3D Cloud reconstruction through geospatially-aware Masked Autoencoders
This addresses the need for real-time 3D cloud data to reduce uncertainties in climate models, representing an incremental advance in domain-specific applications.
The study tackled 3D cloud reconstruction from geostationary imagery and radar data to improve climate predictions, achieving results that outperform state-of-the-art methods like U-Nets with geospatial encoding enhancing performance.
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.