AIFS: A data-driven probabilistic forecasting system

Mariana Clare, ECMWF

AI is driving progress in many fields and weather forecasting is no exception. The past few years have seen several data-driven weather forecasting models that are comparable in skill to leading traditional models. Over the past year, ECMWF has been developing its own data-driven forecasting system, the AIFS. This talk will give an overview of AIFS and discuss its performance for a few significant weather events. It will also showcase ongoing research efforts at ECMWF, including data-driven probabilistic ensemble forecasting, and learning to predict directly from observations.

Mariana Clare is a researcher at the European Centre for Medium Range Weather Forecasts (ECMWF), where she works on building a machine learning model for weather forecasting. She is particularly interested on how to capture the model uncertainty in these data-driven approaches. She recently received a PhD from Imperial College London, focussing on developing advanced numerical and statistical techniques to quantify uncertainty in coastal ocean models. By training she is a mathematician, having done her undergraduate degree in Mathematics at the University of Oxford.

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