2 papers
cs.LG2026
Timestep-Conditioned Transformers for Global Weather Forecasting
Sam Levang, Fran Bartolic, Ty Dickinson +3
Existing machine-learning weather forecasting models rely on predetermined and fixed autoregressive timesteps. The choice of model timestep involves a fundamental trade-off: shorte…
cs.LG2026
Probabilistic Transformers for Joint Modeling of Global Weather Dynamics and Decision-Centric Variables
Paulius Rauba, Viktor Cikojevic, Fran Bartolic +3
Weather forecasts sit upstream of high-stakes decisions in domains such as grid operations, aviation, agriculture, and emergency response. Yet forecast users often face a difficult…