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

By Alberto Carrassi, Marc Bocquet, Laurent Bertino, Geir Evensen. WIREs Climate Change, 2017

752 citations recorded by the source corpus.

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 information (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 environment 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 statistics, 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 geosciences, it has so far remained a topic mostly reserved to experts. We aim this overview 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.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/a8d76b7beb8db7a52e09e01e72f3b0670d357149

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Citations in the corpus

Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere (2023, methodology): “It is the result of reanalysis, a process that uses data-assimilation (Carrassi et al., 2018; Kalnay, 2003) to combine modern numerical weather forecasting models with historical observational records to produce an estimate of the historical ocean-atmosphere system.”
Improving (2018, neutral): “Data assimilation (DA) integrates observations with dynamical models to optimally estimate the state of the climate sys-tem (Carrassi et al., 2018; Penny and Hamill, 2017)…”
A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation (2018, background): “(1)110 and (2) is representative of a broad range of DA problems, as described in seminal papers such111 as Ghil and Malanotte-Rizzoli (1991), and still relevant today as referenced by Houtekamer and112 Zhang (2016) and Carrassi et al. (2018).”