Open AccessControlling Noise in Ensemble Data Assimilation Schemes
Author(s) -
M. Peña,
Zoltán Tóth,
Meng Wei
Publication year - 2010
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/2009mwr2854.1
Subject(s) - data assimilation , covariance , computer science , noise (video) , divergence (linguistics) , ensemble kalman filter , ensemble forecasting , algorithm , covariance matrix , statistics , data mining , kalman filter , machine learning , mathematics , meteorology , artificial intelligence , extended kalman filter , linguistics , physics , philosophy , image (mathematics)
A variety of ad hoc procedures have been developed to prevent filter divergence in ensemble-based data assimilation schemes. These procedures are necessary to reduce the impacts of sampling errors in the background error covariance matrix derived from a limited-size ensemble. The procedures amount to the introduction of additional noise into the assimilation process, possibly reducing the accuracy of the resulting analyses. The effects of this noise on analysis and forecast performance are investigated in a perfect model scenario. Alternative schemes aimed at controlling the unintended injection of noise are proposed and compared. Improved analysis and forecast accuracy is observed in schemes with minimal alteration to the evolving ensemble-based covariance structure.