Chapter 13: Data Assimilation
Abstract
Data assimilation is a powerful methodology for incorporating data into a mathematical model in a way that is consistent with physical processes. It is applied widely in numerical weather prediction and is increasingly useful for the reconstruction of climate states. In this chapter we present a Bayesian approach to data assimilation, discuss the Kalman filter algorithm in a simple scalar setting, and outline the Ensemble Kalman filter, which relies on stochastic simulation.
Fields of Study & Subjects
Meteorological Phenomena and Simulations
Soil Geostatistics and Mapping
Reservoir Engineering and Simulation Methods
Publication details
- Journal / source
- Society for Industrial and Applied Mathematics eBooks
- Publisher
- Society for Industrial and Applied Mathematics
- Pages
- 203–217
- Publication date
- 17 June 2026
- Language
- EN
- Document type
- Book chapter