3D Cloud reconstruction through geospatially-aware Masked Autoencoders
Journal:
arXiv
Published Date:
Jan 3, 2025
Abstract
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.
