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.