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We commonly refer to state-estimation theory in geosciences as dataassimilation. This term encompasses the entire sequence of operations that,starting from the observations of a system, and from additional statistical anddynamical information (such as a dynamical evolution model), provides anestimate of its state. Data assimilation is standard practice in numericalweather prediction, but its application is becoming widespread in many otherareas of climate, atmosphere, ocean and environment modeling; in allcircumstances where one intends to estimate the state of a large dynamicalsystem based on limited information. While the complexity of data assimilation,and of the methods thereof, stands on its interdisciplinary nature acrossstatistics, dynamical systems and numerical optimization, when applied togeosciences an additional difficulty arises by the continually increasingsophistication of the environmental models. Thus, in spite of data assimilationbeing nowadays ubiquitous in geosciences, it has so far remained a topic mostlyreserved to experts. We aim this overview article at geoscientists with abackground in mathematical and physical modeling, who are interested in therapid development of data assimilation and its growing domains of applicationin environmental science, but so far have not delved into its conceptual andmethodological complexities.

Data Assimilation in the Geosciences - An overview on methods, issues and perspectives