How the Met Office Uses Data Assimilation for Forecasting
Data assimilation forms the core of the Met Office’s weather and climate predictions. It is a refined technique that merges millions of real-world observations with model forecasts to create the most accurate picture of environmental systems such as the atmosphere.
This process is a key part of numerical weather prediction, ensuring forecasts remain reliable. It also plays a major role in the Next Generation Modelling Systems (NGMS) initiative, designed to harness the power of the Met Office’s new supercomputer.
The Continuous Forecasting Cycle
Weather prediction is not static but operates in a recurring cycle. For the global model, data assimilation runs every six hours, while for the high-resolution UK model it occurs every hour. Each cycle starts with a previous forecast of atmospheric conditions, known as the background, and integrates millions of fresh observations to update it.
“The goal is to correct the background to produce the best possible initial conditions for the next forecast run.”
Key Components of Each Cycle
- Integration of observation data with model forecasts.
- Adjustment of the background forecast to reflect real-time atmospheric conditions.
- Creation of the corrected atmospheric state, known as the analysis.
- Continuous management of uncertainties to calibrate the relative importance of each data source.
“Understanding and managing these uncertainties is crucial, as they determine how much weight we give to each ingredient in the final analysis.”
Author’s Summary
Accurate weather forecasts at the Met Office rely on data assimilation—an evolving process that blends global observations with model data to refine atmospheric predictions.