Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing
- 335 Downloads
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.
KeywordsRed Sea Data assimilation Seasonal variability Ensemble Kalman filter Ensemble optimal interpolation
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.
- Cipollini P et al (2010) The role of altimetry in coastal observing systems. Proceedings of OceanObs 9:181–191Google Scholar
- Houtekamer PL, Zhang F (2016) Review of the ensemble Kalman filter for atmospheric data assimilation. Mon Weather Rev. doi: 10.1175/MWR-D-15-0440.1
- Nerger L, Hiller W, Schroter J (2005) PDAF—the parallel data assimilation framework: experiences with Kalman filtering use of high performance computing in meteorology:63–83 doi: 10.1142/9789812701831_0006
- Oke PR, Allen JS, Miller RN, Egbert GD, Kosro PM (2002) Assimilation of surface velocity data into a primitive equation coastal ocean model J Geophys Res-Oceans 107 doi:Artn 3122 10.1029/2000jc000511
- Scharroo R, Leuliette EW, Lillibridge JL, Byrne D, Naeije MC, Mitchum GT (2013) RADS: Consistent multi-mission products. In, 2013Google Scholar
- Sofianos SS, Johns WE (2002) An oceanic general circulation model (OGCM) investigation of the Red Sea circulation, 1. Exchange between the Red Sea and the Indian Ocean J Geophys Res-Oceans 107Google Scholar
- Sofianos SS, Johns WE (2003) An oceanic general circulation model (OGCM) investigation of the Red Sea circulation: 2. Three-dimensional circulation in the Red Sea J Geophys Res-Oceans 108Google Scholar
- Vignudelli S, Kostianoy AG, Cipollini P, Benveniste J (2011) Coastal altimetry. Springer Science & Business MediaGoogle Scholar
- Zhan P, Subramanian AC, Yao FC, Kartadikaria AR, Guo D, Hoteit I (2016) The eddy kinetic energy budget in the Red Sea J Geophys Res-Oceans Accepted Author Manuscript. doi: 10.1002/2015JC011589