3 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.LG2026
PARDIG: Global Precipitation Estimates Learned from Sparse Satellite Observations
Guy Shalev, Stephan Rasp, Boris Babenko +14
Estimating precipitation at the global scale is vital for Earth system science and disaster response, yet observational coverage and measurement precision remain constrained. Exist…
cs.LG2026
WeatherNext 3: Increasing resolution and performance of global weather models with raw observations
Stephan Rasp, Boris Babenko, Dominic Masters +22
State-of-the-art AI weather models have shown impressive medium-range forecast skill and computational efficiency, but suffer two key shortcomings: their forecasts have lower spati…
cs.LG2025★ 3 cited
Skillful joint probabilistic weather forecasting from marginals
Ferran Alet, Ilan Price, Andrew El-Kadi +8
Machine learning (ML)-based weather models have rapidly risen to prominence due to their greater accuracy and speed than traditional forecasts based on numerical weather prediction…