Roel Neggers

The complex spatial and temporal structure of cumulus clouds complicates their representation in weather and climate models. Classic meteorological instrumentation struggles to fully capture these features. Networks of multiple high‐resolution hemispheric cameras are increasingly used to fill this data gap, and provide information on this missing multi‐dimensional spatial information. In this study, a path‐tracing algorithm is used to generate virtual camera images of resolved clouds in large‐eddy simulations (LES). These images are then used as a camera network simulator, allowing reconstructions of three‐dimensional cloud edges from the model output. Because the actual LES cloud field is fully known, the combined path‐tracing and reconstruction method can be statistically analyzed. The method is applied to LES realizations of summertime shallow cumulus at the Jülich Observatory for Cloud Evolution (JOYCE), Germany, which also routinely operates a camera network. We find that the path‐tracing method allows accurate reconstruction of up to 70% of the visible cloud edges. Additional sensitivity tests show that the method is robust for changes in its hyperparameters. The sensitivity to cloud optical thickness is also investigated, finding a cloud boundary placement error of approximately 182 m. This error can be considered typical for cloud boundary reconstruction using real stereo camera imagery. The results provide proof of principle for future use of the method for evaluating LES clouds against camera network imagery, and for further optimizing the configuration of such camera networks.