Mesoscale Convective Systems in DYAMOND Models: A Feature Tracking Intercomparison.
Copernicus Publications (2025)
3D Cloud reconstruction through geospatially-aware Masked Autoencoders
(2025)
Invertible Neural Networks for Probabilistic Aerosol Optical Depth Retrieval
IEEE Transactions on Geoscience and Remote Sensing Institute of Electrical and Electronics Engineers (IEEE) 63 (2025) 1-13
Statistical constraints on climate model parameters using a scalable cloud-based inference framework – CORRIGENDUM
Environmental Data Science Cambridge University Press (CUP) 4 (2025)
3D Cloud reconstruction through geospatially-aware Masked Autoencoders
Workshop paper at “Machine Learning and the Physical Sciences”, NeurIPS (2024)
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.