The all-sky cameras that form the network used in this work, are mainly installed in Valladolid (Spain), a city located in the north-central region of the Iberian Peninsula, with approximately 400 000 inhabitants including the metropolitan area , surrounded by rural area. The prevailing climate is continental, characterized by dry and hot summers and cold winters . Cloud conditions for Valladolid site show the lowest radiative impact during winter due to the lower solar zenith angle and the prevalence of thinner cloud layers like fog, compared to the thick convective clouds of spring .

The mentioned network is called PRESENTE as it was developed thanks to a project of the same name. It can be considered as a sub-network within GOA-SCAN (GOA all-Sky CAmeras Network), a bigger all-sky camera network managed by the Group of Atmospheric Optics from the University of Valladolid (GOA-UVa). The PRESENTE network is currently formed by 20 all-sky camera stations (as of February 2026), deployed on public buildings across the city (schools, high schools, university faculties, community centers, among others). The distribution of the stations tries to cover a wide variety of camera distances from more than 50 km to less than 200 m, in order to have different viewing angles of the clouds above them. Two extra cameras are deployed outside the city at 40 and 25 km, in order to assess the capability of the system to retrieve really high clouds (see locations in Fig.  and coordinates in Table ).

The all-sky camera model of the PRESENTE network is the OMEA-3C-TF a modified version of the OMEA-3C camera manufactured by Alcor Systems . This camera is formed by a SONY IMX178 CMOS sensor with a fisheye lens, both encapsulated in a weatherproof case with a BK7 glass dome on top, including a heating system to avoid water condensation . The sensor has three RGB filters (Red, Green and Blue) arranged in a RGGB Bayer pattern mosaic and a triband filter over the sensor (instead of an infrared cutting-off filter like in the OMEA-3C model) in order to reduce the spectral width of each color channel .

These cameras are operated through an external computer placed inside a weatherproof outdoor box. These computers run the GOA OMEA-Capture software , which handles camera configuration, image acquisition, downloading, processing, and data transfer. Following the GOA-SCAN protocols, the PRESENTE instruments are set up to capture a set of multi-exposure raw sky images every five minutes during the daytime and every two minutes at nighttime, which is considered when the solar zenith angle (SZA) is above 97°. For the OMEA-3C-TF model, exposure times are specifically adjusted. At night (SZA>97°), the camera uses, in addition to an ISO amplification set to a gain of 150, a multi-exposure sequence of 1, 5, 10, 20, and 30 s for moonlit nights (when the Moon is between the first and third quarters and the Moon zenith angle below 80°), and 3, 6, 12, 24, and 48 s under moonless conditions. During the day, the sequences depend on SZA: from 0 to 35°, the times are 214, 356, 640, 1208, and 2344 µs; from 35 to 75°, a 4616 µs exposure is added; and finally from 75 to 88°, a 9160 µs exposure is included . These different SZA intervals are set to best adjust to different illumination conditions, also distinguishing fast changes during twilight. To optimize storage without losing coverage of the full sky, the captured images are cropped to 2000×2000pixels and saved in raw format (14-bit stored in 16-bit format, without demosaicing or white-balance corrections). Each image sequence is stored in a single Hierarchical Data Format version 5 (HDF5) file that also contains the associated metadata. The software is designed to automatically send these files to the GOA-SCAN servers, where they are processed into 8-bits RGB High Dynamic Range (HDR) color images . These HDR images are then published on the GOA-UVa website, which provides continuous updates from each station in the network (https://goa.uva.es/proyecto-presente/, last access: 9 September 2026). In this work, all references in the methodology to all-sky images refer to HDR images.

Each GOA-SCAN camera is carefully leveled to face the zenith and geometrically calibrated. This calibration is done with the ORION software . This calibration involves identifying the positions of several stars in a set of cloud-free nighttime images, and relating their measured pixel coordinates to their known astronomical coordinates. Using this information, the software reconstructs the camera's pointing geometry by estimating the image center (sky zenith), the azimuthal offset with respect to image north, and the polynomial relationship between radial pixel distance and zenith angle. The resulting calibration matrices allow the precise mapping of any pixel to its corresponding viewing direction (zenith and azimuth coordinates). The GOA-SCAN cameras are subject to regular maintenance, which includes cleaning and inspection and a new geometric calibration is carried out whenever the camera is moved or its orientation shifts, even slightly .

In addition, a semantic segmentation model developed by GOA-UVa and described by has been used to provide a segmented sky mask for identifying cloudy pixels in each sky image. This model is named “GOA-UVa All-Sky Segmentation U-Net Model” and it is available in . The model segments into five semantic classes: cloud-free, cloud, thin cloud, sun or other. Validation results from showed that it achieves a recall of 87 % in the class non-thin clouds and 96 % in the class clear skies. This model was developed for daytime images, but is also used for nighttime images in this work.

Ceilometers are single-wavelength lidar-based instruments designed to continuously monitor the vertical structure of the lower atmosphere and widely used to determine the CBH. They operate by emitting short laser pulses upward and measuring the backscattered signal returned by aerosols, cloud droplets, and other atmospheric constituents . The CBH is generally estimated as the height for the local maxima in the backscatter signal or in its vertical gradient, allowing the detection of one or multiple cloud layers depending on atmospheric conditions . The ceilometer model used in this study is a Lufft CHM15k-Nimbus, which operates with a pulsed 1064 nm Nd:YAG laser and provides continuous vertical backscatter profiles at 15 m resolution up to 15 km above ground level (a.g.l.). Two different instruments are used, one installed on the same rooftop as the C013 camera, and the other one at CIBA location as the C031 camera (see Table ). Both are part of the ICENet network, and their data are calibrated and processed following ICENet operational protocols . These instruments are configured to provide a range-corrected signal every 15 s. They can provide CBH and cloud penetration depth (CPD) products at three different height levels with the same temporal resolution. From these parameters, the CTH can be estimated by adding the CPD to the CBH at each corresponding level. However, according to the manufacturer's specifications , the CPD represents the depth into the cloud at which the laser signal is fully attenuated. Therefore, CTH values calculated this way are only fully reliable if the ceilometer detects an additional cloud layer above the current one, which guarantees that the laser has completely penetrated the first layer. In many cases, especially with low and dense clouds, the signal may be extinguished before reaching the actual top or any higher layers. In such instances, the CPD does not represent the total cloud thickness, and the resulting CTH must be handled with caution as it likely underestimates the real cloud top.

