How the Met Office Uses Data Assimilation to Produce Its Forecasts
Data assimilation is central to the Met Office's weather and climate predictions. It is an advanced method that merges millions of real-world observations with the latest model forecasts to create the most accurate possible representation of environmental systems like the atmosphere.
Role in Numerical Weather Prediction
Data assimilation for the atmosphere is a vital part of numerical weather prediction. This process is essential for maintaining the accuracy of forecasts and plays a key role in the Met Office's Next Generation Modelling Systems (NGMS) strategy, especially as they prepare for the capabilities of a new supercomputer.
Continuous Assimilation Cycle
Numerical weather prediction is a continuous process, not a single event. The data assimilation cycle repeats regularly: every six hours for the global model and every hour for the high-resolution UK model. Each cycle starts with a previous atmosphere forecast, called the ‘background,’ and integrates millions of new observations.
Purpose of Assimilation
The goal of this process is to adjust the background forecast to generate the best possible initial conditions for the next prediction run.
Key Ingredients in Each Cycle
- Previous forecast (background)
- New observational data
- Management of uncertainties
“Understanding and managing these uncertainties is crucial, as they determine how much weight we give to each ingredient in the final 'analysis', the word we use to describe the corrected state of the atmosphere.”
This careful balance ensures that the final analysis reflects the most reliable information available for accurate forecasting.
Author's summary: Data assimilation continuously refines weather models by integrating real observations and forecasts, enabling the Met Office to deliver precise and reliable atmospheric predictions.