Kalman–Bucy filter

From Glossary of Meteorology
(Or, simply, Kalman filter.) A four-dimensional data assimilation method that provides an estimate of the model state by evolving explicitly the error covariance of the state estimate.

Variants of the Kalman filter algorithm are now being applied to atmospheric data assimilation problems. The filter estimate is based on all data observed up to and including the current time. Generalizations of the Kalman filter exist for continuum dynamics, for nonlinear stochastic systems (e.g., extended or ensemble Kalman filters), for systems that have different types of noise, for unknown noise statistics, and for observations beyond the current time (Kalman smoothers).
Kalman, R. E. 1960. A new approach to linear filtering and prediction problems. Trans. ASME, Ser. D, J. Basic Eng. 82. 35–45.
Cohn, S. E. 1997. An introduction to estimation theory. J. Meteor. Soc. Japan. 75. 257–288.

Copyright 2024 American Meteorological Society (AMS). For permission to reuse any portion of this work, please contact permissions@ametsoc.org. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S.Copyright Act (17 USC § 108) does not require AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, require written permission or a license from AMS. Additional details are provided in the AMS Copyright Policy statement.