Techniques range from comparing experimental data with numerical predictions, to more complex approaches which provide information to improve and refine models. The two figures show predictions of electron density in the terrestrial ionosphere resulting from the interaction of the solar wind and the earth's magnetic field. The left image shows the predictions made using a coarse grid. The picture on the right shows computations on a much finer grid, which more closely predicts the fine scale structure observed in reality. Comparison with past events allows confidence in the prediction of future events.
Past events test specific aspects and help define when the computer can be used to make predictions.
This comparison of predictive models to reality is akin to the general scientific principle of validating theory against experimental results. Other techniques indicate the quality of predictive models. Meteorological models can now be applied with enough resolution to make accurate predictions of how weather patterns will evolve. However, since weather is a chaotic process, any uncertainty in the initial conditions can lead to large differences between a prediction and reality. To address this problem, a technique known as ensemble forecasting is used.