The Sentinel-2 mission, part of the European Space Agency (ESA) Copernicus Programme, provides high-resolution optical imagery for monitoring land and coastal environments. It is composed of two identical satellites, Sentinel-2A and Sentinel-2B, which fly in the same sun-synchronous orbit separated by 180°. Together, they achieve a swath width of 290 km and a revisit frequency of approximately five days under identical viewing geometry; in higher latitudes or overlapping orbits, this frequency can increase .

Each satellite carries a MultiSpectral Instrument (MSI) that records reflected solar radiation in 13 spectral bands spanning from the visible and near-infrared (VNIR) to the shortwave infrared (SWIR) regions. The data are provided at three spatial resolutions: 10 m for four bands, 20 m for six bands, and 60 m for three bands. The 60 m bands are primarily designed for atmospheric correction and cloud detection operations . The Sentinel-2 true color product is generated by directly mapping the reflectances of bands B4 (red), B3 (green), and B2 (blue) into an RGB image .

The Scene Classification Layer (SCL) product, provides a per-pixel mask with 60 m resolution that efficiently detects clouds, snow, and cloud shadows, producing a classification map that includes four cloud-related classes (including cirrus) and six surface classes (shadows, vegetation, bare soil/desert, water, and snow). It operates on Level-1C Top-of-Atmosphere (TOA) reflectances at 60 m resolution and generates additional quality indicators expressing the probability of cloud or snow occurrence. To optimize performance for operational processing, the algorithm relies on a sequential filtering approach that combines spectral thresholds, band ratios, and normalized indices such as NDVI and NDSI to identify potential cloudy pixels. These pixels undergo successive spectral tests in which the cloud probability is iteratively updated, ranging from 0 (high-confidence cloudless sky) to 1 (high-confidence cloud). The sequence terminates when the probability drops to zero, classifying the pixel as cloud-free. This probabilistic framework allows the algorithm to be computationally efficient and robust under varying illumination and atmospheric conditions. A self-organizing neural network (SOM) is also used in later steps to refine cloud and shadow classification. This classification is introduced into Sen2Cor processing chain, which performs the atmospheric correction used to generate level-2A products . In this work, the Sentinel-2 true color and SCL products corresponding to tile 30TUM, defined within the Military Grid Reference System (MGRS) and covering the Valladolid region, have been downloaded from the Copernicus Data Space Ecosystem.

The procedure described in Sect.  is repeated pairing the reference camera with each of the others available in the network. Since the fields of view of multiple cameras may overlap depending on cloud heights, a filtering process is proposed to avoid redundant data and ensure computational efficiency. For each pair of all-sky cameras, the overlap area of their two vision cones (defined by θt=75°) is evaluated at a range of discrete height levels (0.5–15 kma.s.l., in 0.5 km increments). A camera pair is only considered valid if the overlapping area at a given height level represents between 20 % and 80 %. Specifically, at each height level, it is checked whether the overlapping area has already been covered by a previously processed pair. If the new pair does not increase the overlapping area by at least 5 % at any height level, it is excluded from the calculation.

Figure  illustrates the cloud points and their heights retrieved for the reference camera C015 at 11:20 UTC on 28 December 2025, when paired with all the different available cameras that fulfill the overlapping criteria. From Fig. , it can be observed in practically all camera pairs, that the clouds identified in the image are located at an altitude of approximately 7 kma.s.l., with some pixels showing slightly lower heights toward the North. However, certain discrepancies arise depending on the specific auxiliary camera used. These differences may be attributed to several factors, such as geometric calibration uncertainties or optical reflections within the camera domes. Despite these minor variations, the cameras providing the most reliable results are those separated from C015 by distances between approximately 5 and 10 km. In contrast, very few valid cloud height estimates are obtained from camera C031, which lies more than 30 km away from the reference C015 camera. There are also less valid cloud points for camera C026, which is located too close to the reference C015 camera, less than 2 km away. In both cases, the stereoscopic cloud height retrieval deteriorates: for C031, the cameras observe the cloud field from excessively different points, so their fields of view have little overlap, while for C026 the images are too similar for the given cloud height (around 7 kma.g.l.), displacement values are really small, limiting the accuracy of the cloud height retrieval.

In order to minimize the uncertainties in the derived cloud height values, the median value of the cloud height estimates from the different camera pairings is obtained for each pixel of the reference camera. The formed image with these median values is considered as the cloud height matrix for the reference camera. For the case analyzed in Fig. , this would be the median value of all the cloud height matrices shown from the pairing of each all-sky camera available with C015. This approach provides a robust estimate while reducing the influence of noisy or outlier results from individual camera pairs.

To these cloud height estimations obtained for a given reference camera (the mentioned median of all individual pairs results), an additional filtering procedure is applied to refine the results and remove spurious values. First, a median filter with a 3×3 window is applied, and the difference between the filtered and original matrices is calculated. Pixels that deviate by more than 1 km from the local median are discarded, in order to remove isolated outliers and warranty smoothness between adjacent pixels. Next, a layer filtering step is applied. A frequency histogram is computed from the remaining cloud height values, with 1 km bins between 0–15 km (a.g.l.). Then up to four height layers are selected from this distribution corresponding to the most populated bins, retaining only those containing at least 2 % of the valid pixels. The centers of these bins are interpreted as the dominant cloud layers. All pixels whose height differs by more than 0.7 km from the center of every selected layer are discarded, ensuring that only cloud heights consistent with the main layers in the scene are preserved.

