236 citations as recorded by crossref.

1. The thin border between cloud and aerosol: Sensitivity of several ground based observation techniques J. Calbó et al. https://doi.org/10.1016/j.atmosres.2017.06.010
2. Learning Discriminative Features for Ground-Based Cloud Classification via Mutual Information Maximization S. LIU et al. https://doi.org/10.1587/transinf.2014EDL8252
3. Cloud Pattern Analysis Using AI and Machine Learning from Satellite Images . M. Sravani & . K.Krishna Murthy https://doi.org/10.32628/CSEIT25111704
4. CAFTrans: Content-Aware Fusion Transformer for ground-based remote sensing cloud detection S. Liu et al. https://doi.org/10.1016/j.jag.2025.105044
5. Research on simulation and validation methods of aerosol radiative forcing on the Tibetan Plateau based on satellite and ground-based remote sensing observations over the past 20 years L. Wu et al. https://doi.org/10.1016/j.atmosres.2024.107683
6. The state of the atmosphere in the 2016 southern Kerguelen Axis campaign region A. Klekociuk et al. https://doi.org/10.1016/j.dsr2.2019.02.001
7. A review on deep learning techniques for cloud detection methodologies and challenges L. Li et al. https://doi.org/10.1007/s11760-021-01885-7
8. A two-branch cloud detection algorithm based on the fusion of a feature enhancement module and Gaussian mixture model F. Zhou et al. https://doi.org/10.3934/mbe.2023955
9. Intrahour Cloud Tracking Based on Probability Hypothesis Density Filtering F. Barbieri et al. https://doi.org/10.1109/TSTE.2017.2733258
10. The use of a sky camera for solar radiation estimation based on digital image processing J. Alonso-Montesinos & F. Batlles https://doi.org/10.1016/j.energy.2015.07.028
11. 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
12. Short and medium-term cloudiness forecasting using remote sensing techniques and sky camera imagery J. Alonso & F. Batlles https://doi.org/10.1016/j.energy.2014.06.101
13. Sky camera imagery processing based on a sky classification using radiometric data J. Alonso et al. https://doi.org/10.1016/j.energy.2014.02.035
14. 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
15. Quantifying the impact of cloud cover on ground radiation flux measurements using hemispherical images L. Roupioz et al. https://doi.org/10.1080/01431161.2015.1084440
16. The effect of cloud liquid water on tropospheric temperature retrievals from microwave measurements L. Bernet et al. https://doi.org/10.5194/amt-10-4421-2017
17. Machine Learning Models for Approximating Downward Short-Wave Radiation Flux over the Ocean from All-Sky Optical Imagery Based on DASIO Dataset M. Krinitskiy et al. https://doi.org/10.3390/rs15071720
18. Pixel‐Based Image Processing for CIE Standard Sky Classification through ANN D. Granados-López et al. https://doi.org/10.1155/2021/2636157
19. Deep multimodal fusion for ground-based cloud classification in weather station networks S. Liu & M. Li https://doi.org/10.1186/s13638-018-1062-0
20. Can photovoltaics be used to estimate cloud cover? S. Stylianou et al. https://doi.org/10.1080/14786451.2020.1777129
21. Field trial of an automated ground‐based infrared cloud classification system E. Rumi et al. https://doi.org/10.1002/met.1523
22. Detection of cirrus clouds by stereovision system M. Kouahla et al. https://doi.org/10.33793/acperpro.01.01.164
23. Short-term solar radiation forecasting with a novel image processing-based deep learning approach A. Eşlik et al. https://doi.org/10.1016/j.renene.2022.10.063
24. A Review and Evaluation of the State of Art in Image-Based Solar Energy Forecasting: The Methodology and Technology Used C. Travieso-González et al. https://doi.org/10.3390/app14135605
25. A Cloud Motion Estimation Method Based on Cloud Image Depth Feature Matching L. Zou et al. https://doi.org/10.1109/LGRS.2024.3491094
26. Renewable energy powered membrane technology: Integration of solar irradiance forecasting for predictive control of photovoltaic-powered brackish water desalination system M. Ansong et al. https://doi.org/10.1016/j.apenergy.2025.126651
27. Research on ground-based cloud image classification combining local and global features X. Zhang et al. https://doi.org/10.1117/1.JEI.33.4.043030
28. Salient local binary pattern for ground-based cloud classification S. Liu et al. https://doi.org/10.1007/s13351-013-0206-8
29. Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey Y. Nie et al. https://doi.org/10.1016/j.rser.2023.113977
30. Supervised Fine-Grained Cloud Detection and Recognition in Whole-Sky Images L. Ye et al. https://doi.org/10.1109/TGRS.2019.2917612
31. Voting in Transfer Learning System for Ground-Based Cloud Classification M. Manzo & S. Pellino https://doi.org/10.3390/make3030028
32. Real-time prediction intervals for intra-hour DNI forecasts Y. Chu et al. https://doi.org/10.1016/j.renene.2015.04.022
33. Estimating Effect of Sheltering on Horizontal Measurement of Global Solar Radiation Using a Pyranometer Y. Chung et al. https://doi.org/10.3390/atmos17060556
34. A Fog Covered Object Recognition Algorithm Based On Space And Frequency Network Y. Cui et al. https://doi.org/10.32604/iasc.2021.016802
35. Cloud Segmentation, Validation of Weather Data, and Precipitation Prediction Using Machine Learning Algorithms N. Rajendiran et al. https://doi.org/10.1007/s13369-023-08611-0
36. Short-term cloud coverage prediction using the ARIMA time series model Y. Wang et al. https://doi.org/10.1080/2150704X.2017.1418992
37. Development of an all-sky imaging system for cloud cover assessment T. Fa et al. https://doi.org/10.1364/AO.58.005516
38. Multimodal Ground-Based Remote Sensing Cloud Classification via Learning Heterogeneous Deep Features S. Liu et al. https://doi.org/10.1109/TGRS.2020.2984265
39. Research of classification of cloud and aerosol using multi-axis differential optical absorption spectroscopy . Wang Yang et al. https://doi.org/10.7498/aps.63.110708
40. Ground-based measurements of local cloud cover A. Silva & M. de Souza Echer https://doi.org/10.1007/s00703-013-0245-9
41. A Hybrid Thresholding Algorithm for Cloud Detection on Ground-Based Color Images Q. Li et al. https://doi.org/10.1175/JTECH-D-11-00009.1
42. Determination of the sun area in sky camera images using radiometric data J. Alonso et al. https://doi.org/10.1016/j.enconman.2013.10.050
43. A systematic synthesis of sky image enhancement techniques for ground-based solar irradiance forecasting M. Piechocki & M. Kraft https://doi.org/10.1016/j.apenergy.2026.127533
44. Using sky-classification to improve the short-term prediction of irradiance with sky images and convolutional neural networks V. Martinez Lopez et al. https://doi.org/10.1016/j.solener.2024.112320
45. Multimodal GAN for Energy Efficiency and Cloud Classification in Internet of Things S. Liu & M. Li https://doi.org/10.1109/JIOT.2018.2866328
46. Assessment of Nighttime Cloud Cover Products from MODIS and Himawari-8 Data with Ground-Based Camera Observations N. Lagrosas et al. https://doi.org/10.3390/rs14040960
47. CloudDenseNet: Lightweight Ground-Based Cloud Classification Method for Large-Scale Datasets Based on Reconstructed DenseNet S. Li et al. https://doi.org/10.3390/s23187957
48. Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods F. Lin et al. https://doi.org/10.1016/j.ijforecast.2021.11.002
49. Research on Cloud Observation Multi-Task Network Optimization Method Based on Gradient-Driven Sparsification H. Zhu et al. https://doi.org/10.1088/1742-6596/3249/1/012024
50. Automatic Cloud‐Type Classification Based On the Combined Use of a Sky Camera and a Ceilometer J. Huertas‐Tato et al. https://doi.org/10.1002/2017JD027131
51. Equipment and methodologies for cloud detection and classification: A review R. Tapakis & A. Charalambides https://doi.org/10.1016/j.solener.2012.11.015
52. Multi-model solar irradiance prediction based on automatic cloud classification H. Cheng & C. Yu https://doi.org/10.1016/j.energy.2015.08.075
53. Measuring high-resolution sky luminance distributions with a CCD camera K. Tohsing et al. https://doi.org/10.1364/AO.52.001564
54. 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
55. CloudA: A Ground-Based Cloud Classification Method with a Convolutional Neural Network M. Wang et al. https://doi.org/10.1175/JTECH-D-19-0189.1
56. Ground-Based Cloud Detection Using Automatic Graph Cut . Shuang Liu et al. https://doi.org/10.1109/LGRS.2015.2399857
57. Predicting solar irradiance with all-sky image features via regression C. Fu & H. Cheng https://doi.org/10.1016/j.solener.2013.09.016
58. Determination of the optimal camera distance for cloud height measurements with two all-sky imagers P. Kuhn et al. https://doi.org/10.1016/j.solener.2018.12.038
