Data Assimilation for Ocean Drift Trajectories Using Massive Ensembles and GPUs

Holm, Håvard Heitlo; Sætra, Martin Lilleeng; Brodtkorb, André R.

doi:10.1007/978-3-030-43651-3_68

Cited by 6 publications

(3 citation statements)

References 12 publications

(15 reference statements)

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“…To apply P 1/2 next, we can once again evaluate the contribution from S independently for each observation under the same sparsity assumption. (See also Holm (2020)).…”

Section: Sparse Observationsmentioning

confidence: 98%

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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“…To apply P 1/2 next, we can once again evaluate the contribution from S independently for each observation under the same sparsity assumption. (See also Holm (2020)).…”

Section: Sparse Observationsmentioning

confidence: 98%

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

Beiser¹,

Holm²,

Eidsvik³

2023

Preprint

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“…These parameters are therefore global parameters, but since they are scalars they do not contribute to any change in the local correlation structures. Related discussions on how to utilise local covariance structures and sparse observations for efficient implementations of the IEWPF can be found in Holm (2020).…”

Section: Thus Xmentioning

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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“…A higher‐order dynamical model for

{\boldsymbol{\alpha}}_t

would require many more components in the state vector, and this would substantially increase the cost of smoothing. Modeling ocean drifter observations at their natural resolution is very demanding, and one current approach uses nonlinear methods (particle filters) and high‐performance computing (Holm et al, 2020).…”

Section: High Resolution In Timementioning

confidence: 99%

The scope of the Kalman filter for spatio‐temporal applications in environmental science

et al. 2022

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The Kalman filter is a workhorse of dynamical modeling. But there are challenges when using the Kalman filter in environmental science: the complexity of environmental processes, the complicated and irregular nature of many environmental datasets, and the scale of environmental datasets, which may comprise many thousands of observations per time-step. We show how these challenges can be met within the Kalman filter, identifying some situations which are relatively easy to handle, such as datasets which are high-resolution in time, and some which are hard, like areal observations on small contiguous polygons. Overall, we conclude that many applications in environmental science are within the scope of the Kalman filter, or its generalizations.

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Data Assimilation for Ocean Drift Trajectories Using Massive Ensembles and GPUs

Cited by 6 publications

References 12 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

The scope of the Kalman filter for spatio‐temporal applications in environmental science

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