Understanding ensemble forecasting: How the Met Office predicts uncertainty

At the Met Office, we strive to provide the most accurate and reliable forecasts possible, using advanced techniques that account for uncertainty and variability.

One of the most powerful tools in our arsenal is ensemble forecasting, a method that helps us understand and communicate the range of possible weather outcomes, and the confidence we have in our predictions.

What is ensemble forecasting?

Ensemble forecasting is a technique that involves running a weather prediction model multiple times, each with slightly different starting conditions. Rather than relying on a single forecast, we generate a set of forecasts, known as an ensemble, where each individual forecast is referred to as an ensemble member.

The reason for this approach lies in the chaotic nature of the atmosphere. Even the smallest changes in observations can lead to significant differences in the forecast. Since it is impossible to observe every detail of the atmosphere’s initial state, there will always be some uncertainty in weather predictions. By running multiple forecasts from slightly varied starting points, we can explore how these uncertainties might affect the outcome, giving us a clearer picture of what the future might hold.

READ MORE: How the Met Office uses data assimilation to produce its forecasts

Why do we need ensembles?

A single forecast provides one possible future, but it cannot capture the full range of uncertainty inherent in weather prediction. Ensemble forecasting allows us to sample this uncertainty, assuming our model is perfect. If we knew the exact starting conditions and our model perfectly represented the atmosphere, we'd always produce a perfect forecast. However, since both observations and models have limitations, ensembles help us estimate the range of possible outcomes and the likelihood of different weather events.

How ensemble forecasts are produced

At the Met Office, ensemble forecasts are generated by running our computer models multiple times, each with small, carefully designed differences in the starting conditions. These differences, or perturbations, are consistent with the uncertainties in observations. Each ensemble member is equally likely, and together they provide a spectrum of possible futures.

As we look further ahead in time, the forecasts produced by different ensemble members can diverge significantly. This divergence reflects the growing uncertainty in the forecast, and helps forecasters assess the confidence they can place in specific weather events.

The Met Office ensemble system: MOGREPS

Our ensemble forecasting system is known as the Met Office Global and Regional Ensemble Prediction System (MOGREPS). MOGREPS is designed to aid the forecasting of rapid storm development, wind, rain, snow, and fog—phenomena where uncertainty can have a significant impact on decision-making.

MOGREPS consists of two main components:

• MOGREPS-G (Global Ensemble): Produces forecasts for the entire globe up to a week ahead, using a model with grid points separated by about 20 km.
• MOGREPS-UK (Regional Ensemble): Focuses on the UK, providing forecasts for the next five days at a much higher level of detail, with grid points separated by about 2.2 km and 70 vertical levels.

The UK ensemble covers a limited area, so the global ensemble provides boundary information for weather entering the UK domain. The higher resolution of the UK ensemble allows for more detailed predictions, while the global ensemble ensures that large-scale weather patterns are accurately represented.

Sources of uncertainty in forecasting

There are two main sources of uncertainty in weather forecasting:

• Starting conditions: The future evolution of the atmosphere is highly sensitive to small errors in the initial analysis. To address this, we create a set of small perturbations to the starting conditions, consistent with the uncertainties in observations. Each ensemble forecast uses 17 perturbations plus the unperturbed analysis, resulting in 18 different forecasts.
• Forecast model: Our models use complex equations and approximations to simulate atmospheric processes. These approximations can introduce errors, as they may not fully capture the intricacies of the real atmosphere. To account for this, MOGREPS introduces small random variations to the model itself, as well as to the initial state.

READ MORE: What do meteorologists do at the Met Office?

How ensembles help forecasters

Ensemble forecasts provide forecasters with a much richer understanding of what weather events may occur. By comparing the different ensemble members, forecasters can assess the likelihood of specific events. If the forecasts vary widely, it indicates a high level of uncertainty; if they are similar, confidence in the prediction increases.

This information is invaluable for decision-making, especially when the stakes are high. For example, predicting the development of severe storms or heavy rainfall can help emergency services and the public prepare more effectively.

Probability forecasts

To make ensemble information more accessible, we often summarise it using probability forecasts. These can be presented in two main ways:

• Range of values: For a specific weather element, such as temperature or wind speed, we provide a range of possible values along with a measure of confidence that the actual value will fall within that range.
• Percentages: We estimate the probability of a defined event occurring by counting the proportion of ensemble members that predict it. For example, if six out of 24 members forecast more than 5 mm of rain at a location, we estimate a 25% chance of that event.

READ MORE: Smarter forecasts, safer seas: Met Office advances maritime forecast accuracy

Probability forecasts help users assess the risks associated with weather events. If the probability of an event is 10%, it means the event will occur on one in ten occasions. We can only evaluate the accuracy of probability forecasts by looking at large sets of forecasts and comparing predicted probabilities with observed outcomes.

Limitations and calibration

While ensemble forecasts provide valuable guidance, they cannot offer a perfect representation of probability. By reviewing past performance, we use statistical techniques to calibrate our forecasts and improve their reliability.

Ensemble forecasting is a cornerstone of modern weather prediction at the Met Office. By embracing uncertainty and using advanced modelling techniques, we provide forecasts that are not only more accurate but also more informative. Whether you are a forecaster, emergency planner, or member of the public, ensemble forecasts help you make better decisions by revealing the range of possible outcomes and the confidence we have in our predictions.

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