The effect of these two filters is to discard outliers, suppress isolated noisy pixels, and smooth the retrieved cloud height distributions while preserving, if existing, the different cloud height layer structure. The result for the case in Fig. C shows that the filtered heights are spatially coherent and smooth, replicating the shape shown in the camera image and discarding spurious values produced by abnormal highlighted artifacts.

As explained previously, the image size is reduced to a 200×200 grid in order to optimize the computational time required for the correlation calculations between images. These reduced matrices are resized to the original 2000×2000 resolution using nearest-neighbor interpolation in order to include information for all cloudy pixels of the original image.

Once the cloud points and their heights have been determined for a given reference camera, the procedure is repeated using a different camera as the reference. In this sense, each camera in the system is successively designated as the reference camera, and the same stereoscopic reconstruction and filtering steps are applied. The process continues until every camera has been the reference, yielding a set of cloud fields estimations equal in number to the set of reference cameras.

Figure  illustrates the results obtained for each of the cameras in the analyzed case. Cameras C020 and C028 were not working due to malfunctions, C024 is excluded from the plot because it shows few representative cloud heights due to the large distance regarding the rest of the cameras, and C036 was not installed yet. Almost all of them successfully identify a fractured mid height cloud stratum. The retrieved cloud heights are homogeneous through the images, particularly in the central region of the sky image of each camera. There is a decrease in the resolution for the fragmented clouds near the horizon. This is a consequence of the projection geometry and the reduction in effective pixel resolution at high zenith angles, which limits the precision of the correlation process at the edges of the images. In the case of camera C031, the distance to the other cameras prevents it from seeing this range of clouds heights, since the overlapping field of view between cameras is not enough. Only a few pixels from the sky image are correlated with pixels in other cameras' images and maybe there are some wrong correlated pixels that yield on higher altitudes. In the Supplement, Fig. S1 shows another case of interest where it can be seen how the different camera distances in the network can facilitate the retrieval of different cloud layers within the same scene. This Fig. S1 reveals a high level cloud layer with lower level cumulus clouds beneath it. While both structures are not visible in all camera views, they are clearly distinguished in those where they both appear; this points out that the camera network's high density and spatial distribution provide multiple perspectives of the cloud field, enabling distinct cloud layers to be effectively differentiated.

For each reference camera, the final cloud point pixels are converted to georeferenced coordinates using the zenith, azimuth and cloud height information of each pixel. The chosen reference system is a local cartesian centered in the coordinates of the C013 camera (see Table ). To spatially aggregate the retrieved cloud points, a regular geographic grid is defined centered in the C013 camera coordinates. This grid covers a domain of ±0.5° in both longitude and latitude and is discretized in bins with a resolution of 0.0005°. This results in a two-dimensional array of bins, for which both the geographical limits and the central coordinates are explicitly determined. At these spatial scales, the geometric distortions associated with Earth curvature are negligible, this may become relevant when cameras are separated by several kilometers or when clouds reach high altitudes. Nevertheless, the results obtained from implementing this correction have proved to be quite equivalent, so the more simple calculation is implemented in the automation of cloud height retrieval to increase speed and reduce computational cost.

Each available cloud point from all reference cameras, its cloud height estimation and the ID of its corresponding reference camera, is then assigned to one of these grid bins. Points falling outside the defined grid are discarded. Thus, for each bin of the grid, there is a number of cloud points, from one or more reference cameras, which will exhibit a specific cloud height distribution for that bin. For each bin, a set of statistics is computed, including the number of different reference cameras providing cloud points to the bin, the maximum, minimum and the standard deviation of the cloud height distribution of the bin and the number of cloud points obtained within this bin. These statistical estimators allow a robust characterization of the variability within each grid bin while also providing a measure of the confidence offered by multiple camera perspectives.

Figure A shows the number of cameras that contribute with at least one cloud point to each grid bin. The highest overlap appears in the central region of the network, where multiple cameras have overlapping fields of view. In contrast, peripheral areas are only seen by a few cameras. Figure B displays the total number of individual cloud points assigned to each bin. Higher counts indicate regions where the stereoscopic retrieval is most reliable due to strong camera overlap. Lower counts highlight areas with limited observations or pixels removed by the quality filters. It is important to note that the number of data points per bin typically decreases as cloud altitude increases. This is because, for a given viewing angle, the physical area covered by each pixel expands with height, causing the available data to be distributed over a larger surface area. Based on these distributions and in order to ensure the reliability of the aggregated information, only grid bins meeting two criteria are retained: at least two distinct cameras must contribute to the bin, and there must be four or more individual cloud points inside it. These threshold values were manually selected based on an empirical review of the results. While they provide a robust filter for this study, they are not necessarily the most optimal in all scenarios, as the ideal thresholds likely depend on the density of the camera network and the altitude of the clouds being observed. This filtering guarantees that the resulting grid highlights only those regions supported by sufficient sampling, effectively reducing the impact of outliers. The outcome is a set of gridded cloud height maps that are spatially consistent and directly suitable for subsequent analysis and visualization.

Figure A shows the minimum height values retrieved, hereafter referred to as the CBH, for the analyzed case. In this example, the CBH remains relatively constant around 7 kma.s.l., although slightly lower values are observed in the central region of the map. Figure B presents the maximum cloud heights within each grid bin, hereafter denoted as the CTH. These values are generally centered around 8 kma.s.l., even though some isolated bins show abnormally high values that likely correspond to spurious data. Figure C illustrates the standard deviation of all height estimates within each grid bin. Higher standard deviation values indicate increased variability in the retrieved cloud point heights within a given bin. This variability is typically associated with multilayer cloud structures, although in some cases it may also reflect the presence of residual outliers (e.g. the aforementioned spurious CTH values). Notably, the standard deviation is lower in the central part of the grid. This may be attributed to a more precise characterization resulting from the higher number of cameras covering this area; however, it could also be related to the viewing geometry, since these clouds are located in the central part of the images, it is more challenging to distinguish their vertical edges and thickness compared to more slanted views. Together, these three cloud products provide a consistent description of both the vertical structure and the spatial variability of the reconstructed cloud field. Based on these three parameters, it can be concluded that the analyzed cloud layer appears highly homogeneous around 7 km and relatively thin, with differences between top and base heights approximately of 1 km, and with the standard deviation of the heights remaining low, roughly 0.2 km, except for some localized areas in the northern sector. Similarly, in the Supplement, Fig. S2 presents a more complex scenario where the methodology effectively distinguishes distinct cloud levels within the same field of view. In this case, the retrieved heights reveal cloud structures at altitudes between 10–12 kma.s.l., while other regions of the map show clouds at much lower levels, around 2 kma.s.l. This example confirms the system's ability to identify and separate distinct cloud layers.

