Articles | Volume 9, issue 2
https://doi.org/10.5194/amt-9-587-2016
© Author(s) 2016. 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-9-587-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
23 Feb 2016
Research article |
| 23 Feb 2016
A total sky cloud detection method using real clear sky background
Jun Yang
CORRESPONDING AUTHOR
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Atmospheric Sciences Research Center, State University of New York, Albany, NY 12203, USA
Qilong Min
Atmospheric Sciences Research Center, State University of New York, Albany, NY 12203, USA
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Weitao Lu
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Ying Ma
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Wen Yao
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Tianshu Lu
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Juan Du
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Guangyi Liu
Smart Grid Operation Research Center, China Electric Power Research Institute, Beijing 100192, China
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Cited
27 citations as recorded by crossref.
- Cloud detection methodologies: variants and development—a review S. Mahajan & B. Fataniya https://doi.org/10.1007/s40747-019-00128-0
- Hybrid prediction method of solar irradiance applied to short-term photovoltaic energy generation J. Nunes Maciel et al. https://doi.org/10.1016/j.rser.2023.114185
- Retrieval of Oceanic Total Precipitable Water Vapor and Cloud Liquid Water from Fengyun-3D Microwave Sounding Instruments Y. Han et al. https://doi.org/10.1007/s13351-021-0084-4
- Worldwide inter-comparison of clear-sky solar radiation models: Consensus-based review of direct and global irradiance components simulated at the earth surface J. Ruiz-Arias & C. Gueymard https://doi.org/10.1016/j.solener.2018.02.008
- On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval M. Krinitskiy et al. https://doi.org/10.3390/rs13020326
- Dataset for Machine Learning: Explicit All-Sky Image Features to Enhance Solar Irradiance Prediction J. Maciel et al. https://doi.org/10.3390/data9100113
- Retrieval of Atmospheric Profiles in the New York State Mesonet Using One‐Dimensional Variational Algorithm J. Yang & Q. Min https://doi.org/10.1029/2018JD028272
- A Novel Camera-Based Approach to Increase the Quality, Objectivity and Efficiency of Aeronautical Meteorological Observations J. Bartok et al. https://doi.org/10.3390/app12062925
- Twenty-four-hour cloud cover calculation using a ground-based imager with machine learning B. Kim et al. https://doi.org/10.5194/amt-14-6695-2021
- A Cloud Detection Algorithm with Reduction of Sunlight Interference in Ground-Based Sky Images X. Li et al. https://doi.org/10.3390/atmos10110640
- Cloud detection method using ground-based sky images based on clear sky library and superpixel local threshold Y. Niu et al. https://doi.org/10.1016/j.renene.2024.120452
- CloudU-Netv2: A Cloud Segmentation Method for Ground-Based Cloud Images Based on Deep Learning C. Shi et al. https://doi.org/10.1007/s11063-021-10457-2
- Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks C. Shi et al. https://doi.org/10.5194/amt-12-4713-2019
- CloudU-Net: A Deep Convolutional Neural Network Architecture for Daytime and Nighttime Cloud Images’ Segmentation C. Shi et al. https://doi.org/10.1109/LGRS.2020.3009227
- CloudFU-Net: A Fine-Grained Segmentation Method for Ground-Based Cloud Images Based on an Improved Encoder–Decoder Structure C. Shi et al. https://doi.org/10.1109/TGRS.2024.3389089
- From Noon to Sunset: Interactive Rendering, Relighting, and Recolouring of Landscape Photographs by Modifying Solar Position M. Türe et al. https://doi.org/10.1111/cgf.14392
- Hybrid Cloud Detection Algorithm Based on Intelligent Scene Recognition F. Li et al. https://doi.org/10.1175/JTECH-D-21-0159.1
- Automatic Classification of All-Sky Nighttime Cloud Images Based on Machine Learning X. Zhong et al. https://doi.org/10.3390/electronics13081503
- Cloud detection method based on clear sky background under multiple weather conditions J. Song et al. https://doi.org/10.1016/j.solener.2023.03.026
- ELIFAN, an algorithm for the estimation of cloud cover from sky imagers M. Lothon et al. https://doi.org/10.5194/amt-12-5519-2019
- An RGB channel operation for removal of the difference of atmospheric scattering and its application on total sky cloud detection J. Yang et al. https://doi.org/10.5194/amt-10-1191-2017
- Analyzing of Cloud Macroscopic Characteristics in the Shigatse Area of the Tibetan Plateau Using the Total-Sky Images J. Yang et al. https://doi.org/10.1175/JAMC-D-18-0095.1
- Per-pixel classification of clouds from whole sky HDR images P. Satilmis et al. https://doi.org/10.1016/j.image.2020.115950
- Semantic segmentation of terrestrial whole-sky images using the new W-Net model with the stationary wavelet transform 2D D. Fantini et al. https://doi.org/10.1016/j.array.2025.100587
- Intelligent classification of ground-based visible cloud images using a transfer convolutional neural network and fine-tuning M. Wang et al. https://doi.org/10.1364/OE.442455
- Assessments of Cloud Liquid Water and Total Precipitable Water Derived from FY-3E MWTS-III and NOAA-20 ATMS C. Dong et al. https://doi.org/10.3390/rs14081853
- Restoration of remote satellite sensing images using machine and deep learning: A survey M. Abdellaoui et al. https://doi.org/10.22630/MGV.2023.32.2.8
27 citations as recorded by crossref.
