Cloud height and tracking accuracy of three all sky imager systems for individual clouds

Abstract

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.

Real-Time Uncertainty Specification of All Sky Imager Derived Irradiance Nowcasts

“…The used setup with an ASI distance of roughly 494 m and orthogonal image resolution of 1MP (maximum zenith angle of 78°) is less suitable for cloud heights above 5000 m [39]. These results are in alignment with actual cloud height and tracking validation results of the used nowcasting system [26]. The overall results of the validation are presented in Table 2, discretized over cloud height and cloud speed ranges.…”

“…The main source for uncertain cloud shadow edge positions are the cloud height detection and cloud tracking. The expected width of the uncertain transient zones is estimated using previously obtained validation results of the cloud height and cloud tracking [26]. We present a methodology to identify the most accurate and less accurate zones of the DNI maps, using morphological filters and partial 2-D interpolations.…”

“…The classification procedure described in [25] uses 13 variability indices, which partially have been introduced in previous publications (see Table 1). The nowcasting system uses a four dimensional clear sky library for the cloud segmentation [22,28] and a stereoscopic cloud geolocation and tracking approach [26]. The radiative effect of the detected clouds is analyzed by a probabilistic approach using current and historical cloud transmittance and cloud height measurements [24].…”

“…Table 2. Validation results of ASI-based cloud height and cloud tracking approach over cloud height and cloud speed ranges for the selected multi-ASI configuration [26]. For the nowcasting system, the main source of the false shadow positions are the cloud height detection and cloud tracking.…”

“…However, the exact position of the cloud shadow edges is uncertain. Therefore, we estimate the expected uncertainty of the cloud shadow edge positions with previously obtained validation results of the used cloud height and cloud-tracking algorithm [26] and combine this with the basic uncertainty information. Hereby, a transient bandwidth is determined around the expected cloud shadow edges with increased uncertainties.…”