Articles | Volume 17, issue 7
https://doi.org/10.5194/amt-17-1913-2024
https://doi.org/10.5194/amt-17-1913-2024
Research article
 | 
04 Apr 2024
Research article |  | 04 Apr 2024

Parameterizing spectral surface reflectance relationships for the Dark Target aerosol algorithm applied to a geostationary imager

Mijin Kim, Robert C. Levy, Lorraine A. Remer, Shana Mattoo, and Pawan Gupta

Related authors

First Atmospheric Aerosol Monitoring Results from 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. Discuss., https://doi.org/10.5194/amt-2023-221,https://doi.org/10.5194/amt-2023-221, 2023
Revised manuscript accepted for AMT
Short summary

Related subject area

Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
A near-global multiyear climate data record of the fine-mode and coarse-mode components of atmospheric pure dust
Emmanouil Proestakis, Antonis Gkikas, Thanasis Georgiou, Anna Kampouri, Eleni Drakaki, Claire L. Ryder, Franco Marenco, Eleni Marinou, and Vassilis Amiridis
Atmos. Meas. Tech., 17, 3625–3667, https://doi.org/10.5194/amt-17-3625-2024,https://doi.org/10.5194/amt-17-3625-2024, 2024
Short summary
Innovative aerosol hygroscopic growth study from Mie–Raman–fluorescence lidar and microwave radiometer synergy
Robin Miri, Olivier Pujol, Qiaoyun Hu, Philippe Goloub, Igor Veselovskii, Thierry Podvin, and Fabrice Ducos
Atmos. Meas. Tech., 17, 3367–3375, https://doi.org/10.5194/amt-17-3367-2024,https://doi.org/10.5194/amt-17-3367-2024, 2024
Short summary
Evaluation of calibration performance of a low-cost particulate matter sensor using collocated and distant NO2
Kabseok Ko, Seokheon Cho, and Ramesh R. Rao
Atmos. Meas. Tech., 17, 3303–3322, https://doi.org/10.5194/amt-17-3303-2024,https://doi.org/10.5194/amt-17-3303-2024, 2024
Short summary
Geostationary aerosol retrievals of extreme biomass burning plumes during the 2019–2020 Australian bushfires
Daniel J. V. Robbins, Caroline A. Poulsen, Steven T. Siems, Simon R. Proud, Andrew T. Prata, Roy G. Grainger, and Adam C. Povey
Atmos. Meas. Tech., 17, 3279–3302, https://doi.org/10.5194/amt-17-3279-2024,https://doi.org/10.5194/amt-17-3279-2024, 2024
Short summary
Multi-wavelength dataset of aerosol extinction profiles retrieved from GOMOS stellar occultation measurements
Viktoria F. Sofieva, Monika Szelag, Johanna Tamminen, Didier Fussen, Christine Bingen, Filip Vanhellemont, Nina Mateshvili, Alexei Rozanov, and Christine Pohl
Atmos. Meas. Tech., 17, 3085–3101, https://doi.org/10.5194/amt-17-3085-2024,https://doi.org/10.5194/amt-17-3085-2024, 2024
Short summary

Cited articles

Al-Saadi, J., Szykman, J., Pierce, R. B., Kittaka, C., Neil, D., Chu, D. A., Remer, L., Gumley, L., Prins, E., Weinstock, L., MacDonald, C., Wayland, R., Dimmick, F., and Fishman, J.: Improving National Air Quality Forecasts with Satellite Aerosol Observations, B. Am. Meteorol. Soc., 86, 1249–1262, https://doi.org/10.1175/BAMS-86-9-1249, 2005. 
Baldridge, A. M., Hook, S. J., Grove, C. I., and Rivera, G.: The ASTER spectral library version 2.0, Remote Sens. Environ., 113, 711–715, https://doi.org/10.1016/j.rse.2008.11.007, 2009. 
Belward, A. S., Estes, J. E., and Kline, K. D.: The IGBP-DIS global 1-km land-cover data set DISCover: A project overview, Photogramm. Eng. Rem. S., 65, 1013–1020, 1999. 
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S. K., Sherwood, S., Stevens, B., and Zhang, X. Y.: Clouds and Aerosols, in: Climate Change 2013 – The Physical Science Basis, Cambridge University Press, https://doi.org/10.1017/CBO9781107415324.016, 2014. 
Ceamanos, X., Moparthy, S., Carrer, D., and Seidel, F. C.: Assessing the Potential of Geostationary Satellites for Aerosol Remote Sensing Based on Critical Surface Albedo, Remote Sens.-Basel, 11, 2958, https://doi.org/10.3390/rs11242958, 2019. 
Download
Short summary
The study focused on evaluating and modifying the surface reflectance parameterization (SRP) of the Dark Target (DT) algorithm for geostationary observation. When using the DT SRP with the ABIs sensor on GOES-R, artificial diurnal signatures were present in AOD retrieval. To overcome this issue, a new SRP was developed, incorporating solar zenith angle and land cover type. The revised SRP resulted in improved AOD retrieval, demonstrating reduced bias around local noon.