The Advanced Technologies Section (ATS) of the National Center for Computational Sciences at ORNL is a world leader in developing and deploying scientific and technical solutions for leadership-class computing environments. The R&D activities of ATS are organized around designing and deploying leadership class systems, developing artificial intelligence solutions for science and smart facilities of the future, and stewardship of data and workflows at scale to enable science. The ATS Seminar Series is a forum for learning from experts and engaging with collaborators to advance their scientific mission.

Seminar Title:

Machine learning for weather and climate predictions

The talk outlines how machine learning, and in particular deep learning, could help to improve weather predictions in the coming years, and presents an overview of the work on machine learning methods that is ongoing at the European Centre for Medium-Range Weather Forecasts. Weather predictions are based on models of the Earth system — a huge system that consists of many individual components and shows chaotic behavior. For such a system, conventional tools are often struggling to provide satisfying results. On the other hand, a huge amount of data is available from both observations and modelling. Therefore, many machine learning applications are currently tested to improve the different components across the workflow of numerical weather predictions.

Speaker:

Peter Dueben

AI and Machine Learning Coordinator at the European Center for Medium Range Weather Forecasting

Dr. Peter Dueben is the AI and Machine Learning Coordinator at the European Centre for Medium-Range Weather Forecasts, and holds a University Research Fellowship of the Royal Society that enables him to perform research towards the use of machine learning, high-performance computing, and reduced numerical precision in weather and climate predictions. He has also a strong interest in the quantification of uncertainty of predictions for chaotic systems. Dr. Dueben is coordinator of the MAELSTROM EuroHPC-Joint Undertaking project, work-package leader of the ESiWACE2 H2020 project, and Co-PI of an US-INCITE grant to perform season-long, global, storm-resolving simulations. Before moving to ECMWF, Dr. Dueben has written his PhD thesis at the Max Planck Institute for Meteorology, and has worked as PostDoc with Professor Tim Palmer at the University of Oxford.

Recorded Presentation:

Vimeo link: https://vimeo.com/568936156