Learning-Based Retrieval of Cloud Vertical Velocity from Multi-View Satellite Imagery

This thesis presents a deep learning framework for estimating physically meaningful atmospheric quantities directly from passive multi-view satellite observations. A comprehensive simulation pipeline was developed by combining large-eddy simulations of shallow cumulus clouds with physically based volumetric Monte Carlo rendering, atmospheric radiometric correction, sensor noise modeling, and image alignment to generate realistic synthetic satellite imagery together with corresponding ground-truth atmospheric fields. These data were used to train a fully convolutional encoder-decoder network operating on two temporally consecutive three-view observations.

The proposed framework was evaluated on three retrieval tasks: cloud-top vertical velocity, cloud-top height, and vertical velocity at multiple fixed altitude layers throughout the atmospheric column. Experimental results demonstrate accurate retrieval of cloud-top geometry together with successful estimation of cloud-top and volumetric atmospheric motion at the native 20~m spatial resolution of the simulations. The framework recovers physically meaningful atmospheric velocity fields from passive multi-view observations and successfully infers vertical motion throughout much of the cloud-containing atmosphere using a single network architecture and observational input.

The presented results demonstrate that passive multi-view satellite imagery contains sufficient information to support dense retrieval of both geometric and dynamical cloud properties without explicit reconstruction of the three-dimensional cloud structure or intermediate physical modeling. These findings establish the feasibility of learning-based atmospheric retrieval from physically realistic simulations and provide a foundation for future development of operational data-driven cloud retrieval methods.