Articles | Volume 12, issue 3
https://doi.org/10.5194/amt-12-1697-2019
© 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-12-1697-2019
© 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 Mar 2019
Research article |
| 18 Mar 2019
| 18 Mar 2019
Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach
SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584CA Utrecht, the Netherlands
now at: Department of Physics and Astronomy, University of Leicester, University Road, LE1 7RH Leicester, UK
Otto P. Hasekamp
SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584CA Utrecht, the Netherlands
Bastiaan van Diedenhoven
Center for Climate Systems Research, Columbia University, 2910 Broadway, New York, NY 10025, USA
NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10025, USA
Zhibo Zhang
Physics Department, University of Maryland – Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21228, USA
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Cited
14 citations as recorded by crossref.
- Version 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part II: Results over oceans A. Garnier et al. 10.5194/amt-14-3277-2021
- Techniques and Models for Addressing Occupational Risk Using Fuzzy Logic, Neural Networks, Machine Learning, and Genetic Algorithms: A Review and Meta-Analysis C. Mitrakas et al. 10.3390/app15041909
- Global quantification of the dispersion effect with POLDER satellite data H. Wang et al. 10.1038/s41467-025-62238-3
- Spatial distribution of cloud droplet size properties from Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) measurements B. McBride et al. 10.5194/amt-13-1777-2020
- Sensitivity of liquid cloud optical thickness and effective radius retrievals to cloud bow and glory conditions using two SEVIRI imagers N. Benas et al. 10.5194/amt-12-2863-2019
- Validation of GRASP algorithm product from POLDER/PARASOL data and assessment of multi-angular polarimetry potential for aerosol monitoring C. Chen et al. 10.5194/essd-12-3573-2020
- Confronting the Challenge of Modeling Cloud and Precipitation Microphysics H. Morrison et al. 10.1029/2019MS001689
- Above Cloud Aerosol Detection and Retrieval from Multi-Angular Polarimetric Satellite Measurements in a Neural Network Ensemble Approach Z. Yuan et al. 10.5194/amt-18-5415-2025
- Settings for Spaceborne 3-D Scattering Tomography of Liquid-Phase Clouds by the CloudCT Mission M. Tzabari et al. 10.1109/TGRS.2022.3198525
- Retrievals of Biomass Burning Aerosol and Liquid Cloud Properties from Polarimetric Observations Using Deep Learning Techniques M. Segal Rozenhaimer et al. 10.3390/rs17101693
- High-spatial-resolution retrieval of cloud droplet size distribution from polarized observations of the cloudbow V. Pörtge et al. 10.5194/amt-16-645-2023
- Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm D. Miller et al. 10.5194/amt-13-3447-2020
- Cloud detection from multi-angular polarimetric satellite measurements using a neural network ensemble approach Z. Yuan et al. 10.5194/amt-17-2595-2024
- Frontiers in Satellite‐Based Estimates of Cloud‐Mediated Aerosol Forcing D. Rosenfeld et al. 10.1029/2022RG000799
14 citations as recorded by crossref.
- Version 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part II: Results over oceans A. Garnier et al. 10.5194/amt-14-3277-2021
- Techniques and Models for Addressing Occupational Risk Using Fuzzy Logic, Neural Networks, Machine Learning, and Genetic Algorithms: A Review and Meta-Analysis C. Mitrakas et al. 10.3390/app15041909
- Global quantification of the dispersion effect with POLDER satellite data H. Wang et al. 10.1038/s41467-025-62238-3
- Spatial distribution of cloud droplet size properties from Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) measurements B. McBride et al. 10.5194/amt-13-1777-2020
- Sensitivity of liquid cloud optical thickness and effective radius retrievals to cloud bow and glory conditions using two SEVIRI imagers N. Benas et al. 10.5194/amt-12-2863-2019
- Validation of GRASP algorithm product from POLDER/PARASOL data and assessment of multi-angular polarimetry potential for aerosol monitoring C. Chen et al. 10.5194/essd-12-3573-2020
- Confronting the Challenge of Modeling Cloud and Precipitation Microphysics H. Morrison et al. 10.1029/2019MS001689
- Above Cloud Aerosol Detection and Retrieval from Multi-Angular Polarimetric Satellite Measurements in a Neural Network Ensemble Approach Z. Yuan et al. 10.5194/amt-18-5415-2025
- Settings for Spaceborne 3-D Scattering Tomography of Liquid-Phase Clouds by the CloudCT Mission M. Tzabari et al. 10.1109/TGRS.2022.3198525
- Retrievals of Biomass Burning Aerosol and Liquid Cloud Properties from Polarimetric Observations Using Deep Learning Techniques M. Segal Rozenhaimer et al. 10.3390/rs17101693
- High-spatial-resolution retrieval of cloud droplet size distribution from polarized observations of the cloudbow V. Pörtge et al. 10.5194/amt-16-645-2023
- Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm D. Miller et al. 10.5194/amt-13-3447-2020
- Cloud detection from multi-angular polarimetric satellite measurements using a neural network ensemble approach Z. Yuan et al. 10.5194/amt-17-2595-2024
- Frontiers in Satellite‐Based Estimates of Cloud‐Mediated Aerosol Forcing D. Rosenfeld et al. 10.1029/2022RG000799
Latest update: 24 Oct 2025
Short summary
We present a neural network algorithm for the retrieval of cloud physical properties from multi-angle polarimetric measurements. We have trained the algorithm on a large dataset of synthetic measurements and applied it to a year of POLDER-3 data. A comparison against MODIS cloud products reveals that our algorithm is capable of performing cloud property retrievals on a global scale and possibly improves the estimates of cloud effective radius over land with respect to existing POLDER-3 products.
We present a neural network algorithm for the retrieval of cloud physical properties from...
