5 citations · 6 across the 8 of their papers we have counts for
7 papers · 1 filter
Varda-single-1.0: deterministic data-driven weather forecasting at 1 km resolution over Switzerland's complex topography
Alberto Pennino, Francesco Zanetta, Michele Cattaneo +28
We present Varda-single-1.0, a medium-range data-driven weather prediction system built for the Alpine domain. It provides hourly deterministic regional forecasts on a mesh of 1 km…
HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting
Magnus Sikora Ingstad, Mariana C. A. Clare, Olav Ersland +16
Many forecast applications require high frequency temporal resolution, yet most state-of-the-art data-driven weather forecasting systems operate at 6-hourly resolution. Although di…
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…
AIFS-DOP: End-to-End Medium-Range Weather Prediction from Observations Alone with Machine Learning
Ewan Pinnington, Peter Lean, Mihai Alexe +8
We introduce the Artificial Intelligence Forecasting System for Direct Observation Prediction (AIFS-DOP). AIFS-DOP is trained on a 40-year harmonized dataset of gridded observation…
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…
Downscaling weather forecasts from Low- to High-Resolution with Diffusion Models
Joffrey Dumont Le Brazidec, Simon Lang, Martin Leutbecher +9
We introduce a probabilistic diffusion-based method for global atmospheric downscaling implemented within the Anemoi framework. The approach transforms low-resolution ensemble fore…
AIFS-COMPO: A Global Data-Driven Atmospheric Composition Forecasting System
Paula Harder, Johannes Flemming, Mihai Alexe +3
We introduce AIFS-COMPO, a skilful medium-range data-driven global forecasting system for aerosols and reactive gases. Building on the ECMWF Artificial Intelligence Forecast System…