Publication Date:
2022-05-26
Description:
Author Posting. © American Meteorological Society, 2015. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Monthly Weather Review 143 (2015): 195–211, doi:10.1175/MWR-D-14-00051.1.
Description:
Lagrangian measurements from passive ocean instruments provide a useful source of data for estimating and forecasting the ocean’s state (velocity field, salinity field, etc.). However, trajectories from these instruments are often highly nonlinear, leading to difficulties with widely used data assimilation algorithms such as the ensemble Kalman filter (EnKF). Additionally, the velocity field is often modeled as a high-dimensional variable, which precludes the use of more accurate methods such as the particle filter (PF). Here, a hybrid particle–ensemble Kalman filter is developed that applies the EnKF update to the potentially high-dimensional velocity variables, and the PF update to the relatively low-dimensional, highly nonlinear drifter position variable. This algorithm is tested with twin experiments on the linear shallow water equations. In experiments with infrequent observations, the hybrid filter consistently outperformed the EnKF, both by better capturing the Bayesian posterior and by better tracking the truth.
Description:
The work of Apte benefited from the support of the AIRBUS Group Corporate Foundation Chair in Mathematics of Complex Systems established in ICTS-TIFR. Spiller would like to acknowledge support by NSF Grant DMS-1228265 and ONR Grant N00014-11-1-0087. Sandstede gratefully acknowledges support by the NSF through Grant DMS-0907904. Slivinski was supported by the NSF through Grants DMS-0907904 and DMS-1148284.
Description:
2015-07-01
Keywords:
Bayesian methods
;
Filtering techniques
;
Kalman filters
;
Statistical techniques
;
Data assimilation
Repository Name:
Woods Hole Open Access Server
Type:
Article
Format:
application/pdf
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