NTRS - NASA Technical Reports Server

Back to Results
Hybrid Data Assimilation without Ensemble FilteringThe Global Modeling and Assimilation Office is preparing to upgrade its three-dimensional variational system to a hybrid approach in which the ensemble is generated using a square-root ensemble Kalman filter (EnKF) and the variational problem is solved using the Grid-point Statistical Interpolation system. As in most EnKF applications, we found it necessary to employ a combination of multiplicative and additive inflations, to compensate for sampling and modeling errors, respectively and, to maintain the small-member ensemble solution close to the variational solution; we also found it necessary to re-center the members of the ensemble about the variational analysis. During tuning of the filter we have found re-centering and additive inflation to play a considerably larger role than expected, particularly in a dual-resolution context when the variational analysis is ran at larger resolution than the ensemble. This led us to consider a hybrid strategy in which the members of the ensemble are generated by simply converting the variational analysis to the resolution of the ensemble and applying additive inflation, thus bypassing the EnKF. Comparisons of this, so-called, filter-free hybrid procedure with an EnKF-based hybrid procedure and a control non-hybrid, traditional, scheme show both hybrid strategies to provide equally significant improvement over the control; more interestingly, the filter-free procedure was found to give qualitatively similar results to the EnKF-based procedure.
Document ID
Document Type
Preprint (Draft being sent to journal)
Todling, Ricardo (NASA Goddard Space Flight Center Greenbelt, MD United States)
Akkraoui, Amal El (Science Systems and Applications, Inc. Lanham, MD, United States)
Date Acquired
August 29, 2014
Publication Date
January 1, 2014
Subject Category
Geosciences (General)
Report/Patent Number
Funding Number(s)
Distribution Limits
Public Use Permitted.
Kalman Filter
Hybrid-Variational Analysis
Data Assimilation

Available Downloads

NameType 20140011180.pdf STI