Observation Quality Control with a Robust Ensemble Kalman Filter

Soojin Roh, Marc G. Genton, Mikyoung Jun, Istvan Szunyogh, Ibrahim Hoteit

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Current ensemble-based Kalman filter (EnKF) algorithms are not robust to gross observation errors caused by technical or human errors during the data collection process. In this paper, the authors consider two types of gross observational errors, additive statistical outliers and innovation outliers, and introduce a method to make EnKF robust to gross observation errors. Using both a one-dimensional linear system of dynamics and a 40-variable Lorenz model, the performance of the proposed robust ensemble Kalman filter (REnKF) was tested and it was found that the new approach greatly improves the performance of the filter in the presence of gross observation errors and leads to only a modest loss of accuracy with clean, outlier-free, observations.
Original languageEnglish (US)
Pages (from-to)4414-4428
Number of pages15
JournalMonthly Weather Review
Issue number12
StatePublished - Dec 2013


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