9 citations · 17 across the 4 of their papers we have counts for
4 papers
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…
GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations
Mihai Alexe, Eulalie Boucher, Peter Lean +11
We introduce GraphDOP, a new data-driven, end-to-end forecast system developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) that is trained and initialised exc…
Data driven weather forecasts trained and initialised directly from observations
Anthony McNally, Christian Lessig, Peter Lean +11
Skilful Machine Learned weather forecasts have challenged our approach to numerical weather prediction, demonstrating competitive performance compared to traditional physics-based…
Advancing operational PM2.5 forecasting with dual deep neural networks (D-DNet)
Shengjuan Cai, Fangxin Fang, Vincent-Henri Peuch +3
PM2.5 forecasting is crucial for public health, air quality management, and policy development. Traditional physics-based models are computationally demanding and slow to adapt to…