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Data Assimilation for Ocean Drift Trajectories Using Massive Ensembles and GPUs

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.

Category

Academic chapter/article/Conference paper

Client

  • Research Council of Norway (RCN) / 270053
  • Research Council of Norway (RCN) / 250935
  • Notur/NorStore / NN9550K

Language

English

Author(s)

Affiliation

  • Norwegian University of Science and Technology
  • SINTEF Digital / Mathematics and Cybernetics
  • OsloMet - Oslo Metropolitan University
  • Norwegian Meteorological Institute (MET Norway)

Year

2020

Publisher

Springer

Book

Finite Volumes for Complex Applications IX - Methods, Theoretical Aspects, Examples

Issue

323

ISBN

978-3-030-43650-6

Page(s)

715 - 724

View this publication at Cristin