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Progress and Challenges for the Use of Deep Learning to Improve Weather Forecasts

Peter Dueben from ECMWF

In this video from the UK HPC Conference, Peter Dueben from ECMWF presents: Progress and Challenges for the Use of Deep Learning to Improve Weather Forecasts.

I will present recent studies that use deep learning to learn the equations of motion of the atmosphere, to emulate model components of weather forecast models and to enhance usability of weather forecasts. I will then talk about the main challenges for the application of deep learning in cutting-edge weather forecasts and suggest approaches to improve usability in the future.

Peter Dueben is a Royal Society University Research Fellow at the European Centre for Medium-Range Weather Forecasts (ECMWF). He is contributing to the development and optimization of weather and climate models for modern supercomputers. He is focusing on a better understanding of model error and model uncertainty, on the use of reduced numerical precision that is optimised for a given level of model error, on global cloud-resolving simulations with ECMWF’s forecast model, and the use of machine learning, and in particular deep learning, to improve the workflow and predictions. Peter has graduated in Physics and wrote his PhD thesis at the Max Planck Institute for Meteorology in Germany. He worked as Postdoc with Tim Palmer at the University of Oxford and has taken up a position as University Research Fellow of the Royal Society at the European Centre for Medium-Range Weather Forecasts (ECMWF) in 2017.

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