4 papers
Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction
Hannah Guan, Soukayna Mouatadid, Paulo Orenstein +10
Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes. Today, such forecasts enjoy unprecedented a…
AIFS-SUBS: Extending Data-Driven Forecasting to Sub-Seasonal Timescales
Jakob Schloer, Steffen Tietsche, Christopher D. Roberts +6
Data-driven models now rival numerical weather prediction in the medium range, but extending them to sub-seasonal lead times raises challenges absent at shorter horizons. Errors ac…
Representing the Surface Ocean in ECMWF's data-driven forecasting system AIFS
Sara Hahner, Lorenzo Zampieri, Jean-Raymond Bidlot +22
Machine-learning (ML) models, such as the AIFS at the ECMWF, have revolutionised weather forecasting in recent years. We present an extension of the AIFS that jointly models the at…
A Hybrid Deep-Learning Model for El Niño Southern Oscillation in the Low-Data Regime
Jakob Schloer, Matthew Newman, Jannik Thuemmel +2
While deep-learning models have demonstrated skillful El Niño Southern Oscillation (ENSO) forecasts up to one year in advance, they are predominantly trained on climate model simu…