Articles | Volume 10, issue 12
https://doi.org/10.5194/amt-10-4747-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-10-4747-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
05 Dec 2017
Research article |
| 05 Dec 2017
Feasibility study of multi-pixel retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning
Rintaro Okamura
Center for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University, 6-3 Aoba Aramaki-aza, Sendai, Miyagi, 980-8578, Japan
Center for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University, 6-3 Aoba Aramaki-aza, Sendai, Miyagi, 980-8578, Japan
K. Sebastian Schmidt
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA
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Cited
24 citations as recorded by crossref.
- Towards multi-views cloud retrieval accounting for the 3-D structure collected by directional polarization camera H. Yu et al. https://doi.org/10.1016/j.isprsjprs.2024.04.028
- Liquid cloud optical property retrieval and associated uncertainties using multi-angular and bispectral measurements of the airborne radiometer OSIRIS C. Matar et al. https://doi.org/10.5194/amt-16-3221-2023
- Multi-View Polarimetric Scattering Cloud Tomography and Retrieval of Droplet Size A. Levis et al. https://doi.org/10.3390/rs12172831
- Classification of Ice Crystal Habits Observed From Airborne Cloud Particle Imager by Deep Transfer Learning H. Xiao et al. https://doi.org/10.1029/2019EA000636
- Near‐Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects C. Yang et al. https://doi.org/10.1029/2022GL098274
- Retrieval of Cloud Optical Thickness from Sky-View Camera Images using a Deep Convolutional Neural Network based on Three-Dimensional Radiative Transfer R. Masuda et al. https://doi.org/10.3390/rs11171962
- The semi-diurnal cycle of deep convective systems over Eastern China and its surrounding seas in summer based on an automatic tracking algorithm W. Li et al. https://doi.org/10.1007/s00382-020-05474-1
- An Efficient Method for Microphysical Property Retrievals in Vertically Inhomogeneous Marine Water Clouds Using MODIS‐CloudSat Measurements M. Saito et al. https://doi.org/10.1029/2018JD029659
- Creating and Leveraging a Synthetic Dataset of Cloud Optical Thickness Measures for Cloud Detection in MSI A. Pirinen et al. https://doi.org/10.3390/rs16040694
- Errors in satellite-based global horizontal irradiance retrievals due to three-dimensional cloud-radiation interactions J. Wiltink et al. https://doi.org/10.5194/acp-26-7207-2026
- A Comprehensive Study of Estimating Atmospheric Cloud Microphysical Properties Using Deep Learning Techniques Z. Tushar et al. https://doi.org/10.3390/rs18111755
- Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements A. Nair & F. Yu https://doi.org/10.5194/acp-20-12853-2020
- Evaluation of Himawari-8 surface downwelling solar radiation by ground-based measurements A. Damiani et al. https://doi.org/10.5194/amt-11-2501-2018
- Considering the Effects of Horizontal Heterogeneities in Satellite-Based Large-Scale Statistics of Cloud Optical Properties T. Várnai & A. Marshak https://doi.org/10.3390/rs16183388
- Remotely sensed big data: evolution in model development for information extraction [point of view] B. Zhang et al. https://doi.org/10.1109/JPROC.2019.2948454
- Influence of cloud retrieval errors due to three-dimensional radiative effects on calculations of broadband shortwave cloud radiative effect A. Ademakinwa et al. https://doi.org/10.5194/acp-24-3093-2024
- Imagers for Spaceborne Cloud Tomography V. Holodovsky et al. https://doi.org/10.1109/TGRS.2025.3600736
- Remote sensing of cloud droplet radius profiles using solar reflectance from cloud sides – Part 1: Retrieval development and characterization F. Ewald et al. https://doi.org/10.5194/amt-12-1183-2019
- Enhancing nighttime cloud detection for moderate resolution imagers using a transformer based deep learning network Y. Wu et al. https://doi.org/10.1016/j.rse.2025.115067
- Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach A. Di Noia et al. https://doi.org/10.5194/amt-12-1697-2019
- An Automatic Framework of Region‐of‐Interest Detection and Classification for Networked X‐Band Weather Radar System Z. Xu et al. https://doi.org/10.1029/2020EA001181
- Applicability of a Neural Network Approach to Retrieving the Optical Thickness and Effective Radius of Droplets in Single-Layer Horizontally Inhomogeneous Cloudiness T. Russkova & A. Skorokhodov https://doi.org/10.1134/S1024856024701240
- Estimation of County-Level Winter Wheat Yield in China Using a Feature Conflict-Resolving TB-LSTM Model B. Zhao et al. https://doi.org/10.3390/rs18030447
- Segmentation-based multi-pixel cloud optical thickness retrieval using a convolutional neural network V. Nataraja et al. https://doi.org/10.5194/amt-15-5181-2022
24 citations as recorded by crossref.
