Modelling process

The model is essentially a computer simulation of the processes in the Earth's atmosphere, land surface and oceans which affect the weather. Once current weather conditions are known, the changes in the weather are predicted by the model.

1. Snapshot of the current atmospheric conditions in the area of interest - from the surface to the upper atmosphere - at points on a three-dimensional grid.
2. Wind speed, temperature, pressure, moisture and cloud in each grid box stored in a computer.
3. Set of equations, which describe all of the relevant atmospheric processes, are solved for each grid box to predict the values at that point several minutes later.
4. Process is repeated many times, producing a forecast - from the next day weather forecast  to climate predictions of the coming 100 years.

Unified Model

The flagship numerical model developed and used at the Met Office is called the Unified Model (UM), as, unlike most other NWP centres, different configurations of the same model are used for both weather and climate prediction.

Numerical modelling in-depth

Producing forecasts

Running a numerical weather prediction model is only part of the process in producing a weather forecast. Before a forecast is issued, the output from the model is studied by a forecaster. This human-machine partnership is very important in producing accurate weather forecasts.

Short-range

Hours ahead the forecaster is able to compare a model field against actual observations. This means they can:

• Identify any possible errors, make appropriate allowances and possibly add extra detail to the model forecast - things like summer showers are often too small for the computer to pick up.
• Respond quickly and amend a forecast if necessary.

Medium-range

Days and weeks ahead the forecaster is able to compare the results from our model with those from other centres such as ECMWF, NCEP and Deutscher Wetterdienst (DWD).

If all models are producing approximately the same solution confidence in the forecast would be high. Confidence is also decided by the consistency between model runs. If the model is consistent then confidence may be high but if it suddenly changes then confidence falls rapidly. In these situations the solutions of other models may be crucial. Sometimes, alternative forecasts may be issued with probabilities assigned.