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Journal Article
Gopalakrishnan, G, Hoteit I, Cornuelle BD, Rudnick DL.  2019.  Comparison of 4DVAR and EnKF state estimates and forecasts in the Gulf of Mexico. Quarterly Journal of the Royal Meteorological Society. 145:1354-1376.   10.1002/qj.3493   AbstractWebsite

An experiment is conducted to compare four-dimensional variational (4DVAR) and ensemble Kalman filter (EnKF) assimilation systems and their predictability in the Gulf of Mexico (GoM) using the Massachusetts Institute of Technology general circulation model (MITgcm). The quality of the ocean-state estimates, forecasts, and the contribution of ensemble prediction are evaluated. The MITgcm-Estimating the Circulation and Climate of the Ocean (ECCO) 4DVAR (MITgcm-ECCO) and the MITgcm-Data Assimilation Research Testbed (DART) EnKF (MITgcm-DART) systems were used to compute two-month hindcasts (March-April, 2010) by assimilating satellite-derived along-track sea-surface height (SSH) and gridded sea-surface temperature (SST) observations. The estimates from both methods at the end of the hindcast period were then used to initialize forecasts for two months (May-June, 2010). This period was selected because a loop current (LC) eddy (Eddy Franklin: Eddy-F) detachment event occurred at the end of May 2010, immediately after the Deepwater Horizon (DwH) oil spill. Despite some differences between the setups, both systems produce analyses and forecasts of comparable quality and both solutions significantly outperformed model persistence. A reference forecast initialized from the 1/12 degrees Hybrid Coordinate Ocean Model (HYCOM)/NCODA global analysis also performed well. The EnKF experiments for sensitivity to filter parameters showed enhanced predictability when using more ensemble members and stronger covariance localization, but not for larger inflation. The EnKF experiments varying the number of assimilation cycles showed enhanced short-term (long-term) predictability with fewer (more) assimilation cycles. Additional hindcast and forecast experiments at other times of significant LC evolution showed mixed performance of both systems, which depends strongly on the background state of the GoM circulation. The present work demonstrates a practical application of both assimilation methods for the GoM and compares them in a limited number of realizations. The overall conclusion showing improved short-term (long-term) predictability for EnKF (4DVAR) carries an important caveat that the results from this study are specific to a few 4DVAR and EnKF LC eddy separation experiments in the GoM and cannot be generalized to conclude the relative performance of both methods, especially in other applications. However, some of the concepts and methods should carry over to other applications.

Hoteit, I, Hoar T, Gopalakrishnan G, Collins N, Anderson J, Cornuelle B, Kohl A, Heimbach P.  2013.  A MITgcm/DART ensemble analysis and prediction system with application to the Gulf of Mexico. Dynamics of Atmospheres and Oceans. 63:1-23.   10.1016/j.dynatmoce.2013.03.002   AbstractWebsite

This paper describes the development of an advanced ensemble Kalman filter (EnKF)-based ocean data assimilation system for prediction of the evolution of the loop current in the Gulf of Mexico (GoM). The system integrates the Data Assimilation Research Testbed (DART) assimilation package with the Massachusetts Institute of Technology ocean general circulation model (MITgcm). The MITgcm/DART system supports the assimilation of a wide range of ocean observations and uses an ensemble approach to solve the nonlinear assimilation problems. The GoM prediction system was implemented with an eddy-resolving 1/10th degree configuration of the MITgcm. Assimilation experiments were performed over a 6-month period between May and October during a strong loop current event in 1999. The model was sequentially constrained with weekly satellite sea surface temperature and altimetry data. Experiments results suggest that the ensemble-based assimilation system shows a high predictive skill in the GoM, with estimated ensemble spread mainly concentrated around the front of the loop current. Further analysis of the system estimates demonstrates that the ensemble assimilation accurately reproduces the observed features without imposing any negative impact on the dynamical balance of the system. Results from sensitivity experiments with respect to the ensemble filter parameters are also presented and discussed. (C) 2013 Elsevier B.V. All rights reserved.

Edwards, CA, Moore AM, Hoteit I, Cornuelle BD.  2015.  Regional ocean data assimilation. Annual Review of Marine Science, Vol 7. 7:21-42.   10.1146/annurev-marine-010814-015821   AbstractWebsite

This article reviews the past 15 years of developments in regional ocean data assimilation. A variety of scientific, management, and safety-related objectives motivate marine scientists to characterize many ocean environments, including coastal regions. As in weather prediction, the accurate representation of physical, chemical, and/or biological properties in the ocean is challenging. Models and observations alone provide imperfect representations of the ocean state, but together they can offer improved estimates. Variational and sequential methods are among the most widely used in regional ocean systems, and there have been exciting recent advances in ensemble and four-dimensional variational approaches. These techniques are increasingly being tested and adapted for biogeochemical applications.