Deep learning-based generation of 3D cloud structures from geostationary satellite data

Clouds and their interdependent feedback mechanisms remain a source of insecurity in climate science. This said, overcoming relating obstacles especially in the context of a changing climate emphasizes the need for a reliable database today more than ever. While passive remote sensing sensors provide continuous observations of the cloud top, they lack vital information on subjacent levels. Here, active instruments can deliver valuable insights to fill this gap in knowledge.

This study sets on to combine the benefits of both instrument types. It aims (1) to reconstruct the vertical distribution of volumetric radar data along the cloud column and (2) to interpolate the resultant 3D cloud structure to the satellite’s full disk by applying a contemporary Deep-Learning approach. Input data was derived by an automated spatio-temporally matching between high-resoluted satellite channels and the overflight of the radar. These samples display the physical predictors that were fed into the network to reconstruct the cloud vertical distribution on each of the radar’s height levels along the whole domain. Data from the entire year 2017 was used to integrate seasonal variations into the modeling routine.

The results demonstrate not only the network’s ability to reconstruct the cloud column along the radar track but also to interpolate coherent structures into a large-scale perspective. While the model performs equally well over land and water bodies, its applicable time frame is limited to daytime predictions only. Finally, the generated data can be leveraged to build a comprehensive database of 3D cloud structures that is to be exploited in proceeding applications.