Loading Icon

Data assimilation

Data assimilation is a mathematical discipline that seeks to optimally combine theory (usually in the form of a numerical model) with observations. There may be a number of different goals sought, for example—to determine the optimal state estimate of a system, to determine initial conditions for a numerical forecast model, to interpolate sparse observation data using (e.g. physical) knowledge of the system being observed, to train numerical model parameters based on observed data. Depending on the goal, different solution methods may be used. Data assimilation is distinguished from other forms of machine learning, image analysis, and statistical methods in that it utilizes a dynamical model of the system being analyzed.

Metrics Summary

Total Publications
Lifetime
6,863
Prior Five Years
2,110
Total Citations
Lifetime
174,990
Prior Five Years
17,062
Total Scholars
Lifetime
8,937
Prior Five Years
7,767

Institutional Rankings

Global (Worldwide)
Show More
National Institutional Rankings

Publications and Citation History

Publications based on Disciplines

Scholars based on Disciplines

Publications based on Fields

Scholars based on Fields

Highly Ranked Scholars™

Lifetime
Prior Five Years

Highly Cited Publications

Lifetime