari-8 and FY-2E Satellites, Remote Sens.-Basel, 12, 1953, https://doi.org/10.3390/rs12121953, 2020.
Lee, J. H., Hsu, N. C., Sayer, A. M., Seftor, C. J., and Kim, W. V.:
Aerosol layer height with enhanced spectral coverage achieved by synergy between VIIRS and OMPS-NM measurements, IEEE Geosci. Remote S., 18, 949–953, https://doi.org/10.1109/Lgrs.2020.2992099, 2021.
Liao, T. T., Gui, K., Li, Y. F., Wang, X. Y., and Sun, Y.:
Seasonal distribution and vertical structure of different types of aerosols in southwest China observed from CALIOP, Atmos. Environ., 246, 118145, https://doi.org/10.1016/j.atmosenv.2020.118145, 2021.
Lim, H., Choi, M., Kim, J., Kasai, Y., and Chan, P. W.:
AHI/Himawari-8 Yonsei Aerosol Retrieval (YAER): Algorithm, validation and merged products, Remote Sens.-Basel, 10, 699, https://doi.org/10.3390/rs10050699, 2018.
Lim, H., Kim, J., Lee, S. Y., Kasai, Y., and Park, S. S.:
Aerosol Layer Height Retrieval Over Ocean From the Advanced Himawari Imager Using Spectral Reflectance Sensitivity, IEEE Geosci. Remote S., 20, 1000405 https://doi.org/10.1109/Lgrs.2023.3236299, 2023.
Maneewongvatana, S. and Mount, D. M.: Analysis of approximate nearest neighbor searching with clustered point sets, arXiv [preprint], https://doi.org/10.48550/arXiv.cs/9901013, 26 January 1999.
Merucci, L., Zaksek, K., Carboni, E., and Corradini, S.:
Stereoscopic Estimation of Volcanic Ash Cloud-Top Height from Two Geostationary Satellites, Remote Sens.-Basel, 8, 206, https://doi.org/10.3390/rs8030206, 2016.
Michailidis, K., Koukouli, M.-E., Siomos, N., Balis, D., Tuinder, O., Tilstra, L. G., Mona, L., Pappalardo, G., and Bortoli, D.:
First validation of GOME-2/MetOp absorbing aerosol height using EARLINET lidar observations, Atmos. Chem. Phys., 21, 3193–3213, https://doi.org/10.5194/acp-21-3193-2021, 2021.
Nanda, S., de Graaf, M., Veefkind, J. P., Sneep, M., ter Linden, M., Sun, J., and Levelt, P. F.:
A first comparison of TROPOMI aerosol layer height (ALH) to CALIOP data, Atmos. Meas. Tech., 13, 3043–3059, https://doi.org/10.5194/amt-13-3043-2020, 2020.
NASA Earthdata: https://www.earthdata.nasa.gov/, last access: 17 May 2023.
Nelson, D. L., Garay, M. J., Kahn, R. A., and Dunst, B. A.:
Stereoscopic height and wind retrievals for aerosol plumes with the MISR INteractive eXplorer (MINX), Remote Sens.-Basel, 5, 4593–4628, https://doi.org/10.3390/rs5094593, 2013.
Sun, J., Veefkind, P., Nanda, S., van Velthoven, P., and Levelt, P.:
The role of aerosol layer height in quantifying aerosol absorption from ultraviolet satellite observations, Atmos. Meas. Tech., 12, 6319–6340, https://doi.org/10.5194/amt-12-6319-2019, 2019.
Park, S. S., Kim, J., Lee, H., Torres, O., Lee, K.-M., and Lee, S. D.:
Utilization of O4 slant column density to derive aerosol layer height from a space-borne UV–visible hyperspectral sensor: sensitivity and case study, Atmos. Chem. Phys., 16, 1987–2006, https://doi.org/10.5194/acp-16-1987-2016, 2016.
Seiz, G., Tjemkes, S., and Watts, P.:
Multiview cloud-top height and wind retrieval with photogrammetric methods:Application to Meteosat-8 HRV observations, J. Appl. Meteorol. Clim., 46, 1182–1195, https://doi.org//10.1175/JAM2532.1, 2007.
Tabata, T., Andou, A., Bessho, K., Date, K., Dojo, R., Hosaka, K., Mori, N., Murata, H., Nakayama, R., Okuyama, A., and Takahashi, M.:
Himawari-8/AHI latest performance of navigation and calibration, Proc. SPIE, 9881, 98812j, https://doi.org/10.1117/12.2240200, 2016.
Tian, P., Cao, X., Zhang, L., Sun, N., Sun, L., Logan, T., Shi, J., Wang, Y., Ji, Y., Lin, Y., Huang, Z., Zhou, T., Shi, Y., and Zhang, R.:
Aerosol vertical distribution and optical properties over China from long-term satellite and ground-based remote sensing, Atmos. Chem. Phys., 17, 2509–2523, https://doi.org/10.5194/acp-17-2509-2017, 2017.
von Hoyningen-Huene, W., Yoon, J., Vountas, M., Istomina, L. G., Rohen, G., Dinter, T., Kokhanovsky, A. A., and Burrows, J. P.:
Retrieval of spectral aerosol optical thickness over land using ocean color sensors MERIS and SeaWiFS, Atmos. Meas. Tech., 4, 151–171, https://doi.org/10.5194/amt-4-151-2011, 2011.
