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-layer retrieval of aerosol optical depth in the troposphere using SEVIRI data: a case study of the European continent
Maryam Pashayi, Mehran Satari, and Mehdi Momeni Shahraki
Atmos. Meas. Tech., 18, 1415–1439, https://doi.org/10.5194/amt-18-1415-2025,https://doi.org/10.5194/amt-18-1415-2025, 2025
Short summary
Star photometry with all-sky cameras to retrieve aerosol optical depth at night-time
Roberto Román, Daniel González-Fernández, Juan Carlos Antuña-Sánchez, Celia Herrero del Barrio, Sara Herrero-Anta, África Barreto, Victoria E. Cachorro, Lionel Doppler, Ramiro González, Christoph Ritter, David Mateos, Natalia Kouremeti, Gustavo Copes, Abel Calle, María José Granados-Muñoz, Carlos Toledano, and Ángel M. de Frutos
EGUsphere, https://doi.org/10.5194/egusphere-2025-667,https://doi.org/10.5194/egusphere-2025-667, 2025
Short summary
Ground-based contrail observations: comparisons with reanalysis weather data and contrail model simulations
Jade Low, Roger Teoh, Joel Ponsonby, Edward Gryspeerdt, Marc Shapiro, and Marc E. J. Stettler
Atmos. Meas. Tech., 18, 37–56, https://doi.org/10.5194/amt-18-37-2025,https://doi.org/10.5194/amt-18-37-2025, 2025
Short summary
Improvements in aerosol layer height retrievals from TROPOMI oxygen A-band measurements by surface albedo fitting in optimal estimation
Martin de Graaf, Maarten Sneep, Mark ter Linden, L. Gijsbert Tilstra, and J. Pepijn Veefkind
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-198,https://doi.org/10.5194/amt-2024-198, 2025
Revised manuscript accepted for AMT
Short summary
Satellite Aerosol Composition Retrieval from a combination of three different Instruments: Information content analysis
Ulrike Stöffelmair, Thomas Popp, Marco Vountas, and Hartmut Bösch
EGUsphere, https://doi.org/10.5194/egusphere-2024-2800,https://doi.org/10.5194/egusphere-2024-2800, 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.
Share