59. Fuzzy statistics-based affinity propagation technique for clustering in satellite cloud image G. Devika & S. Parthasarathy https://doi.org/10.1080/22797254.2018.1482731
60. Method of identifying the lengths of equivalent clear-sky periods in the time series of DNI measurements based on generalized atmospheric turbidity H. Liu et al. https://doi.org/10.1016/j.renene.2018.12.119
61. Intra-hour irradiance forecasting techniques for solar power integration: A review Y. Chu et al. https://doi.org/10.1016/j.isci.2021.103136
62. ALGA-DenseNet ground-based cloud classification network based on multi-scale features B. Tu et al. https://doi.org/10.1371/journal.pone.0333999
63. Solar radiation forecasting in the short- and medium-term under all sky conditions J. Alonso-Montesinos & F. Batlles https://doi.org/10.1016/j.energy.2015.02.036
64. Cloud observations in Switzerland using hemispherical sky cameras S. Wacker et al. https://doi.org/10.1002/2014JD022643
65. Real-Time Uncertainty Specification of All Sky Imager Derived Irradiance Nowcasts B. Nouri et al. https://doi.org/10.3390/rs11091059
66. An all-sky camera image classification method using cloud cover features X. Li et al. https://doi.org/10.5194/amt-15-3629-2022
67. The effect of clouds on surface solar irradiance, based on data from an all-sky imaging system P. Tzoumanikas et al. https://doi.org/10.1016/j.renene.2016.04.026
68. MMST: A Multi-Modal Ground-Based Cloud Image Classification Method L. Wei et al. https://doi.org/10.3390/s23094222
69. Fusing CNNs and statistical indicators to improve image classification J. Huertas-Tato et al. https://doi.org/10.1016/j.inffus.2021.09.012
70. Deep Convolutional Activations-Based Features for Ground-Based Cloud Classification C. Shi et al. https://doi.org/10.1109/LGRS.2017.2681658
71. Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques H. Cheng & C. Lin https://doi.org/10.5194/amt-10-199-2017
72. Generation of spatially dispersed irradiance time-series based on real cloud patterns M. Jazayeri et al. https://doi.org/10.1016/j.solener.2017.10.026
73. 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
74. Using a Multi-view Convolutional Neural Network to monitor solar irradiance J. Huertas-Tato et al. https://doi.org/10.1007/s00521-021-05959-y
75. Ground-Based Cloud Image Segmentation Method Based on Improved U-Net D. Yin et al. https://doi.org/10.3390/app142311280
76. Compact Sparse Coding for Ground-Based Cloud Classification S. LIU et al. https://doi.org/10.1587/transinf.2015EDL8095
77. A comprehensive analysis of blue-to-red atmospheric signatures from all-sky imagery: CLARIS, an adaptive cloud-cover framework P. Valdelomar et al. https://doi.org/10.1016/j.atmosres.2026.109131
78. Applying self-supervised learning for semantic cloud segmentation of all-sky images Y. Fabel et al. https://doi.org/10.5194/amt-15-797-2022
79. Hierarchical Fusion Transformer for Multimodal Ground-Based Cloud Type Classification S. Liu et al. https://doi.org/10.1109/JSTARS.2025.3614756
80. A Smart Image-Based Cloud Detection System for Intrahour Solar Irradiance Forecasts Y. Chu et al. https://doi.org/10.1175/JTECH-D-13-00209.1
81. Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts T. Schmidt et al. https://doi.org/10.5194/acp-16-3399-2016
82. Review of deep learning-based ground-sky imagery method for intra-hour solar irradiance forecasting: Workflow and perspectives L. Zhang et al. https://doi.org/10.1016/j.rser.2026.117160
83. Cloud segmentation property extraction from total sky image repositories using Python D. Igoe et al. https://doi.org/10.1080/10739149.2019.1603996
84. Distribution-Aware Interactive Attention Network and Large-Scale Cloud Recognition Benchmark on FY-4A Satellite Image J. Zhang et al. https://doi.org/10.1109/TGRS.2024.3453376
85. TransFCloudNet: a dual-branch feature fusion ground-based cloud image fine-grained segmentation method for photovoltaic power prediction Z. Su et al. https://doi.org/10.1016/j.enconman.2025.120647
86. Multimodal Ground-Based Cloud Classification Using Joint Fusion Convolutional Neural Network S. Liu et al. https://doi.org/10.3390/rs10060822
87. 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
88. 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
89. Applications of the Gray Degree-Based Factor Analysis on Cloud Image to Improve the Accuracy of Weather Recognition G. Fan et al. https://doi.org/10.1007/s40995-017-0449-9
