6 papers
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…
Observation-guided Interpolation Using Graph Neural Networks for High-Resolution Nowcasting in Switzerland
Ophélia Miralles, Daniele Nerini, Jonas Bhend +2
Recent advances in neural weather forecasting have shown significant potential for accurate short-term forecasts. However, adapting such gridded approaches to smaller, topographica…
An update to ECMWF's machine-learned weather forecast model AIFS
Gabriel Moldovan, Ewan Pinnington, Ana Prieto Nemesio +18
We present an update to ECMWF's machine-learned weather forecasting model AIFS Single with several key improvements. The model now incorporates physical consistency constraints thr…
AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score
Simon Lang, Mihai Alexe, Mariana C. A. Clare +15
Over the last three decades, ensemble forecasts have become an integral part of forecasting the weather. They provide users with more complete information than single forecasts as…