Data Assimilation for Ocean Drift Trajectories Using Massive Ensembles and GPUs
Chapter
Accepted version
Permanent lenke
https://hdl.handle.net/11250/3107753Utgivelsesdato
2020Metadata
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Originalversjon
Finite Volumes for Complex Applications IX - Methods, Theoretical Aspects, Examples. FVCA 2020. Springer Proceedings in Mathematics & Statistics, vol 323. 2020, 715-724. 10.1007/978-3-030-43651-3_68Sammendrag
In this work, we perform fully nonlinear data assimilation of ocean drift trajectories using multiple GPUs. We use an ensemble of up to 10000 members and the sequential importance resampling algorithm to assimilate observations of drift trajectories into the underlying shallow-water simulation model. Our results show an improved drift trajectory forecast using data assimilation for a complex and realistic simulation scenario, and the implementation exhibits good weak and strong scaling.