To fully assess the accuracy of these results, they must be compared against independent measurements from other instruments. For this purpose, the following section presents a validation study using the CBH and CTH products derived from the methodology applied to the entire historical image record available, comparing them with data from independent ground based and satellite sensors.

Satellite imagery and ground-based all-sky cameras provide complementary perspectives of the cloud field. The proposed stereoscopic method provides cloud height estimates with high temporal resolution at the local/regional scale, while some polar satellite instruments, such as Sentinel-2, offer an overhead view with robust cloud detection but lower temporal resolution. By comparing both datasets, it has been possible to assess whether the cloud structures estimated from the camera network are consistent with the distribution of clouds observed from space.

For a qualitative comparison of the cloud maps obtained from the all-sky camera network with those derived from Sentinel-2, both datasets have been evaluated on a point to point basis. Sentinel-2 SCL (60 m) has been first filtered to obtain the different cloud classes (medium probability, high probability, and thin cirrus). The cloud mask retrieved from the all-sky has been obtained for a geographical grid as explained in Sect. . To ensure spatial consistency, this Sentinel-2 cloud mask has then been resampled to the camera network's geographical grid using the nearest neighbor interpolation method. This has allowed each grid point to be directly compared and classified as cloudy or cloud-free if there was an available cloud height value or not, respectively.

Figure  shows the mentioned Sentinel-2 SCL product at 60 m resolution and its comparison with the all-sky cameras cloud mask, for the particular case, which has been analyzed in the previous section (28 December 2025 at 11:20 UTC). A large fraction of the cameras' retrieved cloud field agrees with medium and high-probability cloud classes in the SCL layer (green pixels), confirming that the camera network captures the main cloud structures, which can also be distinguished in the satellite true-color image. The differences between both datasets have been found mainly at cloud borders. One possible explanation for this is the difference in contrast between cloud and cloud-free pixels and land and cloud pixels, which could make the Sentinel-2 algorithm discard more cloud values in the edges of the clouds where transparency is high. On the other hand, satellites have an almost zenithal point of view of the clouds, whereas cameras observe clouds from different angles. This implies that the level of detail captured depends on the cloud's position in the image; a cloud located near the zenith (the center of the image) is captured with much finer detail than when it is observed near the horizon. This leads to lower sensitivity for the reconstruction algorithm, especially for small, fragmented clouds. In addition, the width of fragmented clouds frequently prevents observing the cloud-free space between them, which can cause an overestimation of the cloud cover, especially between cloud edges. The cloud height is a key factor determining this sensitivity; higher structures are visible over a wider spatial extent and are therefore sampled by a larger number of cameras, leading to a more homogeneous and robust reconstruction of the cloud field, although the quality of the retrieved product also depends on the distance between the cameras. In contrast, mid and low clouds are observed over a more limited common field of view, so fewer cameras contribute to the same grid bins, making the reconstruction more sensitive to outliers. It can be seen in Fig. A, that pixels located at cloud borders coincide with grid bins where the number of contributing cameras is lower, even if the minimum threshold of more than two cameras contributing per bin is satisfied. Although these pixels pass the imposed filtering criterion, the reduced spatial overlap suggests increased uncertainty in the retrieved classification and height at these transition regions. Additionally, the satellite acquisition time does not exactly match the time of the camera images. Even short temporal offsets can result in small displacements of cloud structures, primarily affecting the cloud edges. Nevertheless, the blue regions indicate a robust agreement in cloud-free detection, confirming that the stereoscopic algorithm does not produce significant false positives in cloud-free zones and the discrepancies are found mostly at the transitions between the clouds. Furthermore, the Sentinel-2 SCL product, generated via the Sen2Cor algorithm, presents well-documented limitations. Recent evaluations demonstrate that Sen2Cor's cloud and shadow detection frequently misses thin clouds, partial clouds, and transparent cloud boundaries . Comprehensive accuracy assessments, including validations using the global CloudSEN12 dataset, show that Sen2Cor significantly underperforms in overall accuracy and F1-scores when compared to both alternative physical-rule-based algorithms (like Fmask or MAJA) and modern spatial-temporal machine learning models such as Cloud Score+ . Consequently, the discrepancies observed at the cloud edges may partly stem from the satellite mask's own misclassifications rather than solely from stereoscopic reconstruction uncertainties.

The field of view of the network can also be observed for the present cloud heights, as clouds above 41.9° N latitude are not visible. In the central part of the network, the higher camera density results in shorter camera distances, which ensures strong overlap and reliable detection of low to mid level clouds. Moreover, these central cameras can be combined with more distant peripheral stations, providing larger camera distances that improve the triangulation geometry for higher cloud layers within the same region of the map. In contrast, grid bins located near the outer boundary of the network do not benefit from this geometric complementarity. While clouds in these bins may be observed by nearby stations within the central part of the network, they lack additional outer stations that could provide overlapping views from the opposite side. Consequently, the stereoscopic configuration becomes geometrically constrained toward the perimeter, particularly affecting the reconstruction of lower cloud layers.

Overall, this simple qualitative comparison points out the robustness of the proposed method to detect cloud points with high spatial resolution, identifying the same cloud systems detected by Sentinel-2 at least in the case shown. Discrepancies are largely confined to cloud edges, where fewer cameras see the same cloud point, blind zones, and slight temporal offsets between datasets increase uncertainty.

