TY - JOUR
T1 - Mitigating Observation Perturbation Sampling Errors in the Stochastic EnKF
AU - Hoteit, Ibrahim
AU - Pham, D.-T.
AU - El Gharamti, Mohamad
AU - Luo, X.
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2015/4/6
Y1 - 2015/4/6
N2 - The stochastic ensemble Kalman filter (EnKF) updates its ensemble members with observations perturbed with noise sampled from the distribution of the observational errors. This was shown to introduce noise into the system and may become pronounced when the ensemble size is smaller than the rank of the observational error covariance, which is often the case in real oceanic and atmospheric data assimilation applications. This work introduces an efficient serial scheme to mitigate the impact of observations’ perturbations sampling in the analysis step of the EnKF, which should provide more accurate ensemble estimates of the analysis error covariance matrices. The new scheme is simple to implement within the serial EnKF algorithm, requiring only the approximation of the EnKF sample forecast error covariance matrix by a matrix with one rank less. The new EnKF scheme is implemented and tested with the Lorenz-96 model. Results from numerical experiments are conducted to compare its performance with the EnKF and two standard deterministic EnKFs. This study shows that the new scheme enhances the behavior of the EnKF and may lead to better performance than the deterministic EnKFs even when implemented with relatively small ensembles.
AB - The stochastic ensemble Kalman filter (EnKF) updates its ensemble members with observations perturbed with noise sampled from the distribution of the observational errors. This was shown to introduce noise into the system and may become pronounced when the ensemble size is smaller than the rank of the observational error covariance, which is often the case in real oceanic and atmospheric data assimilation applications. This work introduces an efficient serial scheme to mitigate the impact of observations’ perturbations sampling in the analysis step of the EnKF, which should provide more accurate ensemble estimates of the analysis error covariance matrices. The new scheme is simple to implement within the serial EnKF algorithm, requiring only the approximation of the EnKF sample forecast error covariance matrix by a matrix with one rank less. The new EnKF scheme is implemented and tested with the Lorenz-96 model. Results from numerical experiments are conducted to compare its performance with the EnKF and two standard deterministic EnKFs. This study shows that the new scheme enhances the behavior of the EnKF and may lead to better performance than the deterministic EnKFs even when implemented with relatively small ensembles.
UR - http://hdl.handle.net/10754/565937
UR - http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-14-00088.1
UR - http://www.scopus.com/inward/record.url?scp=84943424519&partnerID=8YFLogxK
U2 - 10.1175/MWR-D-14-00088.1
DO - 10.1175/MWR-D-14-00088.1
M3 - Article
SN - 0027-0644
VL - 143
SP - 2918
EP - 2936
JO - Monthly Weather Review
JF - Monthly Weather Review
IS - 7
ER -