HR: 16:35h
AN: A24B-02 INVITED     [Abstracts]
TI: 4D-Var or Ensemble Kalman Filter
AU: * Kalnay, E
EM: ekalnay@atmos.umd.edu
AF: University of Maryland, 3431 CSS, College Park, MD 20742-2425, United States
AU: Li, H
EM: lhhelen@atmos.umd.edu
AF: University of Maryland, 3431 CSS, College Park, MD 20742-2425, United States
AU: Yang, S
EM: scyang@atmos.umd.edu
AF: University of Maryland, 3431 CSS, College Park, MD 20742-2425, United States
AU: Miyoshi, T
EM: miyoshi@naps.kishou.go.jp
AF: Japan Meteorological Society, 1-3-4 Otemachi, Chiyoda-ku, Tokyo, 100-8122, Japan
AU: Ballabrera, J
AF: Marine Science Institute, CSIC, Barcelona, Spain
AB: We consider the relative advantages of two advanced data assimilation systems, 4D-Var and ensemble Kalman filter (EnKF), currently in use or considered for operational implementation. We explore the impact of tuning assimilation parameters such as the assimilation window length and background error covariance in 4D-Var, the variance inflation in EnKF, and the effect of model errors and reduced observation coverage in both systems. For short assimilation windows EnKF gives more accurate analyses. Both systems reach similar levels of accuracy if long windows are used for 4D-Var, and for infrequent observations, when ensemble perturbations grow nonlinearly and become non-Gaussian, 4D-Var attains lower errors than EnKF. Results obtained with variations of EnKF using operational models and both simulated and real observations are reviewed. A table summarizes the pros and cons of the two methods.
DE: 3225 Numerical approximations and analysis (4260)
DE: 3245 Probabilistic forecasting (3238)
DE: 3275 Uncertainty quantification (1873)
DE: 3315 Data assimilation
DE: 3339 Ocean/atmosphere interactions (0312, 4504)
SC: Atmospheric Sciences [A]
MN: 2007 Joint Assembly