For a more quantitative analysis of the retrieved CBH and CTH values with the cameras, the results have been compared with the independent measurements from the mentioned ceilometer installed in the Science Faculty of Valladolid, collocated with the C013 camera, in the center of the defined cloud grid. The cloud height maps derived from the all-sky camera network have been first projected onto a common geographic grid with a spatial resolution of 0.0005° in both latitude and longitude as explained in the previous section. Specifically, the CBH and CTH values that have been used in this comparison correspond to the grid bin closest to the ceilometer's location, which represents the central bin of the grid. To account for the different integration times when matching both datasets, all ceilometer observations within a ±30 s window have been considered, selecting the CBH or CTH value closest to the camera's estimate. Also, nocturnal camera timestamps have been shifted 45 s to better align them with the center of the exposure interval, that increases for low light conditions, ensuring a more accurate temporal match with the ceilometer data. The analysis covers the period from 27 July 2023 to 19 January 2026. During this time, 377 647 paired ceilometer and camera measurements have been taken, excluding those matches for which the ceilometer presented a sky condition index (SCI) indicating precipitation, fog, snow, reduced window transmission or malfunction.

Table  summarizes the performance of the camera network against the ceilometer for cloud detection. Of all cases where the cameras have identified clouds in the ceilometer's grid bin, they agree with the ceilometer in 95 % of times, indicating a very low false positive rate. In contrast, cameras have correctly identified 59 % of the clouds reported by the ceilometer. The remaining 41 % of clouds detected by the ceilometer but missed by the cameras are primarily due to the ceilometer's ability to operate in nearly all conditions. On the one hand, there are limitations related to very low clouds and overcast conditions, also cases not classified as fog by the ceilometer. While the ceilometer identifies the presence of a cloud layer at very low altitudes, the cameras often fail due to a lack of image texture and contrast, as the images are quite homogeneous and is difficult to find high RGB-correlations. Furthermore, at such low altitudes, there may be no overlap between the field of view of any cameras from the network. On the other hand, there are limitations related to the detection algorithm under specific lighting conditions, it should be noted that the segmentation algorithm used to discard outliers was not originally designed for nocturnal conditions, where it tends to misclassify clouds as cloud-free sky; this even happens sometimes for very dark clouds during daytime. Regarding the few cases (3 %) where the cameras have detected clouds but the ceilometer has reported cloud-free skies, these are likely due to external factors such as dust deposition on the camera, or dome reflections that may be misclassified as clouds. It is also important to note that since the methodology requires a minimum of two reference cameras viewing cloud points in the same bin and applies height constraints based on camera distances, some cloud cases detected by the ceilometer are filtered out for the cameras network by these quality criteria. This is particularly relevant during the periods when only two cameras were operational (almost two months at early stages of the network, see installation dates in Table ); causing many of these missed detections. Despite these limitations, the agreement in cloud-free scenarios has remained remarkably high at 97 %.

Figure  shows the outcome of extending this comparison to the cloud height values estimated by both instruments. The plots show a notable difference between CBH (panels A, B) and CTH (panels C, D) analysis. Results for CBH show a high correlation with a determination coefficient (R2) about 0.89. The majority of the data points are concentrated at low altitudes (<2 kma.g.l.) and align closely with the 1:1 line, confirming the strong agreement between both independent datasets. Some dispersed points are observed where the camera network underestimates the CBH compared to the ceilometer. The mean bias error (MBE) calculated as the mean of the differences (ΔCBH) between camera and ceilometer values, is remarkably low (-0.19 km), indicating high accuracy, as the camera network predicts the CBH at an altitude very similar to the ceilometer on average. This can also be seen in the histogram of CBH differences (Fig. B), which shows a Gaussian-like distribution centered at zero. Approximately 25 % of the differences fall within ±0.2 km, and about 35 % are within ±0.4 km. The Standard Deviation (SD) of the differences is 1 km, which establishes the precision of the method at approximately this value for the current network configuration. Furthermore, the Root Mean Square Error (RMSE) is 1.02 km, nearly identical to the SD because the MBE is close to zero. This value reflects the impact of the dispersed points observed in Fig. A, although most of the data remains closely aligned with the 1:1 line, as is indicated by the pixel density values.

Regarding the CTH, Fig. C shows that the camera network and ceilometer also correlate, though with a lower R2 value of 0.67. While many observations still fall close to the 1:1 line, there is significantly more dispersion compared to the CBH analysis. The cameras tend to yield higher CTH values than the ceilometer for low ceilometer CTH values. This discrepancy is likely due to two factors: first, the ceilometer signal often saturates in low, thick clouds, potentially underestimating the cloud width, and consequently the CTH, but also failing to detect higher layers and thus underestimating the true CTH. Second, the limitation of the cameras as the top part of the cloud is only visible from fewer viewing angles. This trend is reflected in the histogram of differences (Fig. D) by a prominent right-shifted tail of positive values. Despite this, the highest frequency of differences remains centered near 0 km. The mentioned overestimation in low cloud scenarios results in a higher MBE of 1.13 km. The precision of the CTH measurements degrades compared to the CBH as SD increases to 1.65 km. Consequently with these MBE and SD values, the RMSE rises to 2 km, reflecting the increased dispersion in the CTH compared to the CBH.

To ensure a more consistent comparison between the camera network and the ceilometer, a spatial averaging approach has been implemented. Initially, the cameras' cloud heights have been extracted from the single grid bin at the ceilometer's location. However, this isolated bin may not fully represent the ceilometer's measurement, which is integrated over a 15 s interval, whereas the camera acquisition is quasi-instantaneous during daytime and longer than the ceilometer at nighttime. As clouds move during these temporal offsets, a single spatial point can result in discrepancies. To account for this and achieve a more robust comparison, the grid bins within a 150 m radius of the ceilometer location (31 bins in total) have been considered to be a more representative area. The median and standard deviation of these bins have been calculated for each obtained CBH and CTH maps, separately. This median values provide a more realistic information of the cloud height retrievals from the cameras, but in order to ensure the reliability of this dataset, the following filtering criteria has been applied: for each defined grid bin, if there are less than 20 height values within the selected area (31 bins), those values and their median are discarded.

