A Hybrid Ensemble Adjustment Kalman Filter based High-resolution Data Assimilation System for the Red Sea: Implementation and Evaluation
Type
ArticleKAUST Department
Applied Mathematics and Computational Science ProgramComputer, 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
2020-09-18Embargo End Date
2021-08-25Submitted Date
2020-03-11Permanent link to this record
http://hdl.handle.net/10754/664829
Metadata
Show full item recordAbstract
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.3894Publisher
WileyDOI
10.1002/qj.3894Additional Links
https://onlinelibrary.wiley.com/doi/abs/10.1002/qj.3894ae974a485f413a2113503eed53cd6c53
10.1002/qj.3894