Coastal ocean forecasting on the GPU using a two-dimensional finite-volume scheme

Brodtkorb, André R.; Holm, Håvard Heitlo

doi:10.1080/16000870.2021.1876341

Cited by 6 publications

(9 citation statements)

References 35 publications

(39 reference statements)

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“…The model error for

hu

and

hv

is then inferred according to geostrophic balance to ensure physical feasibility. Further details about this model are available in Brodtkorb and Holm (2021) and Holm et al (2020).…”

Section: Comparison For Drift Trajectory Forecasting In a Simplified ...mentioning

confidence: 99%

Comparison of ensemble‐based data assimilation methods for sparse oceanographic data

Beiser,

Holm,

Eidsvik

2024

Quart J Royal Meteoro Soc

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Probabilistic forecasts in oceanographic applications, such as drift trajectory forecasts for search‐and‐rescue operations, face challenges due to high‐dimensional complex models and sparse spatial observations. We discuss localisation strategies for assimilating sparse point observations and compare the implicit equal‐weights particle filter and a localised version of the ensemble‐transform Kalman filter. First, we verify these methods thoroughly against the analytic Kalman filter solution for a linear advection diffusion model. We then use a non‐linear simplified ocean model to do state estimation and drift prediction. The methods are rigorously compared using a wide range of metrics and skill scores. Our findings indicate that both methods succeed in approximating the Kalman filter reference for linear models of moderate dimensions, even for small ensemble sizes. However, in high‐dimensional settings with a non‐linear model, we discover that the outcomes are significantly influenced by the dependence of the ensemble Kalman filter on relaxation and the particle filter's sensitivity to the chosen model error covariance structure. Upon proper relaxation and localisation parametrisation, the ensemble Kalman filter version outperforms the particle filter in our experiments.This article is protected by copyright. All rights reserved.

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“…The model error for

hu

and

hv

is then inferred according to geostrophic balance to ensure physical feasibility. Further details about this model are available in Brodtkorb and Holm (2021) and Holm et al (2020).…”

Section: Comparison For Drift Trajectory Forecasting In a Simplified ...mentioning

confidence: 99%

Comparison of ensemble‐based data assimilation methods for sparse oceanographic data

Beiser,

Holm,

Eidsvik

2024

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

show abstract

“…The model error for hu and hv is then inferred according to geostrophic balance to ensure physical feasibility. Further details about this model are available in Brodtkorb and Holm (2021) and Holm et al (2020).…”

Section: Simplified Ocean Modelmentioning

confidence: 99%

Comparison of Ensemble-Based Data Assimilation Methods for Sparse Oceanographic Data

Beiser¹,

Holm²,

Eidsvik³

2023

Preprint

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For oceanographic applications, probabilistic forecasts typically have to deal with i) high-dimensional complex models, and ii) very sparse spatial observations. In search-and-rescue operations at sea, for instance, the short-term predictions of drift trajectories are essential to efficiently define search areas, but in-situ buoy observations provide only very sparse point measurements, while the mission is ongoing. Statistically optimal forecasts, including consistent uncertainty statements, rely on Bayesian methods for data assimilation to make the best out of both the complex mathematical modeling and the sparse spatial data.To identify suitable approaches for data assimilation in this context, we discuss localisation strategies and compare two state-of-the-art ensemble-based methods for applications with spatially sparse observations. The first method is a version of the ensemble-transform Kalman filter, where we tailor a localisation scheme for sparse point data. The second method is the implicit equal-weights particle filter which has recently been tested for related oceanographic applications.First, we study a linear spatio-temporal model for contaminant advection and diffusion, where the analytical Kalman filter provides a reference. Next, we consider a simplified ocean model for sea currents, where we conduct state estimation and predict drift. Insight is gained by comparing ensemble-based methods on a number of skill scores including prediction bias and accuracy, distribution coverage, rank histograms, spatial connectivity and drift trajectory forecasts.

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“…Values for K j,k and L j,k are obtained by evaluating the integrals in ( 4) by a recursive sum starting from zero at the boundary. Note that the recursive sums for K and L mostly cancel in the reconstruction and are reduced to local operations [1,Sect. 3.1].…”

Section: Well-balanced Schemementioning

confidence: 99%

“…Finally, we study the consequence of using different flux terms and slope limiters when using the CDKLM scheme for simulating a real-world oceanographic scenario. Here, we utilize the full GPUaccelerated framework from [1], in which land mask, bathymetry, initial and boundary conditions, domain orientation, and wind forcing are taken from the operational model NorKyst800 [9], with 800 m horizontal resolution. Figure 5 shows water velocity at 23 h for a few selected values of ϕ and θ for CDKLM.…”

Section: Numerical Experimentsmentioning

confidence: 99%

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Reducing Numerical Artifacts by Sacrificing Well-Balance for Rotating Shallow-Water Flow

Holm,

Beiser

2023

Springer Proceedings in Mathematics &Amp; Statistics

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We consider the problem of rotational shallow-water flow for which non-trivial rotating steady-state solutions are of great importance. In particular, we investigate a high-resolution central-upwind scheme that is well-balanced for a subset of these stationary solutions and show that the well-balanced design is the source of numerical artifacts when applied to more general problems. We propose an alternative flux evaluation that sacrifices the well-balanced property and demonstrate that this gives qualitatively better results for relevant test cases and real-world oceanographic simulations.

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Coastal ocean forecasting on the GPU using a two-dimensional finite-volume scheme

Cited by 6 publications

References 35 publications

Comparison of ensemble‐based data assimilation methods for sparse oceanographic data

Comparison of ensemble‐based data assimilation methods for sparse oceanographic data

Comparison of Ensemble-Based Data Assimilation Methods for Sparse Oceanographic Data

Reducing Numerical Artifacts by Sacrificing Well-Balance for Rotating Shallow-Water Flow

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