Using this new spatial representativeness criterion, the matching process with the ceilometer data has been repeated following the same procedure as the point to point analysis. In this approach, an observation is classified as cloud-free if no valid cloud height is retrieved among all the bins within the mentioned 150 m radius window. These results, presented in the first row of Table , show an overall agreement of 66 % predicting clouds, which represents an improvement over the point to point comparison. The case when cameras detected clouds not identified by the ceilometer remains the same at only 3 %.

Since the illumination conditions seem to affect noticeably the cameras measurements and the performance of the segmentation model, an analysis of the cloud detection depending on the SZA is presented in Table . For daylight conditions the system achieves its peak performance, with the highest cloud detection coincidence of 86 %. In this regime, the high contrast and clear image texture of the images allow for easy visualization and identification of clouds, with high RGB-correlations. The performance of the cloud segmentation algorithm in retaining cloudy pixels is also significant. On the other hand, a higher number of cases where the cameras predict clouds not identified by the ceilometer (8 %) has been found during daytime. This is likely due to the strong presence of the sun in these images, which produces significant saturation if not obstructed by clouds (one example of this can be seen in Fig. S1 Cam C017 in the Supplement). Furthermore, direct solar radiation makes reflections and dirt on the camera dome more apparent (see in the Supplement Figs. S3 and S4 for an example of a dirty dome case); these features can generate high but fictitious RGB-correlations, leading to artifacts and the retrieval of non real clouds in the final product. The performance of cloud detection for the cameras degrades during twilight, close to sunset and sunrise (SZA between 80 and 110°); while the agreement for cloud-free scenarios remains exceptionally high at 99 %, the cloud detection coincidence drops to its worst performance (55 %), with missed clouds increasing to 45 %. Under these conditions, where most of the solar radiation becomes diffuse, the cloud-free sky and the clouds are difficult to distinguish even visually. Consequently, the segmentation model is unable to correctly identify many clouds, increasing the underestimation of cloud presence by the camera method compared to the ceilometer. This highlights the challenges of quick light intensity transitions where the signal to noise ratio decreases, as the exposure times need to be increased. For the nighttime data, there is a 62 % agreement for cloud presence, while 38 % of clouds identified by the ceilometer are missed by the camera network. Interestingly, when filtering for night with moon presence (SZA 110–180° Moon night), the detection accuracy remains similar at 61 %, suggesting that moonlight does not fully compensate for the limitations of the segmentation algorithm and the lower contrast compared to daytime images. Moreover, nocturnal sky images exhibit greater variability between cameras than daytime images due to the influence of nearby artificial light sources; these spatial differences in illumination can also cause the proposed cloud identification algorithm to perform worse under these conditions. Notably, the rate of cases where cameras detect clouds not identified by the ceilometer is lower for twilight and nighttime conditions (1 %). This discrepancy supports the hypothesis that these infrequent false detections are primarily driven by dome reflections and dirt and dust deposition; such factors are significantly more impactful under direct solar illumination, whereas their influence is minimized during the night.

To study the performance of the proposed methodology to retrieve the CBH and CTH values, Figs.  and show the mentioned cloud height median values by the camera compared to the measured by the ceilometer for the different illumination regimes. Figure  shows the results for CBH, presented in columns A (bidimensional frequency histogram) and B (histogram of differences). Figure  shows the results for CTH, following the same structure for the whole dataset in columns A and B, and additionally including, in columns C and D, the subset of cases where the CPD is considered reliable – that is, when an additional cloud layer is detected above, as specified by the manufacturer (see Sect. ). This comparison complements the results of Table , showing not only whether clouds are detected, but also how precise the measurement of their altitude is. To ensure the reliability of the spatial averaging approach, an additional filtering criterion has been applied: any CTH or CBH median value with a standard deviation above 1 km within the <150 m area of the grid has been discarded. This ensures the homogeneity of the median value, as cases with multiple cloud layers at different altitudes within the same 150 m radius area could yield unrealistic median estimates for ceilometer comparisons.

The results show a significant improvement in correlation compared to the single pixel analysis; for the all conditions dataset, the determination coefficient increases to 0.93 for CBH and to 0.72 for CTH. Regarding the different illumination regimes, for CBH, the highest determination coefficient is found during daylight conditions (R2=0.94; Fig. A2), as expected. However, the CTH shows the highest correlation values during twilight (R2=0.77; Fig. A3) and moonlit nights (R2=0.77; Fig. A5), surpassing the daytime correlation (R2=0.68; Fig. A2). Although the camera network's detection sensitivity decreases at night and twilight (highest rates of missed detection respect to the ceilometer), it consistently measures the height of the clouds it successfully identifies. Consistent with the previous central pixel analysis, the data pairs for both CBH and CTH remain primarily concentrated around the 1:1 line, as evidenced by the high density of points in the panels A1–A5 in Fig.  and A1–A5 in Fig. . However, the CTH exhibits higher dispersion, particularly at lower cloud heights, likely for the same reasons discussed in the point to point analysis of Fig. : the ceilometer signal saturates in low, thick clouds, preventing it from detecting higher cloud layers. When restricting the comparison to cases where the CPD is considered reliable (Panels C and D in Fig. ), the overestimation of the CTH by the cameras is notably less pronounced. For the all conditions dataset, R2 increases from 0.72 to 0.80 and the MBE decreases from 1.06 to 0.52 km (Panel D1), while the RMSE decreases from 1.78 to 1.21 km. This improvement is consistent across illumination regimes. This suggests that the observed overestimation at lower levels, while statistically significant, might not be entirely an artifact of the camera system, but could also be due to the ceilometer's lack of sensitivity to higher cloud layers when obscured by optically thick low-level clouds. However, restricting the validation to the filtered subset alone would be overly strict, as it would discard a substantial number of cases in which the ceilometer signal reached a single cloud with no additional layer above it, that the ceilometer fully penetrates. For this reason, both the unfiltered and CPD filtered results are presented.

