Deep learning for precipitation nowcasting in Slovenia

Abstract

Accurate probabilistic precipitation nowcasting remains a major challenge due to the inherent uncertainty and complexity of atmospheric systems, which deterministic models often fail to capture. This thesis addresses this gap by introducing Conditional Flow Matching (CFM), a novel generative modeling approach, to the task of probabilistic nowcasting. We adapt state-of-the-art deep learning architectures to serve as the backbone for CFM, enabling the generation of diverse, high-fidelity ensemble forecasts. Our method achieves state-of-the-art performance on the SEVIR dataset, with a 15% improvement in CRPS over strong baselines like CasCast. We further validate the approach on the ARSO dataset, curated for nowcasting in Slovenia, where transfer learning from SEVIR yields consistent performance gains. Both qualitative and quantitative results demonstrate that CFM produces sharp, reliable, and spatially coherent forecasts, thus advancing the state of probabilistic nowcasting.