The Met Office short-range ensemble system — MOGREPS

MOGREPS logoThe Met Office Global and Regional Ensemble Prediction System (MOGREPS) is an ensemble system that produces uncertainty information for short-range forecasts, up to two days ahead. It focuses on aiding the forecasting of rapid storm development, wind, rain, snow and fog. 

MOGREPS has two components:

  • the global ensemble — produces forecasts for the whole of the globe
  • the regional ensemble — produces forecasts for an area covering the North Atlantic and Europe (NAE) (shown by the coloured area in Fig. 1)
Fig. 1 - area covered by MOGREPS
Fig 1: Regions covered by MOGREPS

Regional and global ensembles

In the regional ensemble the model parameters (temperature, pressure, wind, humidity, etc.) are forecast at grid points separated by about 24 km, and the model has 38 vertical levels.

Both the global and regional ensembles have 24 ensemble members — each model is run using 24 different starting conditions and produces 24 different forecasts.

The NAE ensemble covers a limited area, so the global ensemble provides information on the weather entering the NAE domain through the boundaries. Because the global ensemble covers a much larger area it has to be run at a lower resolution, so the parameters are forecast at grid points separated by about 90 km.

Accounting for errors

There are several sources of uncertainty in weather forecasting which can cause errors in the forecast, including:

The starting conditions

The future evolution of the atmosphere is very sensitive to small errors in the analysis that we use to start the forecast. To create an ensemble forecast we make many small changes to the analysis, to create a set of 24 different starting conditions, from which we run 24 different forecasts.

The forecast model

The model tries to replicate the complex dynamics of the atmosphere and it does this by including many equations and approximations. These approximations will not always adequately represent the processes taking place and this can lead to errors in the forecast. To account for as many different causes of forecast error as possible, MOGREPS makes small random variations to the forecast model itself, as well as changes to the initial state.

While MOGREPS focuses on producing short-range ensemble forecasts we also use another ensemble system to produce uncertainty information for medium-range forecasts.