Building Shared Foundations for Machine-Learned Weather Prediction

Summary

Machine learning (ML) is rapidly moving from the margins of research into the centre of weather forecasting. Only a few years ago, ML models were largely experimental tools, tested alongside traditional numerical weather prediction (NWP) systems. Today, ML is becoming an increasingly important component of operational forecasting systems, complementing established numerical weather prediction. The potential is significant. Machine learning offers the possibility of producing forecasts faster, timelier and potentially at lower computational cost. It opens the door to new capabilities, such as rapidly generating large, reliable ensembles or learning patterns directly from vast volumes of diverse data. Yet moving from promising research to reliable operational forecasting systems is a far more complex challenge than developing a single ML model. It requires the development of new data-driven methodologies in weather forecasting, including datasets, infrastructure and workflows, presenting European national weather services with a transformative challenge that cannot be tackled alone. This is where the ECMWF Machine Learning Project (MLP) comes in. An international collaboration The ECMWF Machine Learning Project (MLP) is a coordinated international collaboration, led by MET Norway and MeteoSwiss, to develop and integrate ML models into the full operational forecasting system. Launched in 2024 under the umbrella of the EUMETNET E-AI programme, the project is a joint effort between ECMWF and 17 national meteorological services. To achieve the integration of ML weather prediction into the operational environment of each participating national meteorological service, the project is not centred on a single model or algorithm. Instead, it focuses on the joint exploration of the foundations needed for regional to global operational ML forecasting. Teams across Europe and beyond are contributing expertise in areas ranging from dataset preparation to model training, ensemble and initial state generation, and operational deployment. The goal is to understand how ML can function within the full forecasting chain, delivering forecasts to users. Rather than each organisation developing these capabilities independently, the MLP brings together expertise from across Europe and beyond, allowing participating meteorological services to share knowledge, establish common standar…