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2003•Quarterly Journal of the Royal Meteorological SocietyRequires access

The potential of the ensemble Kalman filter for NWP—a comparison with 4D‐Var

Andrew C. Lorenc

Open publisher page 768 citations

Abstract

Abstract 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

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What this paper is about

Abstract 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

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Available abstract

Abstract 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

Key concepts: Data assimilation, Numerical weather prediction, Ensemble Kalman filter, Mesoscale meteorology, Meteorology, Kalman filter, Satellite, Environmental science

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