Weather, the Royal Meteorological Society’s long-standing member journal, continues to serve as a publication of choice for research that shapes both scientific thinking and practical understanding of weather and climate. With its focus on accessible, peer-reviewed science that speaks to researchers, practitioners and enthusiast readers alike, the journal regularly showcases work that underpins the societal value of weather and climate science, from risk awareness to real-world decision-making.

This latest example comes from colleagues at the Met Office, whose new paper in Weather brings together 25 years of research to examine why probability-based forecasting is becoming central to modern weather prediction – and why concerns about public understanding of uncertainty may be misplaced.

Why probability is the key to future weather forecasts 

Probability-based forecasts can better inform weather-based decision-making, according to new Met Office research.

For the first time, the Met Office has brought together 25 years of research to explain why probabilistic forecasting is becoming central to UK weather prediction.

The research, funded by Public Weather Service and published in the Royal Meteorological Society’s Weather, is a comprehensive analysis of how probability-based forecasts, which capture the inherent uncertainty of predicted weather patterns, can provide enhanced forecasts that better inform decision-making.

In addition, the peer-reviewed research discusses how public understanding of probability-based forecasts should not be a barrier to uptake as has been previously thought.

Uncertainty in forecasting

Probability forecasts, based on ensemble forecasting as it’s referred to in meteorological science, is a fundamentally different approach to many weather forecasts that appear on TV. 

While on traditional broadcasts, presenters stand in front of a map and show a single (deterministic) projection of future weather patterns, ensemble forecasts use slightly different starting conditions to run the forecast forward multiple times. This approach produces an ensemble forecast – typically 20-50 simulations – offering a richer and more nuanced picture of possible outcomes.  

The graph above gives a representation of how ensemble forecasting works. Lines closer together indicate greater certainty, while large deviations represent uncertainty in possible outcomes. 

The Met Office has pioneered use of ensemble predictions, initially for month-ahead predictions, with research as far back as 1986 on running the forecast ultiple times to better understand the likely scenarios for the atmosphere. Just small changes in starting conditions can result in big changes to forecasts, usually at longer ranges but sometimes at short ranges, so an ensemble forecast is a method of capturing and communicating that uncertainty.

Author of the new research, Met Office Science Fellow Ken Mylne, said: “Ensemble forecasts have often operated as a supplementary system for meteorologists, running alongside single deterministic model runs to provide a measure of uncertainty.

“However, studies over many years show how ensembles provide better predictive skill than single deterministic runs and could, with greater focus on ensembles, capture the range of uncertainty to provide the public with the information they need to make better decisions.”

Understanding probability in forecasts 

The research also addresses whether people can understand uncertainty in forecasts, something that is crucial to realise full benefits from a different approach to producing a weather forecast. 

Communicating uncertainty is often done by presenters with their language, though the most common way uncertainty is communicated through many apps is the ‘% chance of rain’ that is displayed. This is most people’s interaction with ensemble forecasts, communicating the percentage chance of rain falling in a particular hour or day, informed by model runs which capture that uncertainty.

Above, example ideas for how precipitation probabilities could be presented clearly for rapid assimilation and good user comprehension.

Ken explained how previous assumptions around a lack of understanding of probabilities may not be true. He said: “Most previous discussions on expressing probabilities in forecasts started from an assumption that they can be hard for people to understand and that expressing uncertainty could undermine people’s confidence in the forecast and therefore undermine their ability to make decisions.

“However, this research suggests that this assumption is wrong. People can understand probabilistic forecasts and could indeed find it more useful for informing weather-based decisions.”

Read the full articles in February’s Weather:

Probability forecasts – Part 1: ensembles and probabilistic forecasts

Probability forecasts – Part 2: will people understand?