- Cloud detection methodologies: variants and development—a review S. Mahajan & B. Fataniya https://doi.org/10.1007/s40747-019-00128-0
- Hybrid prediction method of solar irradiance applied to short-term photovoltaic energy generation J. Nunes Maciel et al. https://doi.org/10.1016/j.rser.2023.114185
- Retrieval of Oceanic Total Precipitable Water Vapor and Cloud Liquid Water from Fengyun-3D Microwave Sounding Instruments Y. Han et al. https://doi.org/10.1007/s13351-021-0084-4
- Worldwide inter-comparison of clear-sky solar radiation models: Consensus-based review of direct and global irradiance components simulated at the earth surface J. Ruiz-Arias & C. Gueymard https://doi.org/10.1016/j.solener.2018.02.008
- On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval M. Krinitskiy et al. https://doi.org/10.3390/rs13020326
- Dataset for Machine Learning: Explicit All-Sky Image Features to Enhance Solar Irradiance Prediction J. Maciel et al. https://doi.org/10.3390/data9100113
- Retrieval of Atmospheric Profiles in the New York State Mesonet Using One‐Dimensional Variational Algorithm J. Yang & Q. Min https://doi.org/10.1029/2018JD028272
- A Novel Camera-Based Approach to Increase the Quality, Objectivity and Efficiency of Aeronautical Meteorological Observations J. Bartok et al. https://doi.org/10.3390/app12062925
- Twenty-four-hour cloud cover calculation using a ground-based imager with machine learning B. Kim et al. https://doi.org/10.5194/amt-14-6695-2021
- A Cloud Detection Algorithm with Reduction of Sunlight Interference in Ground-Based Sky Images X. Li et al. https://doi.org/10.3390/atmos10110640
- Cloud detection method using ground-based sky images based on clear sky library and superpixel local threshold Y. Niu et al. https://doi.org/10.1016/j.renene.2024.120452
- CloudU-Netv2: A Cloud Segmentation Method for Ground-Based Cloud Images Based on Deep Learning C. Shi et al. https://doi.org/10.1007/s11063-021-10457-2
- Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks C. Shi et al. https://doi.org/10.5194/amt-12-4713-2019
- CloudU-Net: A Deep Convolutional Neural Network Architecture for Daytime and Nighttime Cloud Images’ Segmentation C. Shi et al. https://doi.org/10.1109/LGRS.2020.3009227
- CloudFU-Net: A Fine-Grained Segmentation Method for Ground-Based Cloud Images Based on an Improved Encoder–Decoder Structure C. Shi et al. https://doi.org/10.1109/TGRS.2024.3389089
- From Noon to Sunset: Interactive Rendering, Relighting, and Recolouring of Landscape Photographs by Modifying Solar Position M. Türe et al. https://doi.org/10.1111/cgf.14392
- Hybrid Cloud Detection Algorithm Based on Intelligent Scene Recognition F. Li et al. https://doi.org/10.1175/JTECH-D-21-0159.1
- Automatic Classification of All-Sky Nighttime Cloud Images Based on Machine Learning X. Zhong et al. https://doi.org/10.3390/electronics13081503
- Cloud detection method based on clear sky background under multiple weather conditions J. Song et al. https://doi.org/10.1016/j.solener.2023.03.026
- ELIFAN, an algorithm for the estimation of cloud cover from sky imagers M. Lothon et al. https://doi.org/10.5194/amt-12-5519-2019
- An RGB channel operation for removal of the difference of atmospheric scattering and its application on total sky cloud detection J. Yang et al. https://doi.org/10.5194/amt-10-1191-2017
- Analyzing of Cloud Macroscopic Characteristics in the Shigatse Area of the Tibetan Plateau Using the Total-Sky Images J. Yang et al. https://doi.org/10.1175/JAMC-D-18-0095.1
- Per-pixel classification of clouds from whole sky HDR images P. Satilmis et al. https://doi.org/10.1016/j.image.2020.115950
- Semantic segmentation of terrestrial whole-sky images using the new W-Net model with the stationary wavelet transform 2D D. Fantini et al. https://doi.org/10.1016/j.array.2025.100587
- Intelligent classification of ground-based visible cloud images using a transfer convolutional neural network and fine-tuning M. Wang et al. https://doi.org/10.1364/OE.442455
- Assessments of Cloud Liquid Water and Total Precipitable Water Derived from FY-3E MWTS-III and NOAA-20 ATMS C. Dong et al. https://doi.org/10.3390/rs14081853
- Restoration of remote satellite sensing images using machine and deep learning: A survey M. Abdellaoui et al. https://doi.org/10.22630/MGV.2023.32.2.8
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