Articles | Volume 11, issue 10
https://doi.org/10.5194/amt-11-5549-2018
© Author(s) 2018. 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-11-5549-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Oct 2018
Research article |
| 12 Oct 2018
| 12 Oct 2018
Cloud fraction determined by thermal infrared and visible all-sky cameras
Physikalisch-Meteorologisches Observatorium Davos, World Radiation Center, Davos, Switzerland
Oeschger Center for Climate Change Research and Institute of Applied Physics, University of Bern, Bern, Switzerland
Julian Gröbner
Physikalisch-Meteorologisches Observatorium Davos, World Radiation Center, Davos, Switzerland
Niklaus Kämpfer
Oeschger Center for Climate Change Research and Institute of Applied Physics, University of Bern, Bern, Switzerland
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Cited
18 citations as recorded by crossref.
- Estimation of cloud optical thickness, single scattering albedo and effective droplet radius using a shortwave radiative closure study in Payerne C. Aebi et al.
- Trends in surface radiation and cloud radiative effect at four Swiss sites for the 1996–2015 period S. Nyeki et al.
- ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data F. Vogt et al.
- Day and Night Clouds Detection Using a Thermal-Infrared All-Sky-View Camera Y. Wang et al.
- Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks C. Shi et al.
- Enhancing photovoltaic plant efficiency through a deep framework for cloud detection, shading risk assessment, and cloud trend analysis M. Khala & O. Eloutassi
- Comparison of Cloud Amounts Retrieved with Three Automatic Methods and Visual Observations M. Utrillas et al.
- CloudU-Net: A Deep Convolutional Neural Network Architecture for Daytime and Nighttime Cloud Images’ Segmentation C. Shi et al.
- Solar radiation budget in the atmosphere under broken cloudy sky. An analytical model R. Rosa & A. Silva
- Observations of Nighttime Clouds Over Chiba, Japan, Using Digital Cameras and Satellite Images N. Lagrosas et al.
- A Novel Ground-Based Cloud Image Segmentation Method Based on a Multibranch Asymmetric Convolution Module and Attention Mechanism L. Zhang et al.
- Infrared radiometric image classification and segmentation of cloud structures using a deep-learning framework from ground-based infrared thermal camera observations K. Sommer et al.
- Solar irradiance components estimation based on a low-cost sky-imager C. Sánchez-Segura et al.
- Angular Calibration of Visible and Infrared Binocular All-Sky-View Cameras Using Sun Positions W. Xie et al.
- Intrahour solar radiation forecasting based on sun visibility for different cloud types X. Hou et al.
- Towards an Algorithm for Near Real Time Profiling of Aerosol Species, Trace Gases, and Clouds Based on the Synergy of Remote Sensing Instruments N. Siomos et al.
- Extreme wildfires over northern Greece during summer 2023 – Part A: Effects on aerosol optical properties and solar UV radiation K. Michailidis et al.
- Diurnal (24 h) cycle and seasonal variability of cloud fraction retrieved from a Whole Sky Imager over a complex terrain in the Western Ghats and comparison with MODIS S. Sunil et al.
18 citations as recorded by crossref.
- Estimation of cloud optical thickness, single scattering albedo and effective droplet radius using a shortwave radiative closure study in Payerne C. Aebi et al.
- Trends in surface radiation and cloud radiative effect at four Swiss sites for the 1996–2015 period S. Nyeki et al.
- ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data F. Vogt et al.
- Day and Night Clouds Detection Using a Thermal-Infrared All-Sky-View Camera Y. Wang et al.
- Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks C. Shi et al.
- Enhancing photovoltaic plant efficiency through a deep framework for cloud detection, shading risk assessment, and cloud trend analysis M. Khala & O. Eloutassi
- Comparison of Cloud Amounts Retrieved with Three Automatic Methods and Visual Observations M. Utrillas et al.
- CloudU-Net: A Deep Convolutional Neural Network Architecture for Daytime and Nighttime Cloud Images’ Segmentation C. Shi et al.
- Solar radiation budget in the atmosphere under broken cloudy sky. An analytical model R. Rosa & A. Silva
- Observations of Nighttime Clouds Over Chiba, Japan, Using Digital Cameras and Satellite Images N. Lagrosas et al.
- A Novel Ground-Based Cloud Image Segmentation Method Based on a Multibranch Asymmetric Convolution Module and Attention Mechanism L. Zhang et al.
- Infrared radiometric image classification and segmentation of cloud structures using a deep-learning framework from ground-based infrared thermal camera observations K. Sommer et al.
- Solar irradiance components estimation based on a low-cost sky-imager C. Sánchez-Segura et al.
- Angular Calibration of Visible and Infrared Binocular All-Sky-View Cameras Using Sun Positions W. Xie et al.
- Intrahour solar radiation forecasting based on sun visibility for different cloud types X. Hou et al.
- Towards an Algorithm for Near Real Time Profiling of Aerosol Species, Trace Gases, and Clouds Based on the Synergy of Remote Sensing Instruments N. Siomos et al.
- Extreme wildfires over northern Greece during summer 2023 – Part A: Effects on aerosol optical properties and solar UV radiation K. Michailidis et al.
- Diurnal (24 h) cycle and seasonal variability of cloud fraction retrieved from a Whole Sky Imager over a complex terrain in the Western Ghats and comparison with MODIS S. Sunil et al.
Saved (final revised paper)
Latest update: 18 May 2026
Short summary
A newly developed hemispherical thermal infrared cloud camera (IRCCAM) is presented. The IRCCAM allows automatic cloud detection during the day and at night-time. The cloud fraction determined from the IRCCAM is compared with the cloud fraction determined from other instruments over a time period of 2 years. The IRCCAM has an agreement of +/- 2 oktas cloud fraction in 90 % of the data compared to other instruments. There are no significant differences between seasons or different times of day.
A newly developed hemispherical thermal infrared cloud camera (IRCCAM) is presented. The IRCCAM...
