Jesper Dramsch

Jesper Dramsch (they/them) is a Scientist for Machine Learning at the European Centre for Medium-Range Weather Forecasts, where they implement state-of-the-art machine learning solutions for numerical weather prediction. They are a core developer of AIFS (Artificial Intelligence Forecasting System), ECMWF's fully data-driven NWP model, focusing on graph neural networks for weather forecasting. Jesper has led critical technical innovations, including the refactoring of AIFS infrastructure, implementation of hydra-based configuration systems, and development of operational ai-models plugins for FourCastNet, GraphCast, and AIFS. They have established code quality standards through pre-commit hooks and unit testing frameworks, enabling the scaling of the AIFS team. Jesper Dramsch co-led the development of the data-driven weather forecasting ecosystem Anemoi, used by national weather services globally. Beyond technical contributions, Jesper co-organised ECMWF's first MOOC on Machine Learning in Weather & Climate, reaching thousands of participants globally, and continues to organise ML and AIFS training courses for member states. They serve as co-chair of the Working Group Modelling for the UN ITU Resolutions Global Initiative on AI for Natural Disaster Management. Jesper has successfully bridged the gap between research and operations, ensuring reproducibility and maintaining close collaboration with domain scientists and Member State institutions like DWD.

Jesper brings an interdisciplinary background spanning geophysics, physics, and machine learning to their work at ECMWF. Prior to joining ECMWF, they completed a PhD at the Technical University of Denmark, worked as a Machine Learning Engineer at GMV NSL, and served as a consultant educator in Python and ML. Their commitment to science communication extends beyond ECMWF through multiple channels: teaching on Skillshare, which has over 10,000 students, maintaining the "Late to the Party" newsletter with more than 1,111 subscribers, and achieving YouTube Partner status. As a Fellow of the Software Sustainability Institute, they champion reproducible research practices and maintain several open-source projects, including PythonDeadlin.es and ML.recipes. Jesper has contributed to major ML frameworks through documentation for TensorFlow, Scikit-Learn, and Pandas, and has been recognised as a Kaggle Top 81 contributor. Their previous work at ECMWF included hybrid machine learning approaches for post-processing, observational operators, and S2S forecasting, as well as, collaboration with Microsoft on the PoET project and contributions to the ITU Focus Group on AI for Natural Disaster Management.

Key Responsibilities

• Continued development of Graph Neural Network for numerical weather forecasting in AIFS and Anemoi
• Co-chair of the Working Group Modelling in the UN ITU Global Initiative on Resilience to Natural Hazards through AI Solutions  
• Implement fine-tuning capabilities such as LoRA in Anemoi
• Security enhancements for the Anemoi framework, e.g. safer checkpoint systems

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