2 citations · 2 across the 3 of their papers we have counts for
3 papers
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…
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…
SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking
Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler +8
Subseasonal forecasting of the weather two to six weeks in advance is critical for resource allocation and advance disaster notice but poses many challenges for the forecasting com…