Hybrid Gain Data Assimilation using Variational Corrections in the Subspace Orthogonal to the Ensemble

Chang, Chih-Chien; Penny, Stephen G.; Yang, Shu-Chih

doi:10.1175/MWR-D-19-0128.1

i

Hybrid Gain Data Assimilation using Variational Corrections in the Subspace Orthogonal to the Ensemble

2020
By Chang, Chih-Chien ; Penny, Stephen G. ; Yang, Shu-Chih

Details

Journal Title:

Monthly Weather Review
Personal Author:

Chang, Chih-Chien ; Penny, Stephen G. ; Yang, Shu-Chih
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; CPO (Climate Program Office) ; ESRL (Earth System Research Laboratory) ; PSL (Physical Sciences Laboratory) ; NESDIS (National Environmental Satellite, Data, and Information Service) ; NWS (National Weather Service) ; OST( Office of Science and Technology) ; CIRES (Cooperative Institute for Research in Environmental Sciences)
Description:

The viability of a parameterless hybrid data assimilation algorithm is investigated. As an alternative to the traditional hybrid covariance scheme, hybrid gain data assimilation (HGDA) was proposed to blend the gain matrix derived from the variational method and the ensemble-based Kalman filter (EnKF). A previously proposed HGDA algorithm uses a two-step process applying the EnKF with a variational update. The algorithm is modified here to limit the variational correction to the subspace orthogonal to the ensemble perturbation subspace without the use of a hybrid weighting parameter, as the optimization of such a parameter is nontrivial. The modified HGDA algorithm is investigated with a quasigeostrophic (QG) model. Results indicate that when the climatological background error covariance matrix B and the observation error covariance R are well estimated, state estimates from the parameterless HGDA are more accurate than the parameter-dependent HGDA. The parameterless HGDA not only has potential advantages over the standard HGDA as an online data assimilation algorithm but can also serve as a valuable diagnostic tool for tuning the B and R matrices. It is also found that in this QG model, the empirically best static B matrix for the stand-alone 3DVAR has high variance at larger spatial scales, which degrades the accuracy of the HGDA systems and may not be the best choice for hybrid methods in general. A comparison of defining the orthogonal subspace globally or locally demonstrates that global orthogonality is more advantageous for stabilizing the hybrid system and maintains large-scale balances.
Keywords:

Atmospheric Sciences Error Covariance Kalman Filter Meteorology Nwp Scheme Error Covariance Kalman Filter
Source:

Monthly Weather Review, 148(6), 2331-2350. https://journals.ametsoc.org/view/journals/mwre/148/6/mwrD190128.xml
DOI:

https://doi.org/10.1175/MWR-D-19-0128.1
Document Type:

Journal Article
Funding:

Grant no. NA16OAR4310140 ; Grant no. NA14NES4320003 ; Grant no. NA18NWS4680048
Place as Subject:

North Atlantic ; Pacific
Rights Information:

CC BY
Compliance:

Submitted
Main Document Checksum:

urn:sha256:2bbdf7829e7f5cfddd3aa5d9190617bf60a7bc7483be59639a0815cee2a256a7
Download URL:

https://repository.library.noaa.gov/view/noaa/28736/noaa_28736_DS1.pdf
File Type:

[PDF - 2.51 MB ]

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