Extended versus ensemble Kalman filtering for land data assimilation

Rolf H. Reichle, Jeffrey P. Walker, Randal D. Koster, Paul R. Houser

Research output: Contribution to journalArticleResearchpeer-review

238 Citations (Scopus)

Abstract

The performance of the extended Kalman filter (EKF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation. In a twin experiment for the southeastern United States synthetic observations of near-surface soil moisture are assimilated once every 3 days, neglecting horizontal error correlations and treating catchments independently. Both filters provide satisfactory estimates of soil moisture. The average actual estimation error in volumetric moisture content of the soil profile is 2.2% for the EKF and 2.2% (or 2.1%; or 2.0%) for the EnKF with 4 (or 10; or 500) ensemble members. Expected error covariances of both filters generally differ from actual estimation errors. Nevertheless, nonlinearities in soil processes are treated adequately by both filters. In the application presented herein the EKF and the EnKF with four ensemble members are equally accurate at comparable computational cost. Because of its flexibility and its performance in this study, the EnKF is a promising approach for soil moisture initialization problems.

Original languageEnglish
Pages (from-to)728-740
Number of pages13
JournalJournal of Hydrometeorology
Volume3
Issue number6
DOIs
Publication statusPublished - 1 Dec 2002
Externally publishedYes

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