Regarding the accuracy for the entire dataset, the CBH shows a MBE value of -0.14 km, whereas the unfiltered CTH shows a MBE value of 1.06 km. For CBH distinguishing different illumination intervals, the highest accuracy occurs during daytime, with an MBE of -0.07 km. In contrast, nocturnal conditions show a slight underestimation of CBH, with the MBE reaching -0.19 and -0.20 km for total night and moonlit nights, respectively. In the case of CTH, the MBE has positive values across all intervals due to the overestimation observed for low clouds. This trend is most pronounced during daytime, where the MBE reaches 1.4 km. For twilight and nocturnal conditions, the overestimation is less severe, with MBE values remaining between 0.84–0.93 km. The histograms of the cameras and ceilometer differences (Panels B1–B5 in Fig.  and B1–B5 and D1–D5 in Fig. ) reflect a normal behavior in the differences distribution where the highest frequencies are centered near zero for both CBH and CTH values; however, the ΔCTH distributions shows a skewness behavior with a noticeable positive tail, which explains the obtained positive MBE values for the CTH.

Panels A1–A5 in Fig.  and A1–A5 in Fig.  show for both CBH and CTH a cluster of points where camera derived heights are significantly lower than those from the ceilometer. These represent specific cases associated with multilayer cloud scenarios, such as low cumulus clouds situated beneath a higher cirrus stratum. This discrepancy arises from the differing spatial and temporal resolutions of the instruments, while the ceilometer provides a 15 s highly precise point measurement, the camera network provides a quasi-instantaneous (in daytime) picture of the clouds but the spatial resolution is limited by the constrains of the algorithm and may overestimate the horizontal extent of the lower cumulus. Consequently, the cameras may perceive the lower clouds as a bigger or continuous layer that obstructs the view of the higher clouds identified by the ceilometer. An example for these discrepancies is presented in the Fig. S5, which shows a sequence of images with a sky predominantly covered by low level clouds, with an additional high level cloud layer above them. In this case, the ceilometer at 14:00 UTC reported a cloud height about 9.43 km, whereas the camera network indicates 1.81 kma.g.l., since low clouds were close to the zenith of the image. The higher MBE observed for CBH during nocturnal conditions could be explained by a similar artifact. At night, the cameras have longer exposure times, if clouds are moving fast, this could cause the clouds to appear larger in the image than their actual physical dimensions. Given that higher clouds are also difficult to see under low illumination conditions, this exacerbates the bias towards low altitudes. This is less pronounced in the filtered CTH (Fig. C1–C5).

The precision of the CBH and CTH products from the proposed methodology, represented by the SD, is generally better under the spatial averaging approach compared to the central pixel analysis. Regarding CBH, the SD for the complete dataset is 0.77 km (Fig. B1), whereas for unfiltered CTH, it is 1.44 km (Fig. B1) and it is reduced to 1.1 km for the filtered CTH (Fig. D1). For CBH, the lowest dispersion is found at night (SD=0.67 km), (Fig. B4), while the daytime shows a higher SD of 0.89 km (Fig. B2). These values result in an RMSE of 0.78 km for the entirety of the observations and 0.89 km for daylight conditions. As mentioned, the CTH exhibits greater dispersion due to the difficulty of capturing cloud tops. For the unfiltered CTH, the SD is higher during the day (1.78 km) compared to twilight (1.31 km) and nighttime (1.19 km) (Fig. B2–B4). Consequently, the RMSE for the unfiltered CTH reaches 1.78 km across all conditions and 2.27 km during the day. These metrics align with the higher variability observed in the CTH plots. This effect is less pronounced when filtering for those cases were it is clear that the ceilometer signal was not saturated and it penetrated an entire cloud. In these filtered cases, the SD decreases to 1.24 km during daytime, 1.01 km during twilight, and 1.07 km at night (Fig. D2–D4), further supporting that part of the residual CTH dispersion reflects ceilometer limitations.

This analysis proves that lighting conditions are a key factor for the cloud height retrievals of the all-sky camera network. A lack of light leads to low contrast images, variations between the images captured in different sites due to artificial light sources, and also cloud segmentation errors. Overall, the accuracy is robust, maintaining acceptable errors and proving the spatial averaging and the filtering criteria acceptable.

The obtained results indicate that the accuracy of cloud height retrievals, using the ceilometer as reference, varies significantly across different altitude layers, especially for CTH values. A statistical analysis of these discrepancies has been carried out using 1 km height ranges. The same filtering criteria is applied to the median cloud height value within a 150 m radius of the ceilometer location for all illumination scenarios. Figure  presents the distribution of the differences (ΔCBH and ΔCTH), for each 1 km bin of ceilometer height, showing the following statistical estimators in a box and whisker plot, presenting the interquartile range (IQR), median, mean, standard deviation and percentile 5th (P5) and 95th (P95). In addition, to complement this figure, Table  shows the metrics, including N (the number of coincident cloud height values), MBE, SD and RMSE, for both CBH and CTH in the dataset of all conditions.

Regarding ΔCBH, for the all conditions case, both the mean and median values remain remarkably stable and near zero for cloud layers below 10 km. As indicated in Table , the MBE varies, from 0.17 km (0–1 km range) to -0.57 km (9–10 km range). However, for altitudes exceeding 10 km, there is a more significant underestimation of the CBH, with the MBE reaching -2.34 km in the 11–12 km interval. Figure A1–A5 reveals that this high altitude underestimation is primarily driven by nocturnal conditions, particularly during moonless nights (Panel A4), as expected, since high clouds, such as cirrus, are notably difficult to identify due to their high transparency. However, the retrieval of lower cloud layers (below 4–5 km) remains consistent regardless of lunar illumination. This is likely due to the reflection of artificial light from the city on the cloud base, providing sufficient contrast for the cameras to detect these lower layers even on moonless nights. In contrast, daytime observations (Panel A2) do not exhibit such dependence on altitude, showing only a slight underestimation. The dispersion of ΔCBH generally increases with cloud height. This is evidenced by the increase of IQR, a decrease in the P5, and a monotonic increase in the SD from 0.33 km at 1–2 to 3.71 km at the 12–13 km range. Notably, the higher SD in the 0–1 km range (0.50 km) compared to the 1–2 km range (0.33 km) likely reflects the initial lack of nearby cameras by the ceilometer location, until the installation of C030, enabling the retrieval of lower clouds from this point. These results suggest that while CBH retrieval is highly accurate and precise under daylight, precision decreases with altitude during low light conditions, with accuracy significantly degrading above 10 km.

