{"slug":"the-met-office-makes-leaps-in-last-mile-forecast-generation-with-amazon-nova","url":"https://findausecase.com/use-cases/the-met-office-makes-leaps-in-last-mile-forecast-generation-with-amazon-nova","title":"The Met Office Makes Leaps in Last Mile Forecast Generation with Amazon Nova","description":"The UK's Met Office worked with AWS to prototype automated maritime Shipping Forecast text generation using Amazon Nova Pro and a fine-tuned Nova Lite vision-language model, achieving 62% LLM accuracy for complete forecast generation and 83% gale warning accuracy versus forecaster observations, built in four weeks.","company":"Met Office","industry":"Government & Public Sector","country":"United Kingdom","aiCapabilities":["Generative AI","Computer Vision"],"technology":["Amazon Nova Foundation Models","Amazon Bedrock","Amazon SageMaker"],"deployment":"Unknown","problemStatement":"For key marine warning services such as the Shipping Forecast, expert meteorologists at the Met Office spend approximately 2,920 hours a year manually evaluating vast datasets spanning wind, waves, visibility, and other weather variables across 31 sea areas, then condensing that data into a prescribed text format for broadcast; the Met Office wanted to explore whether AI could augment and automate this textual weather forecast generation as data volumes grow.","solutionApproach":"Working with the AWS Specialist Prototyping Team over four weeks, the Met Office developed a discovery pathfinder prototype using Amazon Nova Foundation Models to turn raw gridded weather data into readable forecast text. It used Nova Pro in Amazon Bedrock with maritime-specific prompt engineering to convert gridded data into text descriptions and forecasts (an LLM approach via an intermediate text representation), and fine-tuned Nova Lite using Amazon SageMaker distributed training as a vision language model to process hourly weather evolution through visual pattern recognition, resulting in a single simplified workflow that could be customized to other use cases.","businessValue":"Evaluated with strict word-level comparisons against human-written forecasts over a three-month period, the prototype demonstrated 62 percent LLM accuracy for complete forecast generation, 52 percent VLM accuracy in data-to-text conversion, and 83 percent gale warning accuracy compared with forecasters' observations. The Met Office describes this as an early, significant milestone rather than an operational deployment; further refinement and testing is required before any potential AI-based improvements to the Shipping Forecast can be made operational, but the pathfinder provides a foundation for possible efficiencies across the almost 300 Met Office products and services that involve transforming raw grid data to text.","evidence":{"band":"high"},"sourceUrl":"https://aws.amazon.com/solutions/case-studies/met-office-case-study/","dates":{"publishedAt":"2026-10-02T05:46:35.593Z","publishedAtSource":"pipeline","updatedAt":"2026-10-02T05:46:35.593Z"},"license":"Open for reading and citing with a link to https://findausecase.com/use-cases/the-met-office-makes-leaps-in-last-mile-forecast-generation-with-amazon-nova. Bulk republication requires permission."}