A hybrid particle–ensemble Kalman filter for Lagrangian data assimilation
MetadataShow full item record
KeywordBayesian methods; Filtering techniques; Kalman filters; Statistical techniques; Data assimilation
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.
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.
Showing items related by title, author, creator and subject.
Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter Rastetter, Edward B.; Williams, Mathew; Griffin, Kevin L.; Kwiatkowski, Bonnie L.; Tomasky, Gabrielle; Potosnak, Mark J.; Stoy, Paul C.; Shaver, Gaius R.; Stieglitz, Marc; Hobbie, John E.; Kling, George W. (Ecological Society of America, 2010-07)Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal ...
Observing system simulation experiments with ensemble Kalman filters in Nantucket Sound, Massachusetts Xue, Pengfei; Chen, Changsheng; Beardsley, Robert C.; Limeburner, Richard (American Geophysical Union, 2011-01-20)Observing system simulation experiments (OSSEs) were performed for Nantucket Sound, Massachusetts, as a pilot study for the design of optimal monitoring networks in the coastal ocean. Experiments were carried out using the ...
Application and comparison of Kalman filters for coastal ocean problems : an experiment with FVCOM Chen, Changsheng; Malanotte-Rizzoli, Paola; Wei, Jun; Beardsley, Robert C.; Lai, Zhigang; Xue, Pengfei; Lyu, Sangjun; Xu, Qichun; Qi, Jianhua; Cowles, Geoffrey W. (American Geophysical Union, 2009-05-13)Twin experiments were made to compare the reduced rank Kalman filter (RRKF), ensemble Kalman filter (EnKF), and ensemble square-root Kalman filter (EnSKF) for coastal ocean problems in three idealized regimes: a flat bottom ...