Solar irradiance nowcasts can be derived with sky images from all sky imagers (ASI) by detecting and analyzing transient clouds, which are the main contributor of intra-hour solar irradiance variability. The accuracy of ASI based solar irradiance nowcasting systems depends on various processing steps. Two vital steps are the cloud height detection and cloud tracking. This task is challenging, due to the atmospheric conditions that are often complex, including various cloud layers moving in different directions simultaneously. This challenge is addressed by detecting and tracking individual clouds. For this, we developed two distinct ASI nowcasting approaches with four or two cameras and a third hybridized approach. These three systems create individual 3-D cloud models with unique attributes 2 including height, position, size, optical properties and motion. This enables us to describe complex multi-layer conditions. In this paper, derived cloud height and motion vectors are compared with a reference ceilometer (height) and shadow camera system (motion) over a 30 day validation period. The validation data set includes a wide range of cloud heights, cloud motion patterns and atmospheric conditions. Furthermore, limitations of ASI based nowcasting systems due to image resolution and image perspective constrains are discussed. The most promising system is found to be the hybridized approach. This approach uses four ASIs and a voxel carving based cloud modeling combined with a cloud segmentation independent stereoscopic cloud height and tracking detection. We observed for this approach an overall mean absolute error of 648 m for the height, 1.3 m/s for the cloud speed and 16.2° for the motion direction.

Abstract. The interest in the use of ceilometers for optical aerosol characterization has increased in the last few years. They operate continuously almost unattended and are also much less expensive than lidars; hence, they can be distributed in dense networks over large areas. However, due to the low signal-to-noise ratio it is not always possible to obtain particle backscatter coefficient profiles, and the vast number of data generated require an automated and unsupervised method that ensures the quality of the profiles inversions.In this work we describe a method that uses aerosol optical depth (AOD) measurements from the AERONET network that it is applied for the calibration and automated quality assurance of inversion of ceilometer profiles. The method is compared with independent inversions obtained by colocated multiwavelength lidar measurements. A difference smaller than 15 % in backscatter is found between both instruments. This method is continuously and automatically applied to the Iberian Ceilometer Network (ICENET) and a case example during an unusually intense dust outbreak affecting the Iberian Peninsula between 20 and 24 February 2016 is shown. Results reveal that it is possible to obtain quantitative optical aerosol properties (particle backscatter coefficient) and discriminate the quality of these retrievals with ceilometers over large areas. This information has a great potential for alert systems and model assimilation and evaluation.

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The demand for accurate solar irradiance nowcast increases together with the rapidly growing share of solar energy within our electricity grids. Intra-hour variabilities, mainly caused by clouds, have a significant impact on solar power plant dispatch and thus on electricity grids. All sky imager (ASI) based nowcasting systems, with a high temporal and spatial resolution, can overall mean-absolute deviation (MAD) and root-mean-square deviation (RMSD) are 0.11 and 0.16 respectively for transmittance. The deviations are significantly lower for optically thick or thin clouds and larger for clouds with moderate transmittance between 0.18 and 0.585. Furthermore we validated the overall DNI forecast quality of the entire nowcasting system, using this transmittance estimation method, over the same data set with three spatially distributed pyrheliometers. Overall deviations of 13% and 21% are reached for the relative MAD and RMSD with a lead time of 10 minutes. The effects of the chosen data set on the validation results are demonstrated by means of the skill score.

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