Data Assimilation for Ocean Drift Trajectories Using Massive Ensembles and GPUs

DOI10.1007/978-3-030-43651-3_68zbMath1454.65089OpenAlexW3034861894MaRDI QIDQ5117502

Håvard H. Holm, André R. Brodtkorb, Martin L. Sætra

Publication date: 25 August 2020

Published in: Finite Volumes for Complex Applications IX - Methods, Theoretical Aspects, Examples (Search for Journal in Brave)

Full work available at URL: https://hdl.handle.net/10642/9916

zbMATH Keywords

particle filtersdata assimilationfinite-volume methodsGPUsshallow-water simulations

Mathematics Subject Classification ID

Other programming paradigms (object-oriented, sequential, concurrent, automatic, etc.) (68N19) Hydrology, hydrography, oceanography (86A05) Water waves, gravity waves; dispersion and scattering, nonlinear interaction (76B15) Signal detection and filtering (aspects of stochastic processes) (60G35) Inference from stochastic processes (62M99) Hyperbolic conservation laws (35L65) Parallel numerical computation (65Y05) Numerical algorithms for specific classes of architectures (65Y10) Finite volume methods for initial value and initial-boundary value problems involving PDEs (65M08) Finite volume methods for boundary value problems involving PDEs (65N08)

Uses Software

• Unnamed Item
• ROMS
• OpenDrift

Cites Work

• Well-balanced schemes for the shallow water equations with Coriolis forces
• Data Assimilation
• Massively parallel implicit equal-weights particle filter for ocean drift trajectory forecasting

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