Speaker
Description
Over more than half a century, Numerical Weather Prediction (NWP) has achieved remarkable progress. This advancement results from the use of increasingly dense observational data types, progress in high-performance computing, and steady improvements in numerical modeling. This long-standing progress faces new challenges, with standard technologies approaching their physical limits, and models reaching resolutions where global high-resolution datasets are lacking.
To address these challenges a threefold strategy is being pursued. Limited-area models are being used to further increase resolution toward the hectometric scale. Existing models are being refactored to enhance their flexibility and efficiency on emerging computing architectures. Machine learning techniques are being integrated with traditional numerical approaches to develop Machine-Learning NWP.
This talk will explore these three directions, highlighting their implementation within major international initiatives.
