82 citations as recorded by crossref.

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23. Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues K. Lee et al. 10.5194/gmd-13-1055-2020
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34. Detection and Tracking of Chattonella spp. and Skeletonema spp. Blooms Using Geostationary Ocean Color Imager (GOCI) in Ariake Sea, Japan C. Feng et al. 10.1029/2020JC016924
35. Using depolarization to quantify ice nucleating particle concentrations: a new method J. Zenker et al. 10.5194/amt-10-4639-2017
36. Assessment of air quality in North Korea from satellite observations H. Chong et al. 10.1016/j.envint.2022.107708
37. Satellite-based estimation of hourly PM2.5 levels during heavy winter pollution episodes in the Yangtze River Delta, China Q. She et al. 10.1016/j.chemosphere.2019.124678
38. Assimilating AOD retrievals from GOCI and VIIRS to forecast surface PM2.5 episodes over Eastern China J. Pang et al. 10.1016/j.atmosenv.2018.02.011
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40. The polarization crossfire (PCF) sensor suite focusing on satellite remote sensing of fine particulate matter PM2.5 from space Z. Li et al. 10.1016/j.jqsrt.2022.108217
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43. Intercomparing the Aerosol Optical Depth Using the Geostationary Satellite Sensors (AHI, GOCI and MI) from Yonsei AErosol Retrieval (YAER) Algorithm H. Lim et al. 10.5467/JKESS.2018.39.2.119
44. Spectral and Spatial Dependencies in the Validation of Satellite-Based Aerosol Optical Depth from the Geostationary Ocean Color Imager Using the Aerosol Robotic Network M. Kim et al. 10.3390/rs15143621
45. An assessment of thin cloud detection by applying bidirectional reflectance distribution function model‐based background surface reflectance using Geostationary Ocean Color Imager (GOCI): A case study for South Korea H. Kim et al. 10.1002/2017JD026707
46. The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future L. Remer et al. 10.3390/rs12182900
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48. The implication of the air quality pattern in South Korea after the COVID-19 outbreak J. Koo et al. 10.1038/s41598-020-80429-4
49. Aerosol model evaluation using two geostationary satellites over East Asia in May 2016 D. Goto et al. 10.1016/j.atmosres.2018.10.016
50. Development of GK-2A AMI Aerosol Detection Algorithm in the East-Asia Region Using Himawari-8 AHI Data J. Jee et al. 10.1007/s13143-019-00156-3
51. Assessment of long-range transboundary aerosols in Seoul, South Korea from Geostationary Ocean Color Imager (GOCI) and ground-based observations S. Lee et al. 10.1016/j.envpol.2020.115924
52. A High-Precision Aerosol Retrieval Algorithm (HiPARA) for Advanced Himawari Imager (AHI) data: Development and verification X. Su et al. 10.1016/j.rse.2020.112221
53. A Simple Band Ratio Library (BRL) Algorithm for Retrieval of Hourly Aerosol Optical Depth Using FY-4A AGRI Geostationary Satellite Data X. Jiang et al. 10.3390/rs14194861
54. The Impact of the Direct Effect of Aerosols on Meteorology and Air Quality Using Aerosol Optical Depth Assimilation During the KORUS‐AQ Campaign J. Jung et al. 10.1029/2019JD030641
55. Instantaneous aerosol and surface retrieval using satellites in geostationary orbit (iAERUS-GEO) – estimation of 15 min aerosol optical depth from MSG/SEVIRI and evaluation with reference data X. Ceamanos et al. 10.5194/amt-16-2575-2023
56. Aerosol profiling using radiometric and polarimetric spectral measurements in the O2 near infrared bands: Estimation of information content and measurement uncertainties M. Choi et al. 10.1016/j.rse.2020.112179
57. Geostationary Ocean Color Imager (GOCI) Marine Fog Detection in Combination with Himawari-8 Based on the Decision Tree D. Kim et al. 10.3390/rs12010149
58. Geographical coverage analysis and usage suggestions of temporal averaged aerosol optical depth product from GOES-R satellite data X. Jiang et al. 10.1080/01431161.2024.2331978
59. Modeling Asian Dust Storms Using WRF‐Chem During the DRAGON‐Asia Field Campaign in April 2012 K. Kim et al. 10.1029/2021JD034793
60. A simplified aerosol retrieval algorithm for Himawari-8 Advanced Himawari Imager over Beijing Z. Zhang et al. 10.1016/j.atmosenv.2018.11.023
61. 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 J. Park et al. 10.5194/amt-16-3039-2023
62. Quasi‐Global Maps of Daily Aerosol Optical Depth From a Ring of Five Geostationary Meteorological Satellites Using AERUS‐GEO X. Ceamanos et al. 10.1029/2021JD034906
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64. Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns H. Lim et al. 10.5194/amt-14-4575-2021
65. Impact of the OMI aerosol optical depth on analysis increments through coupled meteorology–aerosol data assimilation for an Asian dust storm E. Lee et al. 10.1016/j.rse.2017.02.013
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67. An improved algorithm for retrieving high resolution fine-mode aerosol based on polarized satellite data: Application and validation for POLDER-3 B. Ge et al. 10.1016/j.rse.2020.111894
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69. Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) Based on Temporal, Spatial and Spectral Measurements X. Chen et al. 10.3390/rs14040980
70. Quantitative assessment of the potential of optimal estimation for aerosol retrieval from geostationary weather satellites in the frame of the iAERUS‐GEO algorithm A. Georgeot et al. 10.1002/asl.1199
71. An overview of mesoscale aerosol processes, comparisons, and validation studies from DRAGON networks B. Holben et al. 10.5194/acp-18-655-2018
72. Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign M. Choi et al. 10.5194/amt-12-4619-2019
73. Retrieval of aerosol optical properties from GOCI-II observations: Continuation of long-term geostationary aerosol monitoring over East Asia S. Lee et al. 10.1016/j.scitotenv.2023.166504
74. Analysis of Aerosol Optical Properties for High Particulate Matters and Light Asian Dust in Seoul Using GOCI D. Kim et al. 10.5572/KOSAE.2017.33.3.233
75. Continuous mapping of fine particulate matter (PM<sub>2.5</sub>) air quality in East Asia at daily 6  ×  6 km<sup>2</sup> resolution by application of a random forest algorithm to 2011–2019 GOCI geostationary satellite data D. Pendergrass et al. 10.5194/amt-15-1075-2022
76. Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data Y. Lops et al. 10.1029/2021GL093096
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79. GIST-PM-Asia v1: development of a numerical system to improve particulate matter forecasts in South Korea using geostationary satellite-retrieved aerosol optical data over Northeast Asia S. Lee et al. 10.5194/gmd-9-17-2016
80. Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD retrievals against ground sunphotometer observations over East Asia Q. Xiao et al. 10.5194/acp-16-1255-2016
81. 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) M. Kim et al. 10.5194/acp-16-1789-2016
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