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The potential of the ensemble Kalman filter for NWP—a comparison with 4D‐Var
Author(s) -
Lorenc Andrew C.
Publication year - 2003
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 - 10.1256/qj.02.132
Subject(s) - data assimilation , numerical weather prediction , ensemble kalman filter , mesoscale meteorology , meteorology , kalman filter , satellite , environmental science , computer science , north american mesoscale model , range (aeronautics) , remote sensing , global forecast system , climatology , extended kalman filter , geography , artificial intelligence , aerospace engineering , geology , engineering
The ensemble Kalman filter (EnKF) is reviewed for its expected assimilation characteristics and ease of implementation, and compared to the currently more popular four‐dimensional variational assimilation (4D‐Var). The EnKF is attractive when building a new medium‐range ensemble numerical weather prediction (NWP) system. However it is less suitable for NWP systems with uncertainty in a wide range of scales; it may not use high‐resolution satellite data as effectively as 4D‐Var. For limited‐area mesoscale NWP systems a hybrid method is attractive. © Crown copyright, 2003. Royal Meteorological Society