A Stereo Camera Simulator for Large-Eddy Simulations of Continental
Shallow Cumulus clouds based on three-dimensional Path-Tracing
Abstract
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 Blender path-tracing method
allows accurate reconstruction of up to 70% of the
visible cloud edges, depending on camera distance and accuracy
thresholds. Additionally, we conducted sensitivity tests and find that
our method remains consistent and independent of changes in its
hyperparameters. The sensitivity of the stereo reconstruction algorithm
to cloud optical thickness is investigated, finding a cloud boundary
placement error of approximately 182 m. This error can be considered
typical for cloud boundary reconstruction using stereo camera imagery in
general. The results provide proof of principle for future use of the
method for evaluating LES clouds against real camera imagery, and for
further optimizing the configuration of such camera networks.