AI IN SUSTAINABILITY
Operational meteorologists then evaluate and interpret those outputs for decision‐makers, ranging from airport operators to local authorities managing flood risk. Kirstine notes that“ 92.5 % of our next‐day temperature forecasts are accurate within two degrees,” a level of performance that has been steadily improving. Yet incremental gains are no longer enough, especially as climate change drives more frequent extremesand more complex risks.“ We are constantly looking for ways to improve,” Kirstine explains,“ and that’ s why we have been keeping a very close eye on AI.”
In 2022, the Met Office created a data science framework to guide how AI and machine learning would be integrated into forecasting and services. At that stage, AI was expected to be an evolution, not a revolution, with careful experimentation and gradual deployment into parts of the value chain. Then, over the 2022 – 23 winter, everything changed.
AI models challenge the old paradigm While Kirstine was planning a quiet Christmas exploring early large language models such as ChatGPT, the research landscape for AI weather prediction moved dramatically. Several powerful machine learning weather models, developed largely by major technology companies, were released and benchmarked against traditional systems.
“ These models showed a step change in performance,” Kirstine reveals.“ They were genuinely competitive with physicsbased approaches.”
That raised a fundamental question for policymakers and the public – if AI can predict weather as well as conventional systems, do we still need billion‐pound supercomputers and specialist meteorologists? For a public body accountable for safety‐critical information, the answer required rapid, evidence‐based action.
128 April 2026