Articles | Volume 12, issue 2
https://doi.org/10.5194/amt-12-1059-2019
© Author(s) 2019. 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-12-1059-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Feb 2019
Research article |
| 18 Feb 2019
| 18 Feb 2019
A cloud identification algorithm over the Arctic for use with AATSR–SLSTR measurements
Soheila Jafariserajehlou
CORRESPONDING AUTHOR
Institute of Environmental Physics, University of Bremen, Otto-Hahn-Allee 1, 28359 Bremen, Germany
Linlu Mei
Institute of Environmental Physics, University of Bremen, Otto-Hahn-Allee 1, 28359 Bremen, Germany
Marco Vountas
Institute of Environmental Physics, University of Bremen, Otto-Hahn-Allee 1, 28359 Bremen, Germany
Vladimir Rozanov
Institute of Environmental Physics, University of Bremen, Otto-Hahn-Allee 1, 28359 Bremen, Germany
John P. Burrows
Institute of Environmental Physics, University of Bremen, Otto-Hahn-Allee 1, 28359 Bremen, Germany
Rainer Hollmann
DWD – Deutscher Wetterdienst, Frankfurter Straße 135, 63067 Offenbach, Germany
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Cited
17 citations as recorded by crossref.
- A Machine-Learning-Based Cloud Detection and Cloud-Top Thermodynamic Phase Algorithm over the Arctic Using FY3D/MERSI-II C. Yu et al. https://doi.org/10.3390/rs17183128
- Evaluation and comparison of a machine learning cloud identification algorithm for the SLSTR in polar regions C. Poulsen et al. https://doi.org/10.1016/j.rse.2020.111999
- The Arctic Cloud Puzzle: Using ACLOUD/PASCAL Multiplatform Observations to Unravel the Role of Clouds and Aerosol Particles in Arctic Amplification M. Wendisch et al. https://doi.org/10.1175/BAMS-D-18-0072.1
- Model simulations capture seasonal Arctic Haze and clean-air cycle better than satellite and reanalysis B. Swain et al. https://doi.org/10.1038/s41598-025-29188-8
- Innovative cloud quantification: deep learning classification and finite-sector clustering for ground-based all-sky imaging J. Luo et al. https://doi.org/10.5194/amt-17-3765-2024
- The retrieval of snow properties from SLSTR Sentinel-3 – Part 1: Method description and sensitivity study L. Mei et al. https://doi.org/10.5194/tc-15-2757-2021
- Assessment of health hazards of greenhouse workers considering UV exposure and thermal comfort M. Chowdhury et al. https://doi.org/10.1016/j.atech.2023.100319
- Multi-LEO Satellite Stereo Winds J. Carr et al. https://doi.org/10.3390/rs15082154
- Cloud-Tolerant Multiwidth Arctic Sea-Ice Lead Detection Using FY-3D MERSI-II 250-m TIR Data L. Zhang et al. https://doi.org/10.1109/TGRS.2025.3631915
- Performance Evaluation of Machine Learning Regression Models for Rainfall Prediction M. Abdullah & S. Said https://doi.org/10.1007/s40996-024-01691-4
- An extended lidar-based cirrus cloud retrieval scheme: first application over an Arctic site K. Nakoudi et al. https://doi.org/10.1364/OE.414770
- Retrieval of aerosol optical depth over the Arctic cryosphere during spring and summer using satellite observations B. Swain et al. https://doi.org/10.5194/amt-17-359-2024
- A Machine Learning Model for FY-4A Cloud Detection Based on Physical Feature Fusion Y. Liang et al. https://doi.org/10.3390/rs18040536
- Assessment of Direct Normal Irradiance Forecasts Based on IFS/ECMWF Data and Observations in the South of Portugal J. Perdigão et al. https://doi.org/10.3390/forecast2020007
- Insights of aerosol-precipitation nexus in the central Arctic through CMIP6 climate models B. Swain et al. https://doi.org/10.1038/s41612-025-00957-6
- Simulated reflectance above snow constrained by airborne measurements of solar radiation: implications for the snow grain morphology in the Arctic S. Jafariserajehlou et al. https://doi.org/10.5194/amt-14-369-2021
- Aerosols in the central Arctic cryosphere: satellite and model integrated insights during Arctic spring and summer B. Swain et al. https://doi.org/10.5194/acp-24-5671-2024
17 citations as recorded by crossref.