90. Very short-term photovoltaic power forecasting with cloud modeling: A review F. Barbieri et al. https://doi.org/10.1016/j.rser.2016.10.068
91. Integration of cloud top heights retrieved from FY-2 meteorological satellite, radiosonde, and ground-based millimeter wavelength cloud radar observations Y. Wang et al. https://doi.org/10.1016/j.atmosres.2018.07.025
92. Ground-Based Remote Sensing Cloud Classification via Context Graph Attention Network S. Liu et al. https://doi.org/10.1109/TGRS.2021.3063255
93. Cloud Discrimination from Sky Images Using a Clear-Sky Index M. Saito & H. Iwabuchi https://doi.org/10.1175/JTECH-D-15-0204.1
94. Ground-Based Cloud Detection Using Graph Model Built Upon Superpixels C. Shi et al. https://doi.org/10.1109/LGRS.2017.2676007
95. 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
96. 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
97. Integration Transformer for Ground-Based Cloud Image Segmentation S. Liu et al. https://doi.org/10.1109/TGRS.2023.3265384
98. Observation of atmospheric gravity waves using a Raspberry Pi camera module on board the International Space Station T. Magalhães et al. https://doi.org/10.1016/j.actaastro.2021.02.022
99. Development of a cloud detection method from whole-sky color images M. Yabuki et al. https://doi.org/10.1016/j.polar.2014.07.004
100. Ground-based detection of nighttime clouds above Manila Observatory (1464°N, 12107°E) using a digital camera G. Gacal et al. https://doi.org/10.1364/AO.55.006040
101. CloudNet: Ground‐Based Cloud Classification With Deep Convolutional Neural Network J. Zhang et al. https://doi.org/10.1029/2018GL077787
102. A 3D ConvLSTM-CNN network based on multi-channel color extraction for ultra-short-term solar irradiance forecasting X. Huang et al. https://doi.org/10.1016/j.energy.2023.127140
103. Afyonkarahisar Bölgesi Şartlarında Bulut Hareketlerinin Gökyüzü Sınıfları Tabanlı Tahmini A. EŞLİK et al. https://doi.org/10.28948/ngumuh.872533
104. Cloud Image Classification Method Based on Deep Convolutional Neural Network F. Zhang & J. Yan https://doi.org/10.1051/jnwpu/20203840740
105. SegCloud: a novel cloud image segmentation model using a deep convolutional neural network for ground-based all-sky-view camera observation W. Xie et al. https://doi.org/10.5194/amt-13-1953-2020
106. Storm Classification and Dynamic Targeting for a Smart Ice Cloud Sensing Satellite J. Swope et al. https://doi.org/10.2514/1.I011318
107. Ensemble Meteorological Cloud Classification Meets Internet of Dependable and Controllable Things J. Zhang et al. https://doi.org/10.1109/JIOT.2020.3043289
108. An Automated Cirrus Cloud Detection Method for a Ground-Based Cloud Image J. Yang et al. https://doi.org/10.1175/JTECH-D-11-00002.1
109. Radiative budget and cloud radiative effect over the Atlantic from ship-based observations J. Kalisch & A. Macke https://doi.org/10.5194/amt-5-2391-2012
110. Regional high‐resolution cloud climatology based onMODIScloud detection data A. Kotarba https://doi.org/10.1002/joc.4539
111. A High-Accuracy Model Average Ensemble of Convolutional Neural Networks for Classification of Cloud Image Patches on Small Datasets V. Phung & E. Rhee https://doi.org/10.3390/app9214500
112. Coupled atmospheric radiative transfer–machine learning retrieval of cloud shortwave radiative forcing over the Qinghai–Xizang Plateau L. Wu et al. https://doi.org/10.1016/j.atmosres.2026.109065
113. Cloud Observation and Cloud Cover Calculation at Nighttime Using the Automatic Cloud Observation System (ACOS) Package B. Kim & J. Cha https://doi.org/10.3390/rs12142314
114. Cloud Detection Methodology Based on a Sky-imaging System R. Chauvin et al. https://doi.org/10.1016/j.egypro.2015.03.198
115. A Cloud Classification Method Based on a Convolutional Neural Network for FY-4A Satellites Y. Jiang et al. https://doi.org/10.3390/rs14102314
116. Cloud fraction estimation using random forest classifier on sky images S. Sarangi et al. https://doi.org/10.5194/amt-18-5637-2025
117. Automatic Cloud Detection for All-Sky Images Using Superpixel Segmentation . Shuang Liu et al. https://doi.org/10.1109/LGRS.2014.2341291
118. Estimation of Global and Diffuse Photosynthetic Photon Flux Density under Various Sky Conditions Using Ground-Based Whole-Sky Images M. Yamashita & M. Yoshimura https://doi.org/10.3390/rs11080932
