Articles | Volume 9, issue 8
https://doi.org/10.5194/amt-9-4151-2016
© 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-9-4151-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
30 Aug 2016
Research article |
| 30 Aug 2016
Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth
Felipe A. Mejia
CORRESPONDING AUTHOR
Center for Renewable Resources and Integration, Department of Mechanical and
Aerospace Engineering, University of California, San Diego 9500 Gilman Dr.,
La Jolla, CA 92093, USA
Ben Kurtz
Center for Renewable Resources and Integration, Department of Mechanical and
Aerospace Engineering, University of California, San Diego 9500 Gilman Dr.,
La Jolla, CA 92093, USA
Keenan Murray
Center for Renewable Resources and Integration, Department of Mechanical and
Aerospace Engineering, University of California, San Diego 9500 Gilman Dr.,
La Jolla, CA 92093, USA
Laura M. Hinkelman
Center for Renewable Resources and Integration, Department of Mechanical and
Aerospace Engineering, University of California, San Diego 9500 Gilman Dr.,
La Jolla, CA 92093, USA
Manajit Sengupta
Center for Renewable Resources and Integration, Department of Mechanical and
Aerospace Engineering, University of California, San Diego 9500 Gilman Dr.,
La Jolla, CA 92093, USA
Center for Renewable Resources and Integration, Department of Mechanical and
Aerospace Engineering, University of California, San Diego 9500 Gilman Dr.,
La Jolla, CA 92093, USA
Jan Kleissl
Center for Renewable Resources and Integration, Department of Mechanical and
Aerospace Engineering, University of California, San Diego 9500 Gilman Dr.,
La Jolla, CA 92093, USA
Viewed
Total article views: 3,573 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 30 Oct 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,968 | 1,456 | 149 | 3,573 | 161 | 155 |
- HTML: 1,968
- PDF: 1,456
- XML: 149
- Total: 3,573
- BibTeX: 161
- EndNote: 155
Total article views: 2,914 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 30 Aug 2016)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,674 | 1,111 | 129 | 2,914 | 142 | 134 |
- HTML: 1,674
- PDF: 1,111
- XML: 129
- Total: 2,914
- BibTeX: 142
- EndNote: 134
Total article views: 659 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 30 Oct 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 294 | 345 | 20 | 659 | 19 | 21 |
- HTML: 294
- PDF: 345
- XML: 20
- Total: 659
- BibTeX: 19
- EndNote: 21
Cited
16 citations as recorded by crossref.
- Determination of cloud transmittance for all sky imager based solar nowcasting B. Nouri et al. 10.1016/j.solener.2019.02.004
- A novel clear-sky index for color sky imagery used in short-term irradiance forecasting J. Manning & R. Baldick 10.1063/1.5131521
- Measurement of diffuse and plane of array irradiance by a combination of a pyranometer and an all-sky imager N. Blum et al. 10.1016/j.solener.2021.11.064
- Cloud tomography applied to sky images: A virtual testbed F. Mejia et al. 10.1016/j.solener.2018.10.023
- Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2 S. Skakun et al. 10.1016/j.rse.2022.112990
- A pixelwise approach to derive AOD and COD from all-sky imagery J. Gómez-Amo et al. 10.1088/1755-1315/1522/1/012014
- Detecting clear sky images P. Pawar et al. 10.1016/j.solener.2019.02.069
- 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. 10.3390/rs11171962
- CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2 C. Aybar et al. 10.1038/s41597-022-01878-2
- Real-Time Uncertainty Specification of All Sky Imager Derived Irradiance Nowcasts B. Nouri et al. 10.3390/rs11091059
- Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting G. Bassous et al. 10.3390/en14196075
- Segmentation and Classification of Individual Clouds in Images Captured with Horizon-Aimed Cameras for Nowcasting of Solar Irradiance Absorption B. Martins et al. 10.4236/ajcc.2023.124027
- High‐resolution photography of clouds from the surface: Retrieval of optical depth of thin clouds down to centimeter scales S. Schwartz et al. 10.1002/2016JD025384
- Feasibility of Ground-Based Sky-Camera HDR Imagery to Determine Solar Irradiance and Sky Radiance over Different Geometries and Sky Conditions P. Valdelomar et al. 10.3390/rs13245157
- An Intensive Campaign-Based Intercomparison of Cloud Optical Depth from Ground and Satellite Instruments under Overcast Conditions A. Damiani et al. 10.2151/sola.2019-036
- History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining D. Yang et al. 10.1016/j.solener.2017.11.023
15 citations as recorded by crossref.
- Determination of cloud transmittance for all sky imager based solar nowcasting B. Nouri et al. 10.1016/j.solener.2019.02.004
- A novel clear-sky index for color sky imagery used in short-term irradiance forecasting J. Manning & R. Baldick 10.1063/1.5131521
- Measurement of diffuse and plane of array irradiance by a combination of a pyranometer and an all-sky imager N. Blum et al. 10.1016/j.solener.2021.11.064
- Cloud tomography applied to sky images: A virtual testbed F. Mejia et al. 10.1016/j.solener.2018.10.023
- Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2 S. Skakun et al. 10.1016/j.rse.2022.112990
- A pixelwise approach to derive AOD and COD from all-sky imagery J. Gómez-Amo et al. 10.1088/1755-1315/1522/1/012014
- Detecting clear sky images P. Pawar et al. 10.1016/j.solener.2019.02.069
- 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. 10.3390/rs11171962
- CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2 C. Aybar et al. 10.1038/s41597-022-01878-2
- Real-Time Uncertainty Specification of All Sky Imager Derived Irradiance Nowcasts B. Nouri et al. 10.3390/rs11091059
- Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting G. Bassous et al. 10.3390/en14196075
- Segmentation and Classification of Individual Clouds in Images Captured with Horizon-Aimed Cameras for Nowcasting of Solar Irradiance Absorption B. Martins et al. 10.4236/ajcc.2023.124027
- High‐resolution photography of clouds from the surface: Retrieval of optical depth of thin clouds down to centimeter scales S. Schwartz et al. 10.1002/2016JD025384
- Feasibility of Ground-Based Sky-Camera HDR Imagery to Determine Solar Irradiance and Sky Radiance over Different Geometries and Sky Conditions P. Valdelomar et al. 10.3390/rs13245157
- An Intensive Campaign-Based Intercomparison of Cloud Optical Depth from Ground and Satellite Instruments under Overcast Conditions A. Damiani et al. 10.2151/sola.2019-036
Saved (preprint)
Latest update: 04 Nov 2025
Short summary
A method for retrieving cloud optical depth using a sky imager is presented. The method is applied to images taken at the Atmospheric Radiation Measurement site and validated against measurements from a microwave radiometer (MWR), output from the Min method for overcast skies, and τc retrieved by Beer's law from direct normal irradiance (DNI) measurements.
A method for retrieving cloud optical depth using a sky imager is presented. The method is...
