TY - JOUR
T1 - A maximum likelihood ensemble filter via a modified cholesky decomposition for non-gaussian data assimilation
AU - Nino-Ruiz, Elias David
AU - Mancilla-Herrera, Alfonso
AU - Lopez-Restrepo, Santiago
AU - Quintero-Montoya, Olga
PY - 2020/2/1
Y1 - 2020/2/1
N2 - This paper proposes an efficient and practical implementation of the Maximum Likelihood Ensemble Filter via a Modified Cholesky decomposition (MLEF-MC). The method works as follows: via an ensemble of model realizations, a well-conditioned and full-rank square-root approximation of the background error covariance matrix is obtained. This square-root approximation serves as a control space onto which analysis increments can be computed. These are calculated via Line-Search (LS) optimization. We theoretically prove the convergence of the MLEF-MC. Experimental simulations were performed using an Atmospheric General Circulation Model (AT-GCM) and a highly nonlinear observation operator. The results reveal that the proposed method can obtain posterior error estimates within reasonable accuracies in terms of ℓ − 2 error norms. Furthermore, our analysis estimates are similar to those of the MLEF with large ensemble sizes and full observational networks.
AB - This paper proposes an efficient and practical implementation of the Maximum Likelihood Ensemble Filter via a Modified Cholesky decomposition (MLEF-MC). The method works as follows: via an ensemble of model realizations, a well-conditioned and full-rank square-root approximation of the background error covariance matrix is obtained. This square-root approximation serves as a control space onto which analysis increments can be computed. These are calculated via Line-Search (LS) optimization. We theoretically prove the convergence of the MLEF-MC. Experimental simulations were performed using an Atmospheric General Circulation Model (AT-GCM) and a highly nonlinear observation operator. The results reveal that the proposed method can obtain posterior error estimates within reasonable accuracies in terms of ℓ − 2 error norms. Furthermore, our analysis estimates are similar to those of the MLEF with large ensemble sizes and full observational networks.
KW - EnKF
KW - Ensemble-based data assimilation
KW - Line-search optimization
KW - MLEF
KW - Modified cholesky decomposition
UR - http://www.scopus.com/inward/record.url?scp=85079194847&partnerID=8YFLogxK
U2 - 10.3390/s20030877
DO - 10.3390/s20030877
M3 - Article
C2 - 32041372
AN - SCOPUS:85079194847
SN - 1424-8220
VL - 20
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 3
M1 - 877
ER -