How the Met Office uses data assimilation to produce its forecasts

How the Met Office Uses Data Assimilation to Produce Its Forecasts

Data assimilation is the core technique behind the Met Office's weather and climate predictions. It merges millions of real-world observations with the latest model forecasts to create the most accurate representation of environmental systems like the atmosphere.

For the atmosphere, data assimilation is a vital part of numerical weather prediction (NWP). This ongoing process helps keep forecasts reliable and is an essential element of the Met Office's Next Generation Modelling Systems (NGMS) strategy, especially as they prepare to use a new supercomputer.

Continuous Cycle of Numerical Weather Prediction

Numerical weather prediction operates in continual cycles rather than as isolated events. The process repeats every six hours for the global model and every hour for the high-resolution UK model. Each cycle starts with a previous forecast called the “background” and integrates millions of new observations.

The objective is to adjust the background to establish the best possible “initial conditions” for the upcoming forecast.

Key Components of Each Assimilation Cycle

Previous forecast (“background”) of the atmosphere’s state
New observations collected from various sources
Understanding and managing uncertainties in both the model and data

"Understanding and managing these uncertainties is crucial, as they determine how much weight we give to each ingredient in the final ‘analysis’," the term for the corrected state of the atmosphere.

This refined state, or analysis, is used as the starting point for the next forecast run, ensuring continuous improvement in forecast accuracy.

Summary: The Met Office employs data assimilation, combining observations and model data in cyclical updates, to continually refine and enhance the accuracy of its weather forecasts.

Met Office — 2025-11-05