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Journal Articles Wiley Interdisciplinary Reviews: Climate Change Year : 2018

Data assimilation in the geosciences: An overview of methods, issues, and perspectives

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Abstract

We commonly refer to state estimation theory in geosciences as data assimilation (DA). This term encompasses the entire sequence of operations that, starting from the observations of a system, and from additional statistical and dynamical infor- mation (such as a dynamical evolution model), provides an estimate of its state. DA is standard practice in numerical weather prediction, but its application is becoming widespread in many other areas of climate, atmosphere, ocean, and envi- ronment modeling; in all circumstances where one intends to estimate the state of a large dynamical system based on limited information. While the complexity of DA, and of the methods thereof, stands on its interdisciplinary nature across statis- tics, dynamical systems, and numerical optimization, when applied to geosciences, an additional difficulty arises by the continually increasing sophistication of the environmental models. Thus, in spite of DA being nowadays ubiquitous in geos- ciences, it has so far remained a topic mostly reserved to experts. We aim this over- view article at geoscientists with a background in mathematical and physical modeling, who are interested in the rapid development of DA and its growing domains of application in environmental science, but so far have not delved into its conceptual and methodological complexities.

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hal-02905891 , version 1 (23-07-2020)

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Alberto Carrassi, Marc Bocquet, Laurent Bertino, Geir Evensen. Data assimilation in the geosciences: An overview of methods, issues, and perspectives. Wiley Interdisciplinary Reviews: Climate Change, 2018, 9 (5), pp.e535. ⟨10.1002/wcc.535⟩. ⟨hal-02905891⟩
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