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

Holm, H. H., Sætra, M. L. and Van Leeuwen, P. J. (2020) Massively parallel implicit equal-weights particle filter for ocean drift trajectory forecasting. Journal of Computational Physics: X, 6. 100053. ISSN 2590-0552 doi: 10.1016/j.jcpx.2020.100053

Abstract/Summary

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 analyze 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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/89904
Identification Number/DOI	10.1016/j.jcpx.2020.100053
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Elsevier
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