The Met Office Global and Regional Ensemble Prediction System (MOGREPS) is an ensemble system that produces uncertainty information, primarily 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:
Fig 1: Regions covered by MOGREPS
In the regional ensemble the model parameters (temperature, pressure, wind, humidity, etc.) are forecast at grid points separated by about 18 km, and the model has 70 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 60 km.
There are several sources of uncertainty in weather forecasting which can cause errors in the forecast, including:
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 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 the main focus of MOGREPS is on producing short-range ensemble forecasts, we also run a version of the global ensemble to 15 days together with the ensemble system from ECMWF to produce uncertainty information for medium-range forecasts.