Abstract:

The Maximum Likelihood Ensemble Filter (MLEF) has been developed at the
Colorado State University. It is a reduced-rank method based on the
minimization of the cost function. This implies that the solution of
the minimization is the conditional mode of the Probability Density
Function. The ensembles are essentially used to estimate the curvature
of the cost function, which is directly related to the inverse Hessian
of the control problem. Thus, the analysis error covariance is obtained
from the minimization algorithm as an updated inverse Hessian, rather
than as a sample covariance. The Hessian preconditioning is in
principle similar to the transformation of the Ensemble Transform
Kalman Filter.

In THORPEX related research, the MLEF algorithm is installed on the NCEP IBM SP computer, and used with the GFS T62L28 model and the SSI data assimilation system for assimilation of real observations. In the preliminary phase, the MLEF is assimilating surface pressure, temperature and winds. In the near future all operational observations will be assimilated, including satellite and radar. Preliminary results of the MLEF with GFS and SSI will be shown and discussed, as well as some NCEP-specific details of the algorithm.

In THORPEX related research, the MLEF algorithm is installed on the NCEP IBM SP computer, and used with the GFS T62L28 model and the SSI data assimilation system for assimilation of real observations. In the preliminary phase, the MLEF is assimilating surface pressure, temperature and winds. In the near future all operational observations will be assimilated, including satellite and radar. Preliminary results of the MLEF with GFS and SSI will be shown and discussed, as well as some NCEP-specific details of the algorithm.

Milija Zupanski and
Arif Albayrak