Minisymposium Presentation

Recent developments in machine learning for weather forecasting have led to data-driven models that are comparable in skill to leading physics-based NWP systems. Over the past year, ECMWF has been developing its own data-driven forecasting system, the AIFS. By leveraging both data and model parallelism, AIFS can be trained across O(100) GPUs; the latest version runs at a resolution of ca. 0.25-degrees. We give an overview of AIFS and ai-models, the pipeline that has been developed by ECMWF to produce data-driven weather forecasts, and runs daily – with open data delivery - on ECMWF's HPC. In addition, we showcase early results of ongoing research efforts at ECMWF, including data-driven probabilistic ensemble forecasting, and direct observation prediction - a task that aims to produce a weather forecast solely from observational data.