The analysis for CTH, shows that the median of ΔCTH remains close to zero for most height ranges, except for the lowest layers (up to 3 km). For higher cloud heights, the mean values deviate from zero as well at nighttime conditions. However, the data availability for this high altitude clouds is considerably lower (see N in Table ). In contrast, the results for low altitude clouds are highly representative, as they correspond to the height ranges with the largest number of observations. In these low layers, the cameras overestimate the ceilometer's CTH values, with MBE values ranging from 2.68 km (0–1 km range) to 0.83 km (2–3 km range). This overestimation is more pronounced during daytime conditions (Panel B2), which explains the poorer CTH agreement found for the daylight interval in Fig. . While the potential limitations causing this bias have been addressed previously, it is important to emphasize that the ceilometer's signal often fails to penetrate optically dense low clouds, preventing the detection of higher layers. This is likely to happen more during daylight hours, when solar background radiation increases the noise in the ceilometer signal and reduces its sensitivity to higher altitudes. Consequently, the cameras, which benefit from multiple viewing angles, may be obtaining in some cases true cloud tops that the ceilometer cannot resolve due to signal extinction. Regarding the precision of the CTH retrievals, although IQR exhibits larger values for clouds below 3 km, the SD remains relatively stable for mid level clouds (3–6 km), showing values around 1 km. The P5 values remain closer to zero for CTH than for CBH across most intervals, revealing a lower presence of extreme outliers in the cloud top estimates, except during moonless nights. These results indicate that the camera network is capable of retrieving the CTH with high accuracy and consistency for altitudes above 3 km.

In order to complement the statistical analysis, a direct comparison of two distinct days with varying cloud layer configurations is presented in Fig. . It illustrates the temporal evolution of the RCS (panels A and C) obtained by the ceilometer, and the estimated CBH and CTH from the camera network and the ceilometer (panels B and D). Consistent with the previous analysis, camera values are derived from the median height within a 150 m radius of the ceilometer's location.

For the case of 26 January 2025, the RCS (panel A) shows the evolution of a day where a high to mid level cloud layer during the night gradually descended in altitude. At 07:00 UTC, another mid to high cloud is observed, and until 16:00 UTC, the RCS indicates the likely presence of a very low and thick cloud layer, as the ceilometer signal became saturated. Between 14:00–16:00 UTC, precipitation was likely occurring. In the afternoon, again a thick low level cloud layer appeared to saturate the RCS and at night, around 22:00–23:00 UTC, a higher cloud descended with precipitation. Regarding the CBH and CTH values in Panel B, clouds were detected by the ceilometer around 00:00 UTC about 10 km, that the cameras do not detect; as explained previously, this is likely because thin and translucent high clouds are extremely difficult to identify at night. During the rest of the night, both the CBH and CTH from the cameras show good agreement with the ceilometer. In the twilight intervals (around 07:00 and 18:00 UTC), the cameras show a lack of sensitivity, evidenced by two gaps in the cameras retrieved CBH and CTH. During daylight hours, the discrepancy in the cameras' CTH is likely partly due to the ceilometer signal saturating, whereas the cameras provide more information by viewing from different angles. Nevertheless, these values are also noisy. Also, during intervals with rain, droplet deposition on the domes, may explain the presence of high CTH outliers.

In the case of 18 May 2025, the RCS (Panel C) shows clouds around 2.5 km during the first hours of the night, followed by a cloud-free period. Around 05:00 UTC, higher clouds are seen at approximately 7.5 km. During the day, there was a more complex situation with a low layer at around 2.5 km that was probably not very opaque, allowing another layer to be detected between 7.5–10 km. From 18:00 UTC onward, only the lower layer remained. Regarding the cloud heights in Panel D, both instruments agree throughout the day, showing the capability of the cameras network to detect different cloud layers.

Finally, to evaluate the broader capabilities of the proposed method, based on the cameras network, an additional comparison has been performed using a second ceilometer situated at the Research Center for the Lower Atmosphere (CIBA), 35 km away from the primary site. This location also hosts an all-sky camera (C031, see Table ). It is important to note that, due to the geometric limitations of the cameras' fields of view caused by the geographical camera distribution, and the criteria imposed in the proposed methodology, only high altitude clouds can be detected in this location. Figure  shows the camera CBH and CTH values against the ceilometer ones at CIBA station. This figure (panels A and B) clearly illustrates the mentioned limitation to high altitude layers, as no cloud retrievals are found below 7 km. Given this specific limitation, a categorical evaluation of cloud detection is not representative for this station. Camera values agree with the ceilometer cloud heights for both CBH and CTH values even for the low number of observations (CIBA's ceilometer was installed on 30 October 2025). Outliers where the cameras report high altitude clouds while the ceilometer identifies clouds at significantly lower levels (below 2 km) can be attributed to the presence of low clouds at CIBA that attenuate the ceilometer signal, preventing the detection of higher layers, as discussed previously. These outliers impact the accuracy and precision metrics of the dataset. The MBE is shifted toward overestimation, reaching 0.99 km for CBH and 1.19 km for CTH. Similarly, the precision is affected, with SD values of 2.40 and 2.65 km for CBH and CTH, respectively. Despite the presence of these outliers, it can be observed that the most frequent differences between the cameras and the ceilometer are centered around zero for both CBH and CTH. Nevertheless, if these specific outliers are discarded, the remaining data points align closely with the 1:1 line, demonstrating that the camera network is capable of providing reliable cloud height estimates even at significant distances from the network center.