the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 May 2026
Cloud height mapping using multi simultaneous sky images from an all-sky camera network
Abstract. This study presents a refined, automated in near-real time methodology to obtain cloud height maps using a network of 20 all-sky cameras. This low cost and easily manipulated instruments are distributed in the city of Valladolid, Spain, trying to cover uniformly an estimated area up to 200 km2, with the actual spatial coverage depending on cloud height. Camera distances vary from 16 km to less than 1 km, in order to have different viewing angles of the clouds above them. All this cameras are geometrically calibrated and maintained within GOA-SCAN (Group of Atmospheric Optics all-Sky CAmeras Network). The methodology utilizes a stereoscopic approach, pairing simultaneous images from all the cameras. Correlation between the image pixels is computed in order to find the same sky point in both images. Then knowing the baseline distance and the orientation of the cameras, the height of every pixel identified as cloudy is computed. This is repeated for all the camera pairs that have a significant overlapping field of view. Filtering criteria are applied to retain only the significant values. With this methodology, cloud base and top heights (CBH and CTH) maps with a 50 m spatial resolution are obtained every five minutes during daytime and every two minutes for nighttime. These maps are compared with the scene classification product from Sentinel-2 satellite images, finding significant agreement. Some discrepancies were found for high transparent clouds and cloud edges. In addition, all CBH and CTH data available during two and a half years are compared against the independent values measured by a ceilometer collocated with one of the cameras. The determination coefficient for the median CBH values within a circular 150 m distance is 0.93. The obtained CTH values tend to overestimate the ceilometer ones and have wider dispersion, with lower determination coefficient of 0.72. Illuminating conditions are crucial for the correct segmentation of cloudy pixels, limiting the performance of the algorithm at nighttime. Overall, the proposed methodology is promising for obtaining cloud spatial masks and cloud height maps, over different cloud types, layering and conditions.
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Status: open (until 15 Jul 2026)
- RC1: 'Comment on egusphere-2026-2694', Anonymous Referee #1, 26 Jun 2026 reply
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Review of the Manuscript
Title: Cloud height mapping using multi simultaneous sky images from an all-sky camera network
General comments
This manuscript presents a comprehensive and well developed methodology for retrieving cloud base height (CBH) and cloud top height (CTH) using a network of 20 all-sky cameras deployed around Valladolid (Spain). The approach combines stereoscopic reconstruction, advanced filtering, and multi-camera aggregation to produce near-real-time cloud height maps with spatial resolution of 50 m.
The study is clearly motivated and addresses a relevant challenge in atmospheric remote sensing, as it fille the the gap between high-resolution but local ground-based instruments (e.g., ceilometers) and spatially extensive but temporally limited satellite measurements.
Validation against Sentinel-2 cloud masks and ceilometer observations spanning ~2.5 years strengthens the proposed method. The authors report good CBH performance, with R² ≈ 0.93 with low bias, demonstrating significant potential for operational applications such as solar nowcasting and cloud monitoring.
Overall, the manuscript represents a valuable contribution to atmospheric measurement techniques, but several aspects require clarification, and detailed discussion before publication.
1. The main novelty of this work is to scale the existing stereographyc approach using all-sky cameras to a large network in automate and real time processing. However, more explicite explanation of this novelty to differenttiate respect to the key prior works would be desired. In particular, the authors should try to clarify wether the main innovations lies in network scale, agregation strategy or near real time implementations with improved accuracy.
2. The methodology is well detailed but frequently it is difficult to follow. Please improve the detail in the next aspects:
- The image rectifications and projection stage (Section 3.1.1) would be benefit of a clearrer mahtematical formulation, maye including an schematic diagram summarizing transformations and coordinate system.
- The derivation of the uncertainties propagation of the stereoscopic height equation (eq. 1) would be intereting, instead of the reported ±1 pixel shifts.
- How the thresholds criteria have been choosen? Please justify them quantitatively even from the empirical point of view. Are the ersults sensitive to this thresholds?
3. The methodology strongly depends on a supervised segmentation model (U-Net), which is just trained for daytime conditions. Since nighttime and twilight performance are cleary degraded with respect to daytime performance, please discuss the potential bias induced by segmentation uncertainties on CBH and CTH retrieval. Could be the proposed method work without this segmentation process?
4. The validation for CTH and CBH against ceilometers and Satelite could be problematic, especially in case of thick clouds. Can you comment on that?
5. The authors identify some performance limitations (e.g., low clouds, high clouds at night, geometry constraints) but, did you quantify them more systematically? Are they in relation with uncertainties of the method or realted to previosu uncertainties (geometric calibration of the all-sky imager, segmentation, etc)
Minor comments:
The manuscript is generally well written, but some sentences are too long and could be simplified.
Please correct minor grammatical persintent issues (e.g., change “All this cameras” by “All these cameras”).