- Towards multi-views cloud retrieval accounting for the 3-D structure collected by directional polarization camera H. Yu et al. https://doi.org/10.1016/j.isprsjprs.2024.04.028
- Liquid cloud optical property retrieval and associated uncertainties using multi-angular and bispectral measurements of the airborne radiometer OSIRIS C. Matar et al. https://doi.org/10.5194/amt-16-3221-2023
- Multi-View Polarimetric Scattering Cloud Tomography and Retrieval of Droplet Size A. Levis et al. https://doi.org/10.3390/rs12172831
- Classification of Ice Crystal Habits Observed From Airborne Cloud Particle Imager by Deep Transfer Learning H. Xiao et al. https://doi.org/10.1029/2019EA000636
- Near‐Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects C. Yang et al. https://doi.org/10.1029/2022GL098274
- Retrieval of Cloud Optical Thickness from Sky-View Camera Images using a Deep Convolutional Neural Network based on Three-Dimensional Radiative Transfer R. Masuda et al. https://doi.org/10.3390/rs11171962
- The semi-diurnal cycle of deep convective systems over Eastern China and its surrounding seas in summer based on an automatic tracking algorithm W. Li et al. https://doi.org/10.1007/s00382-020-05474-1
- An Efficient Method for Microphysical Property Retrievals in Vertically Inhomogeneous Marine Water Clouds Using MODIS‐CloudSat Measurements M. Saito et al. https://doi.org/10.1029/2018JD029659
- Creating and Leveraging a Synthetic Dataset of Cloud Optical Thickness Measures for Cloud Detection in MSI A. Pirinen et al. https://doi.org/10.3390/rs16040694
- Errors in satellite-based global horizontal irradiance retrievals due to three-dimensional cloud-radiation interactions J. Wiltink et al. https://doi.org/10.5194/acp-26-7207-2026
- A Comprehensive Study of Estimating Atmospheric Cloud Microphysical Properties Using Deep Learning Techniques Z. Tushar et al. https://doi.org/10.3390/rs18111755
- Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements A. Nair & F. Yu https://doi.org/10.5194/acp-20-12853-2020
- Evaluation of Himawari-8 surface downwelling solar radiation by ground-based measurements A. Damiani et al. https://doi.org/10.5194/amt-11-2501-2018
- Considering the Effects of Horizontal Heterogeneities in Satellite-Based Large-Scale Statistics of Cloud Optical Properties T. Várnai & A. Marshak https://doi.org/10.3390/rs16183388
- Remotely sensed big data: evolution in model development for information extraction [point of view] B. Zhang et al. https://doi.org/10.1109/JPROC.2019.2948454
- Influence of cloud retrieval errors due to three-dimensional radiative effects on calculations of broadband shortwave cloud radiative effect A. Ademakinwa et al. https://doi.org/10.5194/acp-24-3093-2024
- Imagers for Spaceborne Cloud Tomography V. Holodovsky et al. https://doi.org/10.1109/TGRS.2025.3600736
- Remote sensing of cloud droplet radius profiles using solar reflectance from cloud sides – Part 1: Retrieval development and characterization F. Ewald et al. https://doi.org/10.5194/amt-12-1183-2019
- Enhancing nighttime cloud detection for moderate resolution imagers using a transformer based deep learning network Y. Wu et al. https://doi.org/10.1016/j.rse.2025.115067
- Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach A. Di Noia et al. https://doi.org/10.5194/amt-12-1697-2019
- An Automatic Framework of Region‐of‐Interest Detection and Classification for Networked X‐Band Weather Radar System Z. Xu et al. https://doi.org/10.1029/2020EA001181
- Applicability of a Neural Network Approach to Retrieving the Optical Thickness and Effective Radius of Droplets in Single-Layer Horizontally Inhomogeneous Cloudiness T. Russkova & A. Skorokhodov https://doi.org/10.1134/S1024856024701240
- Estimation of County-Level Winter Wheat Yield in China Using a Feature Conflict-Resolving TB-LSTM Model B. Zhao et al. https://doi.org/10.3390/rs18030447
- Segmentation-based multi-pixel cloud optical thickness retrieval using a convolutional neural network V. Nataraja et al. https://doi.org/10.5194/amt-15-5181-2022
Saved (final revised paper)
Latest update: 07 Jun 2026
Short summary
Three-dimensional (3-D) radiative transfer effects are a major source of retrieval errors in satellite-based optical remote sensing of clouds. Multi-pixel, multispectral approaches based on deep learning are proposed for retrieval of cloud optical thickness and droplet effective radius. A feasibility test shows that proposed retrieval methods are effective to obtain accurate cloud properties. Use of the convolutional neural network is effective to reduce 3-D radiative transfer effects.
Three-dimensional (3-D) radiative transfer effects are a major source of retrieval errors in...
