How to interface between Shallow Cumulus Modelling and Stereo Camera Observations
Shallow cumulus clouds (ShCu) measurements are crucially important in evaluating Large-Eddy Simulations (LES) and ShCu-parameterizations in numerical weather and climate models. However, these data still mainly consist of one-dimensional profile data, often sampled by lidars or radars. A new method for adding multi-dimensional information is to use networks of multiple hemispheric cameras, which remotely observe ShCu in unprecedented spatial details constantly at high temporal frequency. These cameras provide a large field of view, enabling us to observe whole ShCu-life cycles. Thus, these networks strongly complement existing ground-based instruments. To objectively estimate camera networks' accuracy, we have to test them against virtual LES-cloud fields, that act as ground truth. However, for this purpose virtual camera projections of these cloud fields are needed.
Our study aims to generate such projections by combining radiative transfer theory with open-source path-tracing. With these projections, we emulate our camera network, currently installed at the Jülich Observatory for Cloud Evolution (JOYCE), Germany as part of the ongoing SOCLES project. As input, we use LES-cloud fields. Via the emulated camera images, we reconstruct the cloud fields back in the same way the camera network does it from real-world images. However, by using artificial images over real-world images, we have the advantage of already knowing the whole cloud field. This knowledge enables us to statistically analyze and optimize our network. Concretizing this, here are our research objectives:
- Objectively estimate the efficiency of our camera network
- Analyze the capability of our camera network by investing how much of a cloud shell is on average visible
- Optimize the camera network, using our new insights
Our camera network emulation works well in this workflow. For the selected days, about 70% of the mutually visible cloud grid boxes were rightly reconstructed by our artificial camera network. About 53% of a ShCu-cloud shell is averagely visible by a single stereo camera pair of our network at a single time point. With increasing distance between the two cameras of such stereo camera pairs, fewer cloud shell areas are detected. In fact, for every extra kilometer, about 3.3% of a cloud shell is lost on average.