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Massively parallel implicit equal-weights particle filter for ocean drift trajectory forecasting

Abstract

Forecasting of ocean drift trajectories are important for many applications, including search and rescue operations, oil spill cleanup and iceberg risk mitigation. In an operational setting, forecasts of drift trajectories are produced based on computationally demanding forecasts of three-dimensional ocean currents. Herein, we investigate a complementary approach for shorter time scales by using the recently proposed two-stage implicit equal-weights particle filter applied to a simplified ocean model. To achieve this, we present a new algorithmic design for a data-assimilation system in which all components – including the model, model errors, and particle filter – take advantage of massively parallel compute architectures, such as graphical processing units. Faster computations can enable in-situ and ad-hoc model runs for emergency management, and larger ensembles for better uncertainty quantification. Using a challenging test case with near-realistic chaotic instabilities, we run data-assimilation experiments based on synthetic observations from drifting and moored buoys, and analyse the trajectory forecasts for the drifters. Our results show that even sparse drifter observations are sufficient to significantly improve short-term drift forecasts up to twelve hours. With equidistant moored buoys observing only 0.1% of the state space, the ensemble gives an accurate description of the true state after data assimilation followed by a high-quality probabilistic forecast.

Category

Academic article

Client

  • Research Council of Norway (RCN) / 250935 (GPU Ocean)
  • 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)
  • University of Reading
  • Colorado State University

Year

2020

Published in

Journal of Computational Physics: X

ISSN

2590-0552

Publisher

Elsevier

Volume

6

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