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Ensemble singular vectors as additive inflation in the Local Ensemble Transform Kalman Filter (LETKF) framework with a global NWP model
Author(s) -
Shin Seoleun,
Kang JiSun,
Yang ShuChih,
Kalnay Eugenia
Publication year - 2018
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.3429
Subject(s) - data assimilation , ensemble kalman filter , kalman filter , covariance , computer science , algorithm , subspace topology , ensemble learning , ensemble forecasting , extended kalman filter , mathematics , meteorology , artificial intelligence , statistics , geography
We test an ensemble data assimilation system using the four‐dimensional Local Ensemble Transform Kalman Filter (4D‐LETKF) for a global numerical weather prediction (NWP) model with unstructured grids on the cubed‐sphere. It is challenging to selectively represent structures of dynamically growing errors in background states under system uncertainties such as sampling and model errors. We compute Ensemble Singular Vectors (ESVs) in an attempt to capture fast‐growing errors on the subspace spanned by ensemble perturbations, and use them as additive inflation to enlarge the covariance in the area where errors are flow‐dependently growing. The performance of the 4D‐LETKF system with ESVs is evaluated in real data assimilation, as well as Observing System Simulation Experiments (OSSEs). We find that leading ESVs help to capture fast‐growing errors effectively, especially when model errors are present, and that the use of ESVs as additive inflation significantly improves the performance of the 4D‐LETKF.