119. CloudSwinNet: A hybrid CNN-transformer framework for ground-based cloud images fine-grained segmentation C. Shi et al. https://doi.org/10.1016/j.energy.2024.133128
120. Ground-based cloud classification by learning stable local binary patterns Y. Wang et al. https://doi.org/10.1016/j.atmosres.2018.02.023
121. 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
122. Per-pixel classification of clouds from whole sky HDR images P. Satilmis et al. https://doi.org/10.1016/j.image.2020.115950
123. Cloud Type Classification of Total-Sky Images Using Duplex Norm-Bounded Sparse Coding J. Gan et al. https://doi.org/10.1109/JSTARS.2017.2669206
124. Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data L. Barancsuk et al. https://doi.org/10.3390/en17020438
125. Adaptive Graph Cut Based Cloud Detection in Wireless Sensor Networks S. Liu & Z. Zhang https://doi.org/10.1155/2015/947169
126. Halo ratio from ground-based all-sky imaging P. Dandini et al. https://doi.org/10.5194/amt-12-1295-2019
127. Estimating multidirectional cloud movements from single sky camera using directional statistics H. Wakisaka et al. https://doi.org/10.1016/j.solener.2024.112802
128. Deep CNN Feature Resampling and Ensemble Based on Cross Validation for Image Classification Y. Wang et al. https://doi.org/10.1109/TNNLS.2025.3547228
129. Ground-based all-sky mid-infrared and visible imagery for purposes of characterizing cloud properties D. Klebe et al. https://doi.org/10.5194/amt-7-637-2014
130. Block-based cloud classification with statistical features and distribution of local texture features H. Cheng & C. Yu https://doi.org/10.5194/amt-8-1173-2015
131. A ground-based cloud image classification method based on an improved MobileViT model D. Song et al. https://doi.org/10.1016/j.asr.2026.01.027
132. Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network W. Zhu et al. https://doi.org/10.3390/app12031570
133. CloudY-Net: A Deep Convolutional Neural Network Architecture for Joint Segmentation and Classification of Ground-Based Cloud Images F. Hu et al. https://doi.org/10.3390/atmos14091405
134. Cloud segmentation in astronomical images using gray level co-occurrence matrix and mini-batch k-means H. Fu et al. https://doi.org/10.1016/j.fmre.2025.12.019
135. Estimating Geo‐Referenced Cloud‐Base Height With Whole‐Sky Imagers B. Lyu et al. https://doi.org/10.1029/2019EA000944
136. Hybrid modeling of direct normal irradiance for rooftop photovoltaic systems using multi-feature extraction from cloud images J. Zhu et al. https://doi.org/10.1016/j.jobe.2025.112571
137. ResAG-UNet: A Novel Residual Attention Gated UNet for Cloud Segmentation in Sky Image A. Kumar et al. https://doi.org/10.1109/JPHOTOV.2024.3485188
138. 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
139. Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth F. Mejia et al. https://doi.org/10.5194/amt-9-4151-2016
140. Extraction, classification and visualization of 3-dimensional clouds simulated by cloud-resolving atmospheric model D. Matsuoka https://doi.org/10.1142/S1793962317500519
141. Ground‐Based Cloud Classification Using Task‐Based Graph Convolutional Network S. Liu et al. https://doi.org/10.1029/2020GL087338
142. Clouds over Hyytiälä, Finland: an algorithm to classify clouds based on solar radiation and cloud base height measurements I. Ylivinkka et al. https://doi.org/10.5194/amt-13-5595-2020
143. Automated construction of clear-sky dictionary from all-sky imager data P. Shaffery et al. https://doi.org/10.1016/j.solener.2020.10.052
144. CloudViT: A Lightweight Ground-Based Cloud Image Classification Model with the Ability to Capture Global Features D. Wei et al. https://doi.org/10.32604/cmc.2025.061402
145. Tensor Ensemble of Ground-Based Cloud Sequences: Its Modeling, Classification, and Synthesis . Shuang Liu et al. https://doi.org/10.1109/LGRS.2012.2236073
146. Shadow camera system for the generation of solar irradiance maps P. Kuhn et al. https://doi.org/10.1016/j.solener.2017.05.074
147. Cloud tracking using clusters of feature points for accurate solar irradiance nowcasting H. Cheng https://doi.org/10.1016/j.renene.2016.12.023
148. From pixels to patches: a cloud classification method based on a bag of micro-structures Q. Li et al. https://doi.org/10.5194/amt-9-753-2016
149. Analysis of Measured Drop Size Spectra over Land and Sea K. Bumke & J. Seltmann https://doi.org/10.5402/2012/296575
