164 citations · 164 across the 5 of their papers we have counts for
5 papers
On the sensitivity of machine-learned probabilistic weather forecast models to scale-aware scoring rules
Simon Lang, Martin Leutbecher, Sam Hatfield
Probabilistic forecast models can be machine-learned from data using loss functions based on scoring rules such as the Continuous Ranked Probability Score (CRPS). This note summari…
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…
Hybrid ensemble forecasting combining physics-based and machine-learning predictions through spectral nudging
Inna Polichtchouk, Simon Lang, Sarah-Jane Lock +2
We present the first application of spectral nudging in a probabilistic ensemble forecasting framework, combining the physics-based ECMWF Integrated Forecasting System ensemble (IF…
The rise of data-driven weather forecasting
Zied Ben-Bouallegue, Mariana C A Clare, Linus Magnusson +14
Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting. Rapid progress has been made with impressive results for some application…