Adaptive ensemble optimal interpolation for efficient data assimilation in the red sea
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Accepted manuscript
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2023-02-09
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ArticleAuthors
Toye, Habib
Zhan, Peng

Sana, Furrukh

Sanikommu, Siva Reddy

Raboudi, Naila Mohammed Fathi

Hoteit, Ibrahim

KAUST Department
Applied Mathematics and Computational Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Physical Science and Engineering (PSE) Division
Electrical Engineering Program
Earth Science and Engineering Program
Earth Science and Engineering
KAUST Grant Number
REP/1/3268-01-01Date
2021-02-06Embargo End Date
2023-02-09Submitted Date
2020-02-28Permanent link to this record
http://hdl.handle.net/10754/667512
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Show full item recordAbstract
Ensemble optimal interpolation (EnOI) is a variant of the ensemble Kalman filter (EnKF) that operates with a static ensemble to drastically reduce its computational cost. The idea is to use a pre-selected ensemble to parameterize the background covariance matrix, which avoids the costly integration of the ensemble members with the dynamical model during the forecast step of the filtering process. To better represent the pronounced time-varying circulation of the Red Sea, we propose a new adaptive EnOI approach in which the ensemble members are adaptively selected at every assimilation cycle from a large dictionary of ocean states describing the Red Sea variability. We implement and test different schemes to select the ensemble members (i) based on the similarity to the forecast state according to some criteria, or (ii) in term of best representation of the forecast in an ensemble subspace using an Orthogonal Matching Pursuit (OMP) algorithm. The relevance of the schemes is first demonstrated with the Lorenz 63 and Lorenz 96 models. Then results of numerical experiments assimilating real remote sensing data into a high resolution MIT general circulation model (MITgcm) of the Red Sea using the Data Assimilation Research Testbed (DART) system are presented and discussed.Citation
Toye, H., Zhan, P., Sana, F., Sanikommu, S., Raboudi, N., & Hoteit, I. (2021). Adaptive ensemble optimal interpolation for efficient data assimilation in the red sea. Journal of Computational Science, 51, 101317. doi:10.1016/j.jocs.2021.101317Sponsors
This work was funded by the Office of Sponsored Research (OSR) at King Abdullah University of Science and Technology (KAUST), Saudi Arabia under the Virtual Red Sea Initiative (Grant #REP/1/3268-01-01) and the KAUST Center for Marine Environmental Observations (SAKMEO), Saudi Arabia. The research made use of the KAUST supercomputing facilities.Publisher
Elsevier BVJournal
Journal of Computational ScienceAdditional Links
https://linkinghub.elsevier.com/retrieve/pii/S187775032100017Xae974a485f413a2113503eed53cd6c53
10.1016/j.jocs.2021.101317