Numerical weather prediction
Numerical weather prediction is the way weather forecasts are made, as of 2010. This is done using mathematical models of the atmosphere. Such models describe the current weather conditions, and how they change over time using equations. Using the current weather conditions, the equations can be solved, or approximated to tell what the weather will be like in the near future. The relevant physical parameters, such as pressure, temperature, the direction and the sped of the wind are taken to be functions of time. These are modelled with a system of partial differential equations. This is a dynamic system that is solved numerically. Most of these equations are implemented using FORTRAN. The equations are apporximated. Since this takes a lot of computing power or time, supercomputers are used for this task, most of the time.
Local weather prediction
The results obtained are usually too inaccurate to be used for predicting the weather at a given location. For this reason, meteorologists check the values for plausibility, and compare them to historical data. In other words, meteorologists use the data to produce the weather forecast.
Model Output Statistics is a statistical model that was developed in the 1960s and 1970s. It uses regression for fully automated forecast. With it, historical data is analysed automatically. One of its applications is called Direct Model Output. MOS uses both historical data and statistical modeling. The problem purely statistical modelling is that predictions beyond about siux hours are useless - the model is simply too complex.
Another wll-known model is called Global Forecast System, which is run by the US weather service, NOAA. It calculates the forecast four times a day. Since the forecast data is available for free, it is widely used, especially by smaller stations.
