5 papers
Machine learning is revolutionizing weather forecasting -- the next step is a change in how we work
Peter Dueben, Peter Bauer, Oliver Fuhrer +2
Following the success of machine learning in producing weather predictions with competitive skill compared to complex traditional systems, this article shifts attention from foreca…
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…
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…
High-Resolution Probabilistic Data-Driven Weather Modeling with a Stretched-Grid
Even Marius Nordhagen, Håvard Homleid Haugen, HÃ¥vard Homleid Haugen +12
We present a probabilistic data-driven weather model providing ensembles of high spatial resolution realizations of 87 variables at arbitrary ensemble size and forecast length. The…
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…