Articles | Volume 13, issue 11
https://doi.org/10.5194/amt-13-5955-2020
https://doi.org/10.5194/amt-13-5955-2020
Research article
 | 
09 Nov 2020
Research article |  | 09 Nov 2020

Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm

Hai Zhang, Shobha Kondragunta, Istvan Laszlo, and Mi Zhou

Related authors

MAGARA: a Multi-Angle Geostationary Aerosol Retrieval Algorithm
James A. Limbacher, Ralph A. Kahn, Mariel D. Friberg, Jaehwa Lee, Tyler Summers, and Hai Zhang
Atmos. Meas. Tech., 17, 471–498, https://doi.org/10.5194/amt-17-471-2024,https://doi.org/10.5194/amt-17-471-2024, 2024
Short summary
Screening for snow/snowmelt in SNPP VIIRS aerosol optical depth algorithm
Jingfeng Huang, Istvan Laszlo, Lorraine A. Remer, Hongqing Liu, Hai Zhang, Pubu Ciren, and Shobha Kondragunta
Atmos. Meas. Tech., 11, 5813–5825, https://doi.org/10.5194/amt-11-5813-2018,https://doi.org/10.5194/amt-11-5813-2018, 2018
Short summary
Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD retrievals against ground sunphotometer observations over East Asia
Q. Xiao, H. Zhang, M. Choi, S. Li, S. Kondragunta, J. Kim, B. Holben, R. C. Levy, and Y. Liu
Atmos. Chem. Phys., 16, 1255–1269, https://doi.org/10.5194/acp-16-1255-2016,https://doi.org/10.5194/acp-16-1255-2016, 2016
Short summary

Related subject area

Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Multi-angle aerosol optical depth retrieval method based on improved surface reflectance
Lijuan Chen, Ren Wang, Ying Fei, Peng Fang, Yong Zha, and Haishan Chen
Atmos. Meas. Tech., 17, 4411–4424, https://doi.org/10.5194/amt-17-4411-2024,https://doi.org/10.5194/amt-17-4411-2024, 2024
Short summary
Comparison of diurnal aerosol products retrieved from combinations of micro-pulse lidar and sun photometer observations over the KAUST observation site
Anton Lopatin, Oleg Dubovik, Georgiy Stenchikov, Ellsworth J. Welton, Illia Shevchenko, David Fuertes, Marcos Herreras-Giralda, Tatsiana Lapyonok, and Alexander Smirnov
Atmos. Meas. Tech., 17, 4445–4470, https://doi.org/10.5194/amt-17-4445-2024,https://doi.org/10.5194/amt-17-4445-2024, 2024
Short summary
First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia
Yeseul Cho, Jhoon Kim, Sujung Go, Mijin Kim, Seoyoung Lee, Minseok Kim, Heesung Chong, Won-Jin Lee, Dong-Won Lee, Omar Torres, and Sang Seo Park
Atmos. Meas. Tech., 17, 4369–4390, https://doi.org/10.5194/amt-17-4369-2024,https://doi.org/10.5194/amt-17-4369-2024, 2024
Short summary
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
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
Short summary
Stratospheric aerosol characteristics from SCIAMACHY limb observations: two-parameter retrieval
Christine Pohl, Felix Wrana, Alexei Rozanov, Terry Deshler, Elizaveta Malinina, Christian von Savigny, Landon A. Rieger, Adam E. Bourassa, and John P. Burrows
Atmos. Meas. Tech., 17, 4153–4181, https://doi.org/10.5194/amt-17-4153-2024,https://doi.org/10.5194/amt-17-4153-2024, 2024
Short summary

Cited articles

ABI AOD ATBD: GOES-R Advanced Baseline Imager (ABI) Algorithm Theoretical Basis Document For Suspended Matter/Aerosol Optical Depth and Aerosol Size Parameter, NOAA/NESDIS/STAR, Version 4.2, 14 February 2018, available at: https://www.star.nesdis.noaa.gov/smcd/spb/aq/AerosolWatch/docs/GOES-R_ABI_AOD_ATBD_V4.2_20180214.pdf (last access: 24 February 2020), 2018. 
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, 1989. 
Giles, D. M., Sinyuk, A., Sorokin, M. G., Schafer, J. S., Smirnov, A., Slutsker, I., Eck, T. F., Holben, B. N., Lewis, J. R., Campbell, J. R., Welton, E. J., Korkin, S. V., and Lyapustin, A. I.: Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements, Atmos. Meas. Tech., 12, 169–209, https://doi.org/10.5194/amt-12-169-2019, 2019 
Green, M., Kondragunta, S., Ciren, P., and Xu, C. Y.: Comparison of GOES and MODIS aerosol optical depth (AOD) to aerosol robotic network (AERONET) AOD and IMPROVE PM2.5 mass at Bondville, Illinois, J. Air Waste Manag. Assoc., 59, 1082– 1091, 2009. 
Gupta, P., Levy, R. C., Mattoo, S., Remer, L. A., Holz, R. E., and Heidinger, A. K.: Applying the Dark Target aerosol algorithm with Advanced Himawari Imager observations during the KORUS-AQ field campaign, Atmos. Meas. Tech., 12, 6557–6577, https://doi.org/10.5194/amt-12-6557-2019, 2019. 
Download
Short summary
Geostationary Operational Environmental Satellites (GOES) retrieve high temporal resolution aerosol optical depth, which is a measure of the aerosol quantity within the atmospheric column. This work introduces an algorithm that improves the accuracy of the aerosol optical depth retrievals from GOES. The resulting data product can be used in monitoring the air quality and climate change research.