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
22 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.
- Liquid cloud optical property retrieval and associated uncertainties using multi-angular and bispectral measurements of the airborne radiometer OSIRIS C. Matar et al.
- Multi-View Polarimetric Scattering Cloud Tomography and Retrieval of Droplet Size A. Levis et al.
- Classification of Ice Crystal Habits Observed From Airborne Cloud Particle Imager by Deep Transfer Learning H. Xiao et al.
- Near‐Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects C. Yang et al.
- 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.
- 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.
- An Efficient Method for Microphysical Property Retrievals in Vertically Inhomogeneous Marine Water Clouds Using MODIS‐CloudSat Measurements M. Saito et al.
- Creating and Leveraging a Synthetic Dataset of Cloud Optical Thickness Measures for Cloud Detection in MSI A. Pirinen et al.
- Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements A. Nair & F. Yu
- Evaluation of Himawari-8 surface downwelling solar radiation by ground-based measurements A. Damiani et al.
- Considering the Effects of Horizontal Heterogeneities in Satellite-Based Large-Scale Statistics of Cloud Optical Properties T. Várnai & A. Marshak
- Remotely sensed big data: evolution in model development for information extraction [point of view] B. Zhang et al.
- Influence of cloud retrieval errors due to three-dimensional radiative effects on calculations of broadband shortwave cloud radiative effect A. Ademakinwa et al.
- Imagers for Spaceborne Cloud Tomography V. Holodovsky et al.
- Remote sensing of cloud droplet radius profiles using solar reflectance from cloud sides – Part 1: Retrieval development and characterization F. Ewald et al.
- Enhancing nighttime cloud detection for moderate resolution imagers using a transformer based deep learning network Y. Wu et al.
- Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach A. Di Noia et al.
- An Automatic Framework of Region‐of‐Interest Detection and Classification for Networked X‐Band Weather Radar System Z. Xu et al.
- 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
- Estimation of County-Level Winter Wheat Yield in China Using a Feature Conflict-Resolving TB-LSTM Model B. Zhao et al.
- Segmentation-based multi-pixel cloud optical thickness retrieval using a convolutional neural network V. Nataraja et al.
22 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.
- Liquid cloud optical property retrieval and associated uncertainties using multi-angular and bispectral measurements of the airborne radiometer OSIRIS C. Matar et al.
- Multi-View Polarimetric Scattering Cloud Tomography and Retrieval of Droplet Size A. Levis et al.
- Classification of Ice Crystal Habits Observed From Airborne Cloud Particle Imager by Deep Transfer Learning H. Xiao et al.
- Near‐Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects C. Yang et al.
- 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.
- 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.
- An Efficient Method for Microphysical Property Retrievals in Vertically Inhomogeneous Marine Water Clouds Using MODIS‐CloudSat Measurements M. Saito et al.
- Creating and Leveraging a Synthetic Dataset of Cloud Optical Thickness Measures for Cloud Detection in MSI A. Pirinen et al.
- Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements A. Nair & F. Yu
- Evaluation of Himawari-8 surface downwelling solar radiation by ground-based measurements A. Damiani et al.
- Considering the Effects of Horizontal Heterogeneities in Satellite-Based Large-Scale Statistics of Cloud Optical Properties T. Várnai & A. Marshak
- Remotely sensed big data: evolution in model development for information extraction [point of view] B. Zhang et al.
- Influence of cloud retrieval errors due to three-dimensional radiative effects on calculations of broadband shortwave cloud radiative effect A. Ademakinwa et al.
- Imagers for Spaceborne Cloud Tomography V. Holodovsky et al.
- Remote sensing of cloud droplet radius profiles using solar reflectance from cloud sides – Part 1: Retrieval development and characterization F. Ewald et al.
- Enhancing nighttime cloud detection for moderate resolution imagers using a transformer based deep learning network Y. Wu et al.
- Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach A. Di Noia et al.
- An Automatic Framework of Region‐of‐Interest Detection and Classification for Networked X‐Band Weather Radar System Z. Xu et al.
- 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
- Estimation of County-Level Winter Wheat Yield in China Using a Feature Conflict-Resolving TB-LSTM Model B. Zhao et al.
- Segmentation-based multi-pixel cloud optical thickness retrieval using a convolutional neural network V. Nataraja et al.
Saved (final revised paper)
Latest update: 06 May 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...
