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There has been great progress in data assimilation within atmospheric and oceanographic sciences during the last couple of decades. In data assimilation, one aims at merging the information from observations into a numerical model, typically of a geophysical system. A typical example where data assimilation is needed is in weather forecasting. Here, the atmospheric models must take into account the most recent observations of variables such as temperature and atmospheric pressure for better forecasting of the weather in the next time period. A major challenge for these models is that they contain very large numbers of variables.

The progress in data assimilation is because of both increased computational power and the introduction of techniques that are capable of handling large amounts of data and more severe nonlinearities. The aim of this paper is to focus on one of these techniques, the ensemble …