| dc.contributor.author | Holm, Håvard Heitlo | |
| dc.contributor.author | Sætra, Martin Lilleeng | |
| dc.contributor.author | Brodtkorb, André R. | |
| dc.date.accessioned | 2021-01-30T16:20:00Z | |
| dc.date.accessioned | 2021-03-08T12:11:11Z | |
| dc.date.available | 2021-01-30T16:20:00Z | |
| dc.date.available | 2021-03-08T12:11:11Z | |
| dc.date.issued | 2020-06-10 | |
| dc.identifier.citation | Holm HH, Sætra ML, Brodtkorb A: Data Assimilation for Ocean Drift Trajectories Using Massive Ensembles and GPUs. In: Klöfkorn R, Keilegavlen E, Radu FA, Fuhrmann J. Finite Volumes for Complex Applications IX - Methods, Theoretical Aspects, Examples, 2020. Springer p. 715-724 | en |
| dc.identifier.isbn | 978-3-030-43651-3 | |
| dc.identifier.isbn | 978-3-030-43650-6 | |
| dc.identifier.issn | 2194-1017 | |
| dc.identifier.issn | 2194-1009 | |
| dc.identifier.uri | https://hdl.handle.net/10642/9916 | |
| dc.description.abstract | 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. | en |
| dc.description.sponsorship | This work is supported by the Research Council of Norway (RCN) through grant number 250935 (GPU Ocean). The computations in this paper were performed on equipment provided by the Experimental Infrastructure for Exploration of Exascale Computing (eX3 ), which is financially supported by the RCN under contract 270053. The source code for the methods and experiments described in this paper is available under an GNU free and open source license released under https://doi.org/10.5281/zenodo.3591850. | en |
| dc.language.iso | en | en |
| dc.publisher | Springer | en |
| dc.relation.ispartofseries | Springer Proceedings in Mathematics & Statistics;volume 323 | |
| dc.rights | Authors whose work is accepted for publication in a non-open access Springer book may deposit their author’s accepted manuscript (AAM) in their institutional or funder repository. | en |
| dc.subject | Particle filters | en |
| dc.subject | Finite volume methods | en |
| dc.subject | Shallow water simulations | en |
| dc.title | Data Assimilation for Ocean Drift Trajectories Using Massive Ensembles and GPUs | en |
| dc.type | Conference object | |
| dc.date.updated | 2021-01-30T16:20:00Z | |
| dc.description.version | acceptedVersion | en |
| dc.identifier.doi | https://doi.org/10.1007/978-3-030-43651-3_68 | |
| dc.identifier.cristin | 1816211 | |
| dc.relation.projectID | Notur/NorStore: NN9550K | |
| dc.relation.projectID | Norges forskningsråd: 250935 | |
| dc.relation.projectID | Norges forskningsråd: 270053 | |
| dc.source.isbn | 978-3-030-43650-6 | |
