Teacher: Tuomo Kauranne, Technical University of Lappeenranta

Data assimilation is the process of optimally combining measurements or observations with the predictions of a mathematical model of the phenomenon under study. Data assimilation is used in, for example, weather and climate forecasting, where we make an optimal compromise between observations from ground stations, satellite measurements, radiosondes and so on on the one hand, and a weather forecast by an atmospheric model from some past time on the other hand. One of the most important methods of data assimilation is Kalman filtering. This course gives a brief, hands-on introduction to data assimilation with Matlab, using simple chemical reactions as an example.