Articles | Volume 16, issue 10
https://doi.org/10.5194/amt-16-2575-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-2575-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 May 2023
Research article |
| 26 May 2023
| 26 May 2023
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
CNRM, Météo-France, CNRS, Université de Toulouse, Toulouse, France
Bruno Six
University of Lille, CNRS, CNES, UMS 2877 – ICARE Data and Services Center, 59000 Lille, France
Suman Moparthy
CNRM, Météo-France, CNRS, Université de Toulouse, Toulouse, France
now at: ACRI-ST, Toulouse, France
Dominique Carrer
CNRM, Météo-France, CNRS, Université de Toulouse, Toulouse, France
Adèle Georgeot
CNRM, Météo-France, CNRS, Université de Toulouse, Toulouse, France
Josef Gasteiger
Faculty of Physics, University of Vienna, Vienna, Austria
now at: Hamtec Consulting GmbH at EUMETSAT, Darmstadt, Germany
Jérôme Riedi
University of Lille, CNRS, CNES, UMS 2877 – ICARE Data and Services Center, 59000 Lille, France
University of Lille, CNRS, UMR 8518 – LOA – Laboratoire d'Optique Atmosphérique, 59000 Lille, France
Jean-Luc Attié
LAERO-Laboratoire d'Aérologie, UPS, CNRS, Université de Toulouse, 14 Avenue Edouard Belin, 31400 Toulouse, France
Alexei Lyapustin
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Iosif Katsev
B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Pr. Nezavisimosti 68, 220072, Minsk, Belarus
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Cited
10 citations as recorded by crossref.
- Aerosol Optical Depth Retrieval for Sentinel-2 Based on Convolutional Neural Network Method J. Jiang et al. 10.3390/atmos14091400
- Aerosol optical depth retrieval from the EarthCARE Multi-Spectral Imager: the M-AOT product N. Docter et al. 10.5194/amt-16-3437-2023
- Remote sensing and model analysis of biomass burning smoke transported across the Atlantic during the 2020 Western US wildfire season X. Ceamanos et al. 10.1038/s41598-023-39312-1
- Aerosol Optical Depth Measurements from a Simulated Low-Cost Multi-Wavelength Ground-Based Camera: A Clear Case over a Peri-Urban Area V. Boulisset et al. 10.3390/rs16010140
- Insights into airborne particulate matter: artificial intelligence-driven PM2.5 modelling in Hyderabad district, India N. A K & A. Mathew 10.1007/s00477-024-02728-w
- MAGARA: a Multi-Angle Geostationary Aerosol Retrieval Algorithm J. Limbacher et al. 10.5194/amt-17-471-2024
- Quantitative assessment of the potential of optimal estimation for aerosol retrieval from geostationary weather satellites in the frame of the iAERUS‐GEO algorithm A. Georgeot et al. 10.1002/asl.1199
- Parameterizing spectral surface reflectance relationships for the Dark Target aerosol algorithm applied to a geostationary imager M. Kim et al. 10.5194/amt-17-1913-2024
- First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia Y. Cho et al. 10.5194/amt-17-4369-2024
- Surface PM10 Air Pollution of Karachi, Pakistan: Spatial-Temporal Statistical Modeling Using Aerosol-Optical-Depth Remote Sensing A. Das et al. 10.1089/ees.2024.0200
9 citations as recorded by crossref.
- Aerosol Optical Depth Retrieval for Sentinel-2 Based on Convolutional Neural Network Method J. Jiang et al. 10.3390/atmos14091400
- Aerosol optical depth retrieval from the EarthCARE Multi-Spectral Imager: the M-AOT product N. Docter et al. 10.5194/amt-16-3437-2023
- Remote sensing and model analysis of biomass burning smoke transported across the Atlantic during the 2020 Western US wildfire season X. Ceamanos et al. 10.1038/s41598-023-39312-1
- Aerosol Optical Depth Measurements from a Simulated Low-Cost Multi-Wavelength Ground-Based Camera: A Clear Case over a Peri-Urban Area V. Boulisset et al. 10.3390/rs16010140
- Insights into airborne particulate matter: artificial intelligence-driven PM2.5 modelling in Hyderabad district, India N. A K & A. Mathew 10.1007/s00477-024-02728-w
- MAGARA: a Multi-Angle Geostationary Aerosol Retrieval Algorithm J. Limbacher et al. 10.5194/amt-17-471-2024
- Quantitative assessment of the potential of optimal estimation for aerosol retrieval from geostationary weather satellites in the frame of the iAERUS‐GEO algorithm A. Georgeot et al. 10.1002/asl.1199
- Parameterizing spectral surface reflectance relationships for the Dark Target aerosol algorithm applied to a geostationary imager M. Kim et al. 10.5194/amt-17-1913-2024
- First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia Y. Cho et al. 10.5194/amt-17-4369-2024
Latest update: 20 Nov 2024
Short summary
A new algorithm to retrieve the diurnal evolution of aerosol optical depth over land and ocean from geostationary meteorological satellites is proposed and successfully evaluated with reference ground-based and satellite data. The high-temporal-resolution aerosol observations that are obtained from the EUMETSAT Meteosat Second Generation mission are unprecedented and open the door to studies that cannot be conducted with the once-a-day observations available from low-Earth-orbit satellites.
A new algorithm to retrieve the diurnal evolution of aerosol optical depth over land and ocean...
