A Survey of Implicit Particle Filters for Data Assimilation

Chorin, Alexandre J.; Morzfeld, Matthias; Tu, Xuemin

doi:10.1007/978-1-4614-7789-1_3

Cited by 15 publications

(14 citation statements)

References 53 publications

(104 reference statements)

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“…The IEWPF combines the implicit sampling framework of Chorin et al . () with the equal‐weights idea from Ades and Van Leeuwen (). By the implicit construction, no parameter tuning is required.…”

Section: Introductionmentioning

confidence: 99%

“…The implicit equal-weights particle filter (IEWPF), introduced by Zhu et al (2016), similarly prevents filter degeneracy by constructing the proposal distribution so that the weights are uniform. The IEWPF combines the implicit sampling framework of