150. Ground-based image analysis: A tutorial on machine-learning techniques and applications S. Dev et al. https://doi.org/10.1109/MGRS.2015.2510448
151. Interactive landscape–scale cloud animation using DCGAN P. Goswami et al. https://doi.org/10.3389/fcomp.2023.957920
152. Algorithms and uncertainties for the determination of multispectral irradiance components and aerosol optical depth from a shipborne rotating shadowband radiometer J. Witthuhn et al. https://doi.org/10.5194/amt-10-709-2017
153. Ultra-short-term photovoltaic power prediction based on ground-based cloud images: A review C. Shi et al. https://doi.org/10.1016/j.apenergy.2025.126943
154. Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed C. Chow et al. https://doi.org/10.1016/j.solener.2011.08.025
155. Hybrid Cloud Detection Algorithm Based on Intelligent Scene Recognition F. Li et al. https://doi.org/10.1175/JTECH-D-21-0159.1
156. A dual attentional skip connection based Swin‐UNet for real‐time cloud segmentation F. Wei et al. https://doi.org/10.1049/ipr2.13186
157. Information integration for ground-based cloud classification using joint consistent sparse coding in heterogeneous sensor network S. Liu et al. https://doi.org/10.1016/j.sigpro.2015.06.004
158. Cloud cover detection combining high dynamic range sky images and ceilometer measurements R. Román et al. https://doi.org/10.1016/j.atmosres.2017.06.006
159. 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
160. Bi-model short-term solar irradiance prediction using support vector regressors H. Cheng et al. https://doi.org/10.1016/j.energy.2014.03.096
161. A Selection Criterion for the Optimal Resolution of Ground-Based Remote Sensing Cloud Images for Cloud Classification Y. Wang et al. https://doi.org/10.1109/TGRS.2018.2866206
162. Convolutional Neural Network for High-Resolution Cloud Motion Prediction from Hemispheric Sky Images C. Crisosto et al. https://doi.org/10.3390/en14030753
163. 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
164. Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics M. Krinitskiy et al. https://doi.org/10.3390/atmos9110426
165. Neural Networks and Support Vector Machine Algorithms for Automatic Cloud Classification of Whole-Sky Ground-Based Images A. Taravat et al. https://doi.org/10.1109/LGRS.2014.2356616
166. 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
167. Automatic Multiclass Classification of Unlabeled Ground-Based Sky Images for Minute-Scale PV Energy Yield Forecasting M. Kousounadis-Knousen et al. https://doi.org/10.1109/ACCESS.2025.3587059
168. Classification of Ground-Based Cloud Images by Contrastive Self-Supervised Learning Q. Lv et al. https://doi.org/10.3390/rs14225821
169. Benchmarking of six cloud segmentation algorithms for ground-based all-sky imagers M. Hasenbalg et al. https://doi.org/10.1016/j.solener.2020.02.042
170. MPCM-Net: A Multiscale Network That Integrates Partial Attention Convolution With Mamba for Ground-Based Cloud Image Segmentation P. Niu et al. https://doi.org/10.1109/TGRS.2026.3666092
171. A ground-based cloud image classification method for photovoltaic power prediction based on Convolutional Neural Networks and Vision Transformer C. Shi et al. https://doi.org/10.1016/j.engappai.2025.111582
172. Cloud radiative effect, cloud fraction and cloud type at two stations in Switzerland using hemispherical sky cameras C. Aebi et al. https://doi.org/10.5194/amt-10-4587-2017
173. Cloud Fractions Estimated from Shipboard Whole-Sky Camera and Ceilometer Observations between East Asia and Antarctica M. KUJI et al. https://doi.org/10.2151/jmsj.2018-025
174. Benchmarking of solar irradiance nowcast performance derived from all-sky imagers S. Logothetis et al. https://doi.org/10.1016/j.renene.2022.08.127
175. Daytime Cloud Detection Method Using the All-Sky Imager over PERMATApintar Observatory M. Azhar et al. https://doi.org/10.3390/universe7020041
176. Deep photovoltaic nowcasting J. Zhang et al. https://doi.org/10.1016/j.solener.2018.10.024
177. Monitoring Cloud Motion in Cyprus for Solar Irradiance Prediction R. Tapakis & A. Charalambides https://doi.org/10.1155/2013/320618
178. Rough-Set-Based Color Channel Selection S. Dev et al. https://doi.org/10.1109/LGRS.2016.2625303
179. Cloud Shape Classification System Based on Multi-Channel CNN and Improved FDM M. Zhao et al. https://doi.org/10.1109/ACCESS.2020.2978090
