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    Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

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    ocean_dynamics_draft_revised_R2.pdf
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    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Toye, Habib
    Zhan, Peng cc
    Gopalakrishnan, Ganesh
    Kartadikaria, Aditya R. cc
    Huang, Huang cc
    Knio, Omar cc
    Hoteit, Ibrahim cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Beacon Development Company
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Earth Fluid Modeling and Prediction Group
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2017-05-26
    Online Publication Date
    2017-05-26
    Print Publication Date
    2017-07
    Permanent link to this record
    http://hdl.handle.net/10754/624028
    
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    Abstract
    We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.
    Citation
    Toye H, Zhan P, Gopalakrishnan G, Kartadikaria AR, Huang H, et al. (2017) Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing. Ocean Dynamics. Available: http://dx.doi.org/10.1007/s10236-017-1064-1.
    Sponsors
    This research work was supported by King Abdullah University of Science and Technology (KAUST), Saudi Arabia, and the Saudi ARAMCO Marine Environmental Research Center at KAUST (SAMERCK). The research made use of the resources of the Super computing Laboratory and computer clusters at KAUST.
    Publisher
    Springer Nature
    Journal
    Ocean Dynamics
    DOI
    10.1007/s10236-017-1064-1
    Additional Links
    https://link.springer.com/article/10.1007%2Fs10236-017-1064-1
    ae974a485f413a2113503eed53cd6c53
    10.1007/s10236-017-1064-1
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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