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

DOI10.1016/j.jcpx.2020.100053arXiv1910.01031MaRDI QIDQ6186228

Martin Lilleeng Sætra, Peter Jan van Leeuwen, Unnamed Author

Publication date: 9 January 2024

Published in: Journal of Computational Physics: X (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.01031

zbMATH Keywords

particle filtersdata assimilationfinite-volume methodGPU computingshallow-water simulationdrift trajectory forecasting

Mathematics Subject Classification ID

Basic methods in fluid mechanics (76Mxx) Incompressible inviscid fluids (76Bxx) Probabilistic methods, stochastic differential equations (65Cxx)

Related Items (1)

Data Assimilation for Ocean Drift Trajectories Using Massive Ensembles and GPUs

Cites Work

• Data assimilation using a GPU accelerated path integral Monte Carlo approach
• Modified particle filter methods for assimilating Lagrangian data into a point-vortex model
• Efficient shallow water simulations on GPUs: implementation, visualization, verification, and validation
• Sequential Monte Carlo Methods for Dynamic Systems
• How to Solve Systems of Conservation Laws Numerically Using the Graphics Processor as a High-Performance Computational Engine

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