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

TL;DR

This paper provides an overview of data assimilation in geosciences, discussing its methods, challenges, and future perspectives, aimed at scientists interested in its rapid development and applications.

Contribution

It offers a comprehensive overview tailored for geoscientists, explaining the conceptual and methodological aspects of data assimilation and its expanding role in environmental modeling.

Findings

Data assimilation is crucial in many geoscience applications.

The complexity of methods arises from interdisciplinary nature.

Environmental model sophistication increases challenges in data assimilation.

Abstract

We commonly refer to state-estimation theory in geosciences as data assimilation. 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. Data assimilation 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 data assimilation, 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 data assimilation 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 data assimilation and its growing domains of application in environmental science, but so far have not delved into its conceptual and methodological complexities.