Articles | Volume 17, issue 14
https://doi.org/10.5194/amt-17-4317-2024
https://doi.org/10.5194/amt-17-4317-2024
Research article
 | 
19 Jul 2024
Research article |  | 19 Jul 2024

Aerosol optical depth data fusion with Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT-2) instruments GEMS, AMI, and GOCI-II: statistical and deep neural network methods

Minseok Kim, Jhoon Kim, Hyunkwang Lim, Seoyoung Lee, Yeseul Cho, Yun-Gon Lee, Sujung Go, and Kyunghwa Lee

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Cited articles

Ahn, C., Torres, O., and Jethva, H.: Assessment of OMI near-UV aerosol optical depth over land, J. Geophys. Res.-Atmos., 119, 2457–2473, https://doi.org/10.1002/2013jd020188, 2014. 
Box, G. E. P. and Cox, D. R.: An Analysis of Transformations Revisited, Rebutted, J. Am Stat. Assoc., 77, 209–210, https://doi.org/10.2307/2287791, 1982. 
Cho, Y., Kim, J., Go, S., Kim, M., Lee, S., Kim, M., Chong, H., Lee, W.-J., Lee, D.-W., Torres, O., and Park, S. S.: First Atmospheric Aerosol Monitoring Results from Geostationary Environment Monitoring Spectrometer (GEMS) over Asia, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2023-221, in review, 2023. 
Choi, J. K., Park, M. S., Han, K. S., Kim, H. C., and Im, J.: One Year of GOCI-II Launch Present and Future, Korean J. Remote Sens., 37, 1229–1234, https://doi.org/10.7780/kjrs.2021.37.5.2.1, 2021. 
Choi, M., Kim, J., Lee, J., Kim, M., Park, Y.-J., Jeong, U., Kim, W., Hong, H., Holben, B., Eck, T. F., Song, C. H., Lim, J.-H., and Song, C.-K.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign, Atmos. Meas. Tech., 9, 1377–1398, https://doi.org/10.5194/amt-9-1377-2016, 2016. 
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Short summary
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.