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    A Hybrid Ensemble Adjustment Kalman Filter based High-resolution Data Assimilation System for the Red Sea: Implementation and Evaluation

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    Type
    Article
    Authors
    Toye, Habib cc
    Sanikommu, Siva Reddy cc
    Raboudi, Naila Mohammed Fathi cc
    Hoteit, Ibrahim cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Earth Fluid Modeling and Prediction Group
    Earth Science and Engineering
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2020-09-18
    Embargo End Date
    2021-08-25
    Submitted Date
    2020-03-11
    Permanent link to this record
    http://hdl.handle.net/10754/664829
    
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    Abstract
    A new Hybrid ensemble data assimilation system is implemented with a Massachusetts Institute of Technology general circulation model (MITgcm) of the Red Sea. The system is based on the Data Assimilation Research Testbed (DART) and combines a time-varying ensemble generated by the Ensemble Adjustment Kalman filter (EAKF) with a pre-selected quasi-static (monthly varying) ensemble as used in an Ensemble Optimal Interpolation (EnOI) scheme. The goal is to develop an efficient system that enhances the state estimate and model forecasting skill in the Red Sea with reduced computational load compared to the EAKF. Observations of satellite sea surface temperature (SST), altimeter sea surface height (SSH), and in situ temperature and salinity profiles are assimilated to evaluate the new system. The performance of the Hybrid scheme (here after Hybrid-EAKF) is assessed with respect to the EnOI and the EAKF results. The comparisons are based on the daily averaged forecasts against satellite SST and SSH measurements and independent in situ temperature and salinity profiles. Hybrid-EAKF yields significant improvements in terms of ocean state estimates compared to both EnOI and EAKF, in particular mitigating for dynamical imbalances that affects EnOI. Hybrid-EAKF improves the estimation of SST and SSH root-mean-square-differences by up to 20% compared to EAKF. High-resolution mesoscale eddy features, which dominate the Red Sea circulation, are further better represented in Hybrid-EAKF. Important reduction, by about 75%, in computational cost is also achieved with the Hybrid-EAKF system compared to the EAKF. These significant improvements were obtained with the Hybrid-EAKF after accounting for uncertainties in the atmospheric forcing and internal model physics in the time-varying ensemble.
    Citation
    Toye, H., Sanikommu, S., Raboudi, N. F., & Hoteit, I. (2020). A Hybrid Ensemble Adjustment Kalman Filter based High-resolution Data Assimilation System for the Red Sea: Implementation and Evaluation. Quarterly Journal of the Royal Meteorological Society. doi:10.1002/qj.3894
    Publisher
    Wiley
    Journal
    Quarterly Journal of the Royal Meteorological Society
    DOI
    10.1002/qj.3894
    Additional Links
    https://onlinelibrary.wiley.com/doi/abs/10.1002/qj.3894
    ae974a485f413a2113503eed53cd6c53
    10.1002/qj.3894
    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 Science and Engineering (CEMSE) Division

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