A Brief Tutorial on the Ensemble Kalman Filter

2009/01/23 by Jan Mandel, Mandel, Jan · 1 citation
Earth and Planetary Sciences · Environmental Science · Physics and Astronomy · #Atmospheric and Environmental Gas Dynamics #Atmospheric and Oceanic Physics (physics.ao-ph) #Climate variability and models #FOS: Physical sciences #Meteorological Phenomena and Simulations #physics.ao-ph

paper · pdf · doi:10.48550/arxiv.0901.3725

7 pages

arxiv created 2009/01/23 · openalex publication_date 2009/01/23 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models. The EnKF originated as a version of the Kalman filter for large problems (essentially, the covariance matrix is replaced by the sample covariance), and it is now an important data assimilation component of ensemble forecasting. EnKF is related to the particle filter (in this context, a particle is the same thing as an ensemble member) but the EnKF makes the assumption that all probability distributions involved are Gaussian. This article briefly describes the derivation and practical implementation of the basic version of EnKF, and reviews several extensions.

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