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Growing‐error correction of ensemble Kalman filter using empirical singular vectors
Author(s) -
Ham YooGeun,
Kang InSik
Publication year - 2010
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.711
Subject(s) - ensemble kalman filter , data assimilation , kalman filter , ensemble forecasting , forecast skill , mean squared prediction error , ensemble average , ensemble learning , mathematics , filter (signal processing) , algorithm , computer science , meteorology , statistics , climatology , extended kalman filter , machine learning , geology , geography , computer vision
Abstract In this study, a new Ensemble Kalman Filter (EnKF) algorithm called EnKF with growing‐error correction (EnKF‐GEC) is developed for minimizing the growing component of the forecast error; for this purpose, prospective observations are assimilated using empirical singular vectors (ESVs). Unlike the Ensemble Kalman Smoother (EnKS) or four‐dimensional EnKF (4DEnKF), the EnKF‐GEC is designed to reduce the analysis error at the last analysis time (errors of initial condition for prediction). By performing assimilation experiments using the CZ‐SPEEDY coupled model within a perfect model framework, it is shown that the analysis errors obtained using the EnKF‐GEC are significantly reduced as compared to those obtained using the conventional EnKF until the last analysis time as well as during the middle of analysis time. This indicates that the new algorithm is beneficial for prediction. Seasonal prediction results show that the prediction skill when initial conditions are generated by the EnKF‐GEC is superior to when initial conditions are generated by the conventional EnKF or EnKS, particularly during the early forecast lead month. For example, correlation skill improvement with 16 ensemble members is about 0.1 for a 3‐month lead forecast. In addition, it is shown that the new EnKF algorithm is more effective for unpredictable regions, where the value of the unstable singular vector is robust. Copyright © 2010 Royal Meteorological Society