Welton, E. J., Voss, K. J., Gordon, H. R., Maring, H., Smirnov, A., Holben, B., Schmid, B., Livingston, J. M., Russell, P. B., Durkee, P. A., Formenti, P., and Andreae, M. O.:
Ground-based lidar measurements of aerosols during ACE-2: instrument description, results, and comparisons with other ground-based and airborne measurements, Tellus B, 52, 636–651, https://doi.org/10.1034/j.1600-0889.2000.00025.x, 2000.
Winker, D. M., Hunt, W. H., and McGill, M. J.:
Initial performance assessment of CALIOP, Geophys. Res. Lett., 34, L19803, https://doi.org/10.1029/2007gl030135, 2007.
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y. X., Powell, K. A., Liu, Z. Y., Hunt, W. H., and Young, S. A.:
Overview of the CALIPSO mission and CALIOP data processing algorithms, J. Atmos. Ocean. Tech., 26, 2310–2323, https://doi.org/10.1175/2009jtecha1281.1, 2009.
Winker, D. M., Pelon, J., Coakley, Jr. J. A., Ackerman, S. A., Charlson, R. J., Colarco, P. R., Flamant, P., Fu, Q., Hoff, R. M., Kittaka, C., Kubar, T. K., le Treut, H., McCormick, M. P., Mégie, G., Poole, L., Powell, K., Trepte, C., Vaughan, M. A., and Wielicki B. A.:
The CALIPSO Mission: A Global 3D View of Aerosols and Clouds, B. Am. Meteorol. Soc., 91, 9, 1211–1230, https://doi.org/10.1175/2010BAMS3009.1, 2010.
Wu, L. H., Hasekamp, O., van Diedenhoven, B., Cairns, B., Yorks, J. E., and Chowdhary, J.:
Passive remote sensing of aerosol layer height using near-UV multiangle polarization measurements, Geophys. Res. Lett., 43, 8783–8790, https://doi.org/10.1002/2016gl069848, 2016.
Yang, J., Zhang, Z. Q., Wei, C. Y., Lu, F., and Guo, Q.:
Introducing the New Generation of Chinese Geostationary Weather Satellites, Fengyun-4., B. Am. Meteorol. Soc., 98, 1637–1658, https://doi.org/10.1175/Bams-D-16-0065.1, 2017.
Yeo, H., Kim, S. W., Lee, C., Kim, D., Kim, B. G., Kim, S., Nam, H. G., Noh, Y. M., Park, S., Park, C. B., Seo, K., Choi, J. Y., Lee, M. I., and Lee, E. H.:
The KALION automated aerosol type classification and mass concentration calculation algorithm, Korean J. Rem. Sens., 32, 2, 119–131, https://doi.org/10.7780/kjrs.2016.32.2.5, 2016.
Zhang, H., Kondragunta, S., Laszlo, I., and Zhou, M.:
Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm, Atmos. Meas. Tech., 13, 5955–5975, https://doi.org/10.5194/amt-13-5955-2020, 2020.
Zakšek, K., Hort, M., Zaletelj, J., and Langmann, B.:
Monitoring volcanic ash cloud top height through simultaneous retrieval of optical data from polar orbiting and geostationary satellites, Atmos. Chem. Phys., 13, 2589–2606, https://doi.org/10.5194/acp-13-2589-2013, 2013.
Zhong, B., Ma, Y. B., Yang, A. X., and Wu, J. J.:
Radiometric performance evaluation of FY-4A/AGRI based on Aqua/MODIS, Sensors-Basel, 21, 1859, https://doi.org/10.3390/s21051859, 2021.
Zoogman, P., Liu, X., Suleiman, R. M., Pennington, W. F., Flittner, D. E., Al-Saadi, J. A., Hilton, B. B., Nicks, D. K., Newchurch, M. J., Carr, J. L., Janz, S. J., Andraschko, M. R., Arola, A., Baker, B. D., Canova, B. P., Miller, C. C., Cohen, R. C., Davis, J. E., Dussault, M. E., Edwards, D. P., Fishman, J., Ghulam, A., Abad, G. G., Grutter, M., Herman, J. R., Houck, J., Jacob, D. J., Joiner, J., Kerridge, B. J., Kim, J., Krotkov, N. A., Lamsal, L., Li, C., Lindfors, A., Martin, R. V., McElroy, C. T., McLinden, C., Natraj, V., Neil, D. O., Nowlan, C. R., O'Sullivan, E. J., Palmer, P. I., Pierce, R. B., Pippin, M. R., Saiz-Lopez, A., Spurr, R. J. D., Szykman, J. J., Torres, O., Veefkind, J. P., 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.
Short summary
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.
Aerosol height information is important when seeking an understanding of the vertical structure...
