Articles | Volume 14, issue 7
https://doi.org/10.5194/amt-14-5199-2021
© Author(s) 2021. 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-14-5199-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Jul 2021
Research article |
| 30 Jul 2021
| 30 Jul 2021
Cloud height measurement by a network of all-sky imagers
Institut für Solarforschung, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Paseo de Almería, 73, 2, 04001 Almeria, Spain
Institut für Vernetzte Energiesysteme, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Bijan Nouri
Institut für Solarforschung, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Paseo de Almería, 73, 2, 04001 Almeria, Spain
Stefan Wilbert
Institut für Solarforschung, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Paseo de Almería, 73, 2, 04001 Almeria, Spain
Thomas Schmidt
Institut für Vernetzte Energiesysteme, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Ontje Lünsdorf
Institut für Vernetzte Energiesysteme, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Jonas Stührenberg
Institut für Vernetzte Energiesysteme, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Detlev Heinemann
Institut für Vernetzte Energiesysteme, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Andreas Kazantzidis
Laboratory of Atmospheric Physics, Department of Physics, University of Patras, 26500 Patras, Greece
Robert Pitz-Paal
Institut für Solarforschung, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Linder Höhe, 51147 Cologne, Germany
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Cited
12 citations as recorded by crossref.
- Optimizing spatiotemporal prediction accuracy of regional solar irradiance through multi-angle cloud layer 3D reconstruction W. Ma et al. 10.1016/j.enconman.2025.119814
- Benchmarking of solar irradiance nowcast performance derived from all-sky imagers S. Logothetis et al. 10.1016/j.renene.2022.08.127
- TransCloudSeg: Ground-Based Cloud Image Segmentation With Transformer S. Liu et al. 10.1109/JSTARS.2022.3194316
- Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey Y. Nie et al. 10.1016/j.rser.2023.113977
- Analyzing Spatial Variations of Cloud Attenuation by a Network of All-Sky Imagers N. Blum et al. 10.3390/rs14225685
- Cloud height and thickness measurement based on a superconducting nanowire single-photon detector T. Gao & J. Feng 10.1364/JOSAA.479717
- A Hybrid Solar Irradiance Nowcasting Approach: Combining All Sky Imager Systems and Persistence Irradiance Models for Increased Accuracy B. Nouri et al. 10.1002/solr.202100442
- Integration Transformer for Ground-Based Cloud Image Segmentation S. Liu et al. 10.1109/TGRS.2023.3265384
- Characteristics of cloud base height distribution over a tropical urban region Hyderabad, India H. Vanlalrochana et al. 10.1016/j.atmosres.2025.108476
- Advances in solar forecasting: Computer vision with deep learning Q. Paletta et al. 10.1016/j.adapen.2023.100150
- SkyGPT: Probabilistic ultra-short-term solar forecasting using synthetic sky images from physics-constrained VideoGPT Y. Nie et al. 10.1016/j.adapen.2024.100172
- Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data P. Gregor et al. 10.5194/amt-16-3257-2023
12 citations as recorded by crossref.
- Optimizing spatiotemporal prediction accuracy of regional solar irradiance through multi-angle cloud layer 3D reconstruction W. Ma et al. 10.1016/j.enconman.2025.119814
- Benchmarking of solar irradiance nowcast performance derived from all-sky imagers S. Logothetis et al. 10.1016/j.renene.2022.08.127
- TransCloudSeg: Ground-Based Cloud Image Segmentation With Transformer S. Liu et al. 10.1109/JSTARS.2022.3194316
- Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey Y. Nie et al. 10.1016/j.rser.2023.113977
- Analyzing Spatial Variations of Cloud Attenuation by a Network of All-Sky Imagers N. Blum et al. 10.3390/rs14225685
- Cloud height and thickness measurement based on a superconducting nanowire single-photon detector T. Gao & J. Feng 10.1364/JOSAA.479717
- A Hybrid Solar Irradiance Nowcasting Approach: Combining All Sky Imager Systems and Persistence Irradiance Models for Increased Accuracy B. Nouri et al. 10.1002/solr.202100442
- Integration Transformer for Ground-Based Cloud Image Segmentation S. Liu et al. 10.1109/TGRS.2023.3265384
- Characteristics of cloud base height distribution over a tropical urban region Hyderabad, India H. Vanlalrochana et al. 10.1016/j.atmosres.2025.108476
- Advances in solar forecasting: Computer vision with deep learning Q. Paletta et al. 10.1016/j.adapen.2023.100150
- SkyGPT: Probabilistic ultra-short-term solar forecasting using synthetic sky images from physics-constrained VideoGPT Y. Nie et al. 10.1016/j.adapen.2024.100172
- Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data P. Gregor et al. 10.5194/amt-16-3257-2023
Latest update: 06 Nov 2025
Short summary
Cloud base height (CBH) is important, e.g., to forecast solar irradiance and, with it, photovoltaic production. All-sky imagers (ASIs), cameras monitoring the sky above their point of installation, can provide such forecasts and also measure CBH. We present a network of ASIs to measure CBH. The network provides numerous readings of CBH simultaneously. We combine these with a statistical procedure. Validation attests to significantly higher accuracy of the combination compared to two ASIs alone.
Cloud base height (CBH) is important, e.g., to forecast solar irradiance and, with it,...
