Data assimilation in the geosciences
An overview of methods, issues, and perspectives
Bibliographic Data
| ID | 12929711 |
|---|---|
| Authors | Alberto Carrassi (0000-0003-0722-5600, Nansen Environmental and Remote Sensing Center, corresponding author), Marc Bocquet (0000-0003-2675-0347, École nationale des ponts et chaussées), Laurent Bertino (0000-0002-1220-7207, Nansen Environmental and Remote Sensing Center), Geir Evensen (0000-0002-2458-6152, International Research Institute of Stavanger) |
| Year | 2018 |
| Volume | 9 |
| Issue | 5 |
| Publication date | 2018-07-09 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Wiley Interdisciplinary Reviews Climate Change (JOURNAL) |
| Journal identifiers | ISSN: 1757-7780 • E-ISSN: 1757-7799 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/wcc.535 |
| OpenAlex | W2752514830 |
| Language | EN |
| Citations received | 4 |
| References cited | 286 |
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. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models
Assimilation (phonology · Data assimilation · Data science · Dynamical systems theory · Geography · Management science · Meteorology · Operations research · Sophistication · Atmospheric and Environmental Gas Dynamics · Climate variability and models · Computer Science · Engineering · Mathematics · Meteorological Phenomena and Simulations
| Unique citing works | 4 |
|---|---|
| Citations per year | 0,67 |
| Citation span | 2020 - 2026 (7) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 4 |