Regional and basin scale applications of ensemble adjustment Kalman filter and 4D-Var ocean data assimilation systems

• Published in: Progress in oceanography Vol. 189; p. 102450
• Main Authors: Moore, Andrew, Zavala-Garay, Javier, Arango, Hernan G., Edwards, Christopher A., Anderson, Jeffrey, Hoar, Tim
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2020
• Subjects: 4D-Var; basins; California Current; covariance; Ensemble Kalman filter; Indian Ocean; monsoon season; oceans; Pacific Ocean; Indian Ocean; Pacific Ocean; California Current; 4D-Var; Indian Ocean; Ensemble Kalman filter
• ISSN: 0079-6611; 1873-4472; 1873-4472
• DOI: 10.1016/j.pocean.2020.102450

•Data assimilated in ROMS from real observing systems using an EAKF and 4D-Var.•OSSEs and OSEs were performed for the California Current system and Indian Ocean.•Both systems behave as designed in OSSEs, and their performance is comparable.•OSEs reveal challenges of assimilating real observations using both approaches. The performance of two common approaches to data assimilation, an Ensemble Adjustment Kalman Filter (EAKF) and a 4-dimensional variational (4D-Var) method, is quantified in a popular community ocean model, the Regional Ocean Modeling Systems (ROMS). Two distinct circulation environments are considered: the California Current System (CCS), which is an eastern boundary upwelling regime, and the Indian Ocean (IO) characterized by an equatorial waveguide subject to the energetic seasonal reversals of the Indian and Asian Monsoons. In the case of the CCS, experiments were performed using synthetic observations, so-called Observing System Simulation Experiments (OSSEs). An extensive suite of CCS OSSEs were conducted to explore the performance of both data assimilation approaches to system configuration. For the EAKF, this includes the method for generating the seed ensemble, ensemble size, localization scales, and the length of the assimilation window. In the case of 4D-Var, the influence of assimilation window length, and the formulation of the background error covariance were explored. The performance of the EAKF was found to be influenced most by the size of the ensemble and by the method used to generate the initial seed ensemble where centering of the ensemble was found to yield improvement. For 4D-Var, the assimilation window length is by far the most critical factor, with an increase in system performance as the window length is extended. In general, the EAKF and 4D-Var systems converge to similar solutions over time, which are independent of the starting point. The EAKF employs a First-Guess at Appropriate Time (FGAT) strategy, and some experiments indicate that short FGAT windows can be problematic due to the introduction of frequent initialization shocks. While the EAKF generally out-performs 4D-Var in the OSSEs, analysis of the innovations from the two systems through time indicates that they track each other closely. Additional Observing System Experiments (OSEs) were performed in the CCS and IO configurations of ROMS using real ocean observations. In this case, the comparison of the EAKF and 4D-Var state estimates with independent observations indicates that the EAKF and 4D-Var state estimates diverge over time, although the 4D-Var estimates are somewhat better by some measures. The relative performance of the EAKF and 4D-Var systems is similar across the wide-range of circulation regimes that characterize the CCS and IO, suggesting that the results presented here are a robust indicator of expected performance in other regions of the world ocean.