Articles | Volume 17, issue 2
https://doi.org/10.5194/amt-17-471-2024
https://doi.org/10.5194/amt-17-471-2024
Research article
 | 
24 Jan 2024
Research article |  | 24 Jan 2024

MAGARA: a Multi-Angle Geostationary Aerosol Retrieval Algorithm

James A. Limbacher, Ralph A. Kahn, Mariel D. Friberg, Jaehwa Lee, Tyler Summers, and Hai Zhang

Related authors

Expanding the coverage of Multi-angle Imaging SpectroRadiometer (MISR) aerosol retrievals over shallow, turbid, and eutrophic waters
Robert R. Nelson, Marcin L. Witek, Michael J. Garay, Michael A. Bull, James A. Limbacher, Ralph A. Kahn, and David J. Diner
Atmos. Meas. Tech., 16, 4947–4960, https://doi.org/10.5194/amt-16-4947-2023,https://doi.org/10.5194/amt-16-4947-2023, 2023
Short summary

Related subject area

Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Transport of the Hunga volcanic aerosols inferred from Himawari-8/9 limb measurements
Fred Prata
Atmos. Meas. Tech., 17, 3751–3764, https://doi.org/10.5194/amt-17-3751-2024,https://doi.org/10.5194/amt-17-3751-2024, 2024
Short summary
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

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, https://www.star.nesdis.noaa.gov/smcd/spb/aq/AerosolWatch/docs/GOES-R_ABI_AOD_ATBD_V4.2_20180214.pdf (last access: 5 March 2021), 2018. 
Baldassari, E.: Camp Fire death toll grows to 29, matching 1933 blaze as state's deadliest, East Bay Times, 11, https://www.presstelegram.com/2018/11/12/camp-fire-death- toll-grows-to-29-matching-1933-griffith-park-blaze-for-deadliest-in-california/ (last access: 9 January 2024), 12 November 2018. 
Bian, Q., Kreidenweis, S., Chiu, J. C., Miller, S. D., Xu, X., Wang, J., Kahn, R. A., Limbacher, J. A., Remer, L. A., and Levy, R. C.: Constraining Aerosol Phase Function Using Dual-View Geostationary Satellites, J. Geophys. Res.-Atmos., 126, e2021JD035209, https://doi.org/10.1029/2021JD035209, 2021. 
Cal Fire: Kincade Fire Incident, https://www.fire.ca.gov/incidents/2019/10/23/kincade-fire (last access: 4 September 2022), 2020. 
Ceamanos, X., Six, B., Moparthy, S., Carrer, D., Georgeot, A., Gasteiger, J., Riedi, J., Attié, J.-L., Lyapustin, A., and Katsev, I.: 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, Atmos. Meas. Tech., 16, 2575–2599, https://doi.org/10.5194/amt-16-2575-2023, 2023. 
Download
Short summary
We present the new Multi-Angle Geostationary Aerosol Retrieval Algorithm (MAGARA) that fuses observations from GOES-16 and GOES-17 to retrieve information about aerosol loading (at 10–15 min cadence) and aerosol particle properties (daily), all at pixel-level resolution. We present MAGARA results for three case studies: the 2018 California Camp Fire, the 2019 Williams Flats Fire, and the 2019 Kincade Fire. We also compare MAGARA aerosol loading and particle properties with AERONET.