180. Deep Synthesis of Cloud Lighting P. Satilmis et al. https://doi.org/10.1109/MCG.2022.3172846
181. The Impact of Solar Angle and Cloud Shadows on 3D Reconstruction of Rolling Stock Cargo C. Ostrand et al. https://doi.org/10.1109/ACCESS.2025.3554409
182. Advancing semantic cloud segmentation in all-sky images: A semi-supervised learning approach with ceilometer-driven weak labels D. Magiera et al. https://doi.org/10.1016/j.solener.2025.113822
183. Correcting confounding canopy structure, biochemistry and soil background effects improves leaf area index estimates across diverse ecosystems from Sentinel-2 imagery L. Wan et al. https://doi.org/10.1016/j.rse.2024.114224
184. DeepCloud: Ground-Based Cloud Image Categorization Using Deep Convolutional Features L. Ye et al. https://doi.org/10.1109/TGRS.2017.2712809
185. Learning Discriminative Salient LBP for Cloud Classification in Wireless Sensor Networks S. Liu & Z. Zhang https://doi.org/10.1155/2015/327290
186. Determination of cloud transmittance for all sky imager based solar nowcasting B. Nouri et al. https://doi.org/10.1016/j.solener.2019.02.004
187. Ground‐based observations of clouds through both an automatic imager and human observation A. Silva & M. Souza‐Echer https://doi.org/10.1002/met.1542
188. Optimizing cloud motion estimation on the edge with phase correlation and optical flow B. Raut et al. https://doi.org/10.5194/amt-16-1195-2023
189. Improving the Heliosat-2 method for surface solar irradiation estimation under cloudy sky areas D. Mouhamet et al. https://doi.org/10.1016/j.solener.2018.05.032
190. A Novel Ground-Based Cloud Image Segmentation Method Based on a Multibranch Asymmetric Convolution Module and Attention Mechanism L. Zhang et al. https://doi.org/10.3390/rs14163970
191. Observations of Nighttime Clouds Over Chiba, Japan, Using Digital Cameras and Satellite Images N. Lagrosas et al. https://doi.org/10.1029/2021JD034772
192. Nighttime cloud cover from multi-site observations and its relationships with ground- and satellite-based measurements N. Lagrosas et al. https://doi.org/10.1016/j.atmosres.2026.108823
193. Cloud fraction retrieval and its variability during daytime from ground-based sky imagery over a tropical station in India A. Nikumbh et al. https://doi.org/10.1016/j.jastp.2019.05.002
194. Unsupervised Semantic-Based Aggregation of Deep Convolutional Features J. Xu et al. https://doi.org/10.1109/TIP.2018.2867104
195. Ground-Based Remote Sensing Cloud Image Segmentation Using Convolution-MLP Network S. Liu et al. https://doi.org/10.1109/JSTARS.2025.3593473
196. TransCloudSeg: Ground-Based Cloud Image Segmentation With Transformer S. Liu et al. https://doi.org/10.1109/JSTARS.2022.3194316
197. Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images L. Krauz et al. https://doi.org/10.3390/rs12111902
198. A Hybrid Ensemble Learning Model for Short-Term Solar Irradiance Forecasting Using Historical Observations and Sky Images Z. Wang et al. https://doi.org/10.1109/TIA.2022.3231842
199. Effect of Asynchronous Data Processing on Solar Irradiance and Clearness Index Estimation by Sky Imagery N. Herrera et al. https://doi.org/10.3103/S0003701X20060043
200. Color-Based Segmentation of Sky/Cloud Images From Ground-Based Cameras S. Dev et al. https://doi.org/10.1109/JSTARS.2016.2558474
201. Very short-term solar irradiance forecasting based on open-source low-cost sky imager and hybrid deep-learning techniques M. Ansong et al. https://doi.org/10.1016/j.solener.2025.113516
202. Cloud classification of ground-based infrared images combining manifold and texture features Q. Luo et al. https://doi.org/10.5194/amt-11-5351-2018
203. Sky Images for Short-Term Solar Irradiance Forecast: A Comparative Study of Linear Machine Learning Models E. Shirazi et al. https://doi.org/10.1109/JPHOTOV.2024.3398365
204. Geometric calibration of all-sky cameras using sun and moon positions: A comprehensive analysis N. Blum et al. https://doi.org/10.1016/j.solener.2025.113476
205. Short-term solar irradiance forecasting under data transmission constraints J. Hammond et al. https://doi.org/10.1016/j.renene.2024.121058
206. A Novel Robust Classification Method for Ground-Based Clouds A. Yu et al. https://doi.org/10.3390/atmos12080999
