Machine learning is revolutionizing weather forecasting – the next step is a change in how we work

Citation: Dueben, P., Bauer, P., Fuhrer, O., Koldunov, N., & Kristiansen, J. (2026). Machine learning is revolutionizing weather forecasting – the next step is a change in how we work. arXiv preprint arXiv:2606.25076v1.Key Takeaways: A Shift in the Forecasting Value Chain: While machine learning has rapidly achieved competitive skill in weather predictions, the next critical phase is a complete evolution of working practices and operating models. This transition will fundamentally reshape how models are coded, how observational data is exploited, and how forecasts are verified and turned into public services.The Rise of Agentic AI and Automated Workflows: The traditional, slow-paced manual approach to Earth-system model development is giving way to AI-assisted and agentic workflows. Large Language Models (LLMs) and software agents will increasingly handle the writing, testing, optimizing, and porting of code, shifting the role of human scientists from active programmers to supervisors, test designers, and monitors.Modernized Software and Open Data Stewardship: To benefit from rapid AI innovation, meteorological centres must adopt industry-standard software frameworks (like Python, PyTorch, and JAX) and modular code structures. This runs parallel to a major shift in data stewardship, moving toward highly compressed, cloud-native, open-access datasets that can be efficiently searched and streamed by both humans and AI agents.On-the-Fly Generative Emulation: Emerging generative machine learning foundation models will enable interactive, real-time "what-if" simulations and climate scenario exploration. Instead of moving or storing massive datasets, models can recreate specific atmospheric states on demand, though this introduces a critical need for rigorous verification techniques to distinguish physical realism from AI hallucinations.Organizational Risks and Preserving Expertise: Adapting to mixed human-AI environments presents risks, including the potential erosion of expert scientific knowledge if critical workflows are fully offloaded to AI. Weather and climate centres must proactively design transparent model diagnostics, continuous testing, and educational practices to maintain human operational understanding.