Abstract
We apply the recently proposed hybrid particle-ensemble Kalman filter to assimilate Lagrangian data into a non-linear, high-dimensional quasi-geostrophic ocean model. Effectively the hybrid filter applies a particle filter to the highly nonlinear, low-dimensional Lagrangian instrument variables while applying an ensemble Kalman type update to the high-dimensional Eulerian flow field. We present some initial results from this hybrid filter and compare those to results from a standard ensemble Kalman filter and an ensemble run without assimilation.
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Notes
- 1.
We will use the term drifter going forward to refer to any Lagrangian instrument.
- 2.
We use a modified version of the codes due to Guillaume Roullet. [13].
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Acknowledgments
The authors would like to acknowledge the use of the numerical implementation of the QG model by Guillaume Roullet (see http://stockage.univ-brest.fr/~roullet/codes.html). The authors would like to thank Chris Jones for initially suggesting this collaboration and the Mathematics and Climate Research Network (NSF grant DMS-0940363) for enabling this collaboration. Apte would like to thank the EADS/Airbus Chair in ‘Mathematics of Complex Systems’ at TIFR for partial support for this work. Spiller would like to acknowledge support by NSF grant DMS-1228265 and ONR grant N00014-11-1-0087.
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Slivinski, L., Spiller, E., Apte, A. (2015). A Hybrid Particle-Ensemble Kalman Filter for High Dimensional Lagrangian Data Assimilation. In: Ravela, S., Sandu, A. (eds) Dynamic Data-Driven Environmental Systems Science. DyDESS 2014. Lecture Notes in Computer Science(), vol 8964. Springer, Cham. https://doi.org/10.1007/978-3-319-25138-7_24
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DOI: https://doi.org/10.1007/978-3-319-25138-7_24
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