3 papers
cs.LG2026
Assessing the Utility of Volumetric Motion Fields for Radar-based Precipitation Nowcasting with Physics-informed Deep Learning
Peter PavlÃk, Anna Bou Ezzeddine, Viera Rozinajová
Estimating motion from spatiotemporal geoscientific data is a fundamental component of many environmental modeling and forecasting tasks. In this work, we propose a physics-informe…
cs.CV2025
Do Echo Top Heights Improve Deep Learning Nowcasts?
Peter PavlÃk, Marc Schleiss, Anna Bou Ezzeddine +1
Precipitation nowcasting -- the short-term prediction of rainfall using recent radar observations -- is critical for weather-sensitive sectors such as transportation, agriculture,…
cs.LG2025
Fully Differentiable Lagrangian Convolutional Neural Network for Physics-Informed Precipitation Nowcasting
Peter PavlÃk, Martin Výboh, Anna Bou Ezzeddine +1
This paper presents a convolutional neural network model for precipitation nowcasting that combines data-driven learning with physics-informed domain knowledge. We propose LUPIN, a…