- A Machine-Learning-Based Cloud Detection and Cloud-Top Thermodynamic Phase Algorithm over the Arctic Using FY3D/MERSI-II C. Yu et al. https://doi.org/10.3390/rs17183128
- Evaluation and comparison of a machine learning cloud identification algorithm for the SLSTR in polar regions C. Poulsen et al. https://doi.org/10.1016/j.rse.2020.111999
- The Arctic Cloud Puzzle: Using ACLOUD/PASCAL Multiplatform Observations to Unravel the Role of Clouds and Aerosol Particles in Arctic Amplification M. Wendisch et al. https://doi.org/10.1175/BAMS-D-18-0072.1
- Model simulations capture seasonal Arctic Haze and clean-air cycle better than satellite and reanalysis B. Swain et al. https://doi.org/10.1038/s41598-025-29188-8
- Innovative cloud quantification: deep learning classification and finite-sector clustering for ground-based all-sky imaging J. Luo et al. https://doi.org/10.5194/amt-17-3765-2024
- The retrieval of snow properties from SLSTR Sentinel-3 – Part 1: Method description and sensitivity study L. Mei et al. https://doi.org/10.5194/tc-15-2757-2021
- Assessment of health hazards of greenhouse workers considering UV exposure and thermal comfort M. Chowdhury et al. https://doi.org/10.1016/j.atech.2023.100319
- Multi-LEO Satellite Stereo Winds J. Carr et al. https://doi.org/10.3390/rs15082154
- Cloud-Tolerant Multiwidth Arctic Sea-Ice Lead Detection Using FY-3D MERSI-II 250-m TIR Data L. Zhang et al. https://doi.org/10.1109/TGRS.2025.3631915
- Performance Evaluation of Machine Learning Regression Models for Rainfall Prediction M. Abdullah & S. Said https://doi.org/10.1007/s40996-024-01691-4
- An extended lidar-based cirrus cloud retrieval scheme: first application over an Arctic site K. Nakoudi et al. https://doi.org/10.1364/OE.414770
- Retrieval of aerosol optical depth over the Arctic cryosphere during spring and summer using satellite observations B. Swain et al. https://doi.org/10.5194/amt-17-359-2024
- A Machine Learning Model for FY-4A Cloud Detection Based on Physical Feature Fusion Y. Liang et al. https://doi.org/10.3390/rs18040536
- Assessment of Direct Normal Irradiance Forecasts Based on IFS/ECMWF Data and Observations in the South of Portugal J. Perdigão et al. https://doi.org/10.3390/forecast2020007
- Insights of aerosol-precipitation nexus in the central Arctic through CMIP6 climate models B. Swain et al. https://doi.org/10.1038/s41612-025-00957-6
- Simulated reflectance above snow constrained by airborne measurements of solar radiation: implications for the snow grain morphology in the Arctic S. Jafariserajehlou et al. https://doi.org/10.5194/amt-14-369-2021
- Aerosols in the central Arctic cryosphere: satellite and model integrated insights during Arctic spring and summer B. Swain et al. https://doi.org/10.5194/acp-24-5671-2024
Saved (final revised paper)
Latest update: 01 Jun 2026
Short summary
We developed a new algorithm for cloud identification over the Arctic. This algorithm called ASCIA, utilizes time-series measurements of Advanced Along-Track Scanning Radiometer (AATSR) on Envisat and Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel-3A and -3B.
The data product of ASCIA is compared with three satellite products: ASCIA shows an improved performance compared to them. We validated ASCIA by ground-based measurements and a promising agreement is achieved.
We developed a new algorithm for cloud identification over the Arctic. This algorithm called...
