Women in WeatherGenerator: Celebrating International Women's Day

Science advances through the courage to ask bold questions and across the WeatherGenerator project, women are at the forefront of asking them. From developing the core AI architecture to adapting it for real-world applications in energy, health, and extreme weather forecasting, female researchers and engineers are shaping the future of AI-driven meteorology every day.

WeatherGenerator is an EU Horizon project bringing together 16 leading European organisations to build a next-generation foundation model for weather and climate. Behind this mission is a diverse community of scientists, and on this International Women’s Day, we want to shine a light on some of the extraordinary women who make this project what it is. Coming from across the globe, bringing expertise in atmospheric chemistry, machine learning, probabilistic modelling, remote sensing, and operational meteorology.

We are proud to introduce them.

Asma Semcheddine Forschungszentrum Jülich – Jülich Supercomputing Centre | Theme 2: Core Model Development

I am an aspiring researcher working at the intersection of atmospheric chemistry, machine learning, and Earth system modelling. My work focuses on tropospheric trace gas simulation, air quality forecasting, and deep learning. I hold a PhD in remote sensing image classification from the University of Boumerdes, Algeria, and I am currently based at the Jülich Supercomputing Centre, where I contribute to the core model development efforts of WeatherGenerator.

Sara Akodad Météo-France | Theme 3: Applications

I am a researcher at Météo-France, where I have been working for the past two years on artificial intelligence for weather forecasting. My research focuses on developing data-driven models for regional, high-resolution weather prediction that learn directly from large meteorological datasets. By leveraging modern machine learning techniques, I aim to improve the accuracy and detail of forecasts and explore how purely data-driven approaches can advance the next generation of meteorological prediction systems.

I obtained my PhD on ensemble methods for satellite image classification and forest health monitoring. After that, I spent two years as a researcher at the French space agency (CNES), contributing to the development of methods for 3D geospatial mapping from satellite imagery.

Sarah Ibrahimi Royal Netherlands Meteorological Institute (KNMI) | Theme 3: Applications

I am a Machine Learning researcher specialising in probabilistic modelling for extreme weather prediction. Within WeatherGenerator, I work on advancing the core model to deliver high-resolution ensemble forecasts of extreme events across Western Europe — tackling challenges in uncertainty quantification, extreme event modelling, and parameter-efficient fine-tuning.

I earned my PhD from the University of Amsterdam, where I developed robust methods for visual and multimodal similarity search under challenging real-world data constraints.

Jesica Piñon Statkraft | Theme 3: Applications

I am a Senior Meteorologist at Statkraft, where I help transform weather and climate insights into practical support for our renewable energy operations. Having accurate and actionable weather and climate information is crucial for guiding both daily operational decisions and long-term investment planning.

In WeatherGenerator, I am part of Theme 3, where we are adapting the model for real use in the energy sector. I coordinate the team working on our applications and also contribute to fine-tuning the model so it fits our operational needs — especially for inflow forecasting, where reliable output really matters for our business.

And my simple advice for women working in science: trust yourselves. Your ideas are worth voicing. Don’t be afraid to take space, ask questions, and keep moving forward.

Tansylu Akhmetova Buluttan Weather Intelligence | Theme 3: Applications

I am a Machine Learning engineer at Buluttan Weather Intelligence, working on renewable energy forecasting. I hold a BSc in Computer Science and Engineering from Sabancı University. My work spans model research and development, performance analysis, and the deployment of AI models into operational workflows. Within WeatherGenerator, I contribute by helping design and evaluate fine-tuning experiments to adapt the model for power forecasting applications.

Laure Raynaud Météo-France | Team Leader, Research Department

I started my career at Météo-France 20 years ago, focusing on the development of operational Numerical Weather Prediction systems at both global and regional scales. Alongside model development, I have worked with a wide range of end-users — from the agriculture, energy, and aviation domains — to co-design specific applications of our forecasts. My current challenge is to coordinate the development of data-driven weather models at Météo-France, supported by participation in several European research projects, including WeatherGenerator.

Florentine Weber

Jülich Supercomputing Centre & Centre for Earth System Observations and Computational Analysis (CESOC) | Theme 2: Core Model Development

I am a physicist working at the intersection of machine learning, high-performance computing, and climate modelling. I support international scientific initiatives such as the World Climate Research Programme and global climate assessments, because I am deeply committed to bridging complementary expertise across communities.

To me, progress thrives at the intersections of diverse perspectives. I facilitate connections between scientists and stakeholders to spark dialogue and unearth innovative insights into our changing planet. I hold a PhD from the University of Sheffield, where I studied drying signals in the atmosphere and was trained at the Grantham Centre for Sustainable Futures. My earlier research integrated human influences such as irrigation and urbanisation into ECMWF forecasting systems, demonstrating how societal dynamics shape climate predictions.

Happy International Women’s Day from the entire WeatherGenerator team!