207. CAU-Net: An attention-based feature enhancement model for ground-based cloud image segmentation applicable to peri-solar regions J. Zhu et al. https://doi.org/10.1016/j.asr.2025.10.048
208. Rainfall Prediction Using Deep Learning Based on Cloud Classification on Coastal Cities B. Nugroho et al. https://doi.org/10.1088/1755-1315/1543/1/012038
209. Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information Z. Zhang et al. https://doi.org/10.3390/app8050748
210. mCLOUD: A Multiview Visual Feature Extraction Mechanism for Ground-Based Cloud Image Categorization Y. Xiao et al. https://doi.org/10.1175/JTECH-D-15-0015.1
211. An Efficient Solution for Semantic Segmentation of Three Ground‐based Cloud Datasets Q. Song et al. https://doi.org/10.1029/2019EA001040
212. A total sky cloud detection method using real clear sky background J. Yang et al. https://doi.org/10.5194/amt-9-587-2016
213. Comparison of Cloud Cover from All-Sky Imager and Meteorological Observer J. Huo & D. Lu https://doi.org/10.1175/JTECH-D-11-00006.1
214. Cross-Domain Ground-Based Cloud Classification Based on Transfer of Local Features and Discriminative Metric Learning Z. Zhang et al. https://doi.org/10.3390/rs10010008
215. Calibration of an all-sky camera for obtaining sky radiance at three wavelengths R. Román et al. https://doi.org/10.5194/amt-5-2013-2012
216. Forecasting of solar radiation in photovoltaic power station based on ground‐based cloud images and BP neural network K. Hu et al. https://doi.org/10.1049/gtd2.12309
217. An Efficient Cloud Classification Method Based on a Densely Connected Hybrid Convolutional Network for FY-4A B. Wang et al. https://doi.org/10.3390/rs15102673
218. Cloud detection and classification with the use of whole-sky ground-based images A. Kazantzidis et al. https://doi.org/10.1016/j.atmosres.2012.05.005
219. Texture Feature Extraction Method for Ground Nephogram Based on Hilbert Spectrum of Bidimensional Empirical Mode Decomposition X. Chen et al. https://doi.org/10.1175/JTECH-D-13-00238.1
220. Improving cloud type classification of ground-based images using region covariance descriptors Y. Tang et al. https://doi.org/10.5194/amt-14-737-2021
221. An automated cloud detection method based on the green channel of total-sky visible images J. Yang et al. https://doi.org/10.5194/amt-8-4671-2015
222. 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
223. A Novel Method for Ground-Based Cloud Image Classification Using Transformer X. Li et al. https://doi.org/10.3390/rs14163978
224. Cloud type classification using deep learning with cloud images M. Guzel et al. https://doi.org/10.7717/peerj-cs.1779
225. Cloud fraction determined by thermal infrared and visible all-sky cameras C. Aebi et al. https://doi.org/10.5194/amt-11-5549-2018
226. CloudRaednet: residual attention-based encoder–decoder network for ground-based cloud images segmentation in nychthemeron C. Shi et al. https://doi.org/10.1080/01431161.2022.2054298
227. Deep tensor fusion network for multimodal ground-based cloud classification in weather station networks M. Li et al. https://doi.org/10.1016/j.adhoc.2019.101991
228. A method for detailed, short-term energy yield forecasting of photovoltaic installations D. Anagnostos et al. https://doi.org/10.1016/j.renene.2018.06.058
229. 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
230. Improved RepVGG ground-based cloud image classification with attention convolution C. Shi et al. https://doi.org/10.5194/amt-17-979-2024
231. A Cloud Detection Algorithm with Reduction of Sunlight Interference in Ground-Based Sky Images X. Li et al. https://doi.org/10.3390/atmos10110640
232. Learning group patterns for ground-based cloud classification in wireless sensor networks S. Liu & Z. Zhang https://doi.org/10.1186/s13638-016-0564-x
233. Ground-Based Cloud-Type Recognition Using Manifold Kernel Sparse Coding and Dictionary Learning Q. Luo et al. https://doi.org/10.1155/2018/9684206
234. A method for cloud detection and opacity classification based on ground based sky imagery M. Ghonima et al. https://doi.org/10.5194/amt-5-2881-2012
235. Cloud Classification of Ground-Based Images Using Texture–Structure Features W. Zhuo et al. https://doi.org/10.1175/JTECH-D-13-00048.1
236. Development of a Machine Learning Forecast Model for Global Horizontal Irradiation Adapted to Tibet Based on Visible All-Sky Imaging L. Wu et al. https://doi.org/10.3390/rs15092340