Clouds play a key role in Earth's radiation balance with complex effects that introduce large uncertainties into climate models. Real-time 3D cloud data is essential for improving climate predictions. This study leverages geostationary imagery from MSG/SEVIRI and radar reflectivity measurements of cloud profiles from CloudSat/CPR to reconstruct 3D cloud structures. We first apply self-supervised learning (SSL) methods-Masked Autoencoders (MAE) and geospatially-aware SatMAE on unlabelled MSG images, and then fine-tune our models on matched image-profile pairs. Our approach outperforms state-of-the-art methods like U-Nets, and our geospatial encoding further improves prediction results, demonstrating the potential of SSL for cloud reconstruction.
@article{arxiv.2501.02035,
title = {3D Cloud reconstruction through geospatially-aware Masked Autoencoders},
author = {Stella Girtsou and Emiliano Diaz Salas-Porras and Lilli Freischem and Joppe Massant and Kyriaki-Margarita Bintsi and Guiseppe Castiglione and William Jones and Michael Eisinger and Emmanuel Johnson and Anna Jungbluth},
journal= {arXiv preprint arXiv:2501.02035},
year = {2025}
}