  This paper provides an outlook on the future of operational weather
prediction given the recent evolution in science, computing and machine
learning. In many parts, this evolution strongly deviates from the strategy
operational centres have formulated only several years ago. New opportunities
in digital technology have greatly accelerated progress, and the full
integration of computational science in numerical weather prediction centres is
common knowledge now. Within the last few years, a vast machine learning
research community has emerged for creating new and tailor-made products,
accelerating processing and - most of all - creating emulators for the entire
production of global forecasts that outperform traditional systems. In this
context, the role of both numerical models and observations is changing from
being equation to data driven. Analyses and reanalyses are becoming the new
currency for training machine learning, and operational centres are in a
powerful position as they generate these datasets based on decades worth of
experience. This environment creates incredible opportunities to progress much
faster than in the past but also uncertainties about what the strategic
implications on defining cost-effective and sustainable research and operations
are, and how to achieve sufficient high-performance computing and data handling
capacities. It will take individual national public services a while to
understand what to focus on and how to coordinate their substantial investments
in staff and infrastructure at institutional, national and international level.
This paper addresses this new situation operational weather prediction finds
itself in through formulating the most likely "what if?" scenarios for the near
future and provides an outline for how weather centres could adapt.
