Definition of Term: Calibration

The inputs to a simulator that are the correct or best values to use to predict a particular real-world system are very often uncertain.

Example: An atmospheric dispersion simulator models the way that pollutants are spread through the atmosphere when released. Its outputs concern how much of a pollutant reaches various places or susceptible targets. In order to use the simluator to predict the effects of a specific pollutant release, we need to specif the appropriate input values for the location of the release, the quantity released, the wind speed and direction and so on. All of these may in practice be uncertain.

If we can make observations of the real-world system, then we can use these to learn about those uncertain inputs. A crude way to do this is to adjust the inputs so as to make the simulator predict as closely as possible the actual observation points. This is widely done by model users, and is called calibration. The best fitting values of the uncertain parameters are then used to make predictions of the system.

Example: Observing the amount of pollutant reaching some specific points, we can then calibrate the model and use the best fitting input values to predict the amounts reaching other points and hence assess the consequences for key susceptible targets.

In MUCM, we take the broader view that such observations allow us to learn about the uncertain inputs, but not to eliminate uncertainty. We therefore consider calibration to be the process of using observations of the real system to modify (and usually to reduce) the uncertainty about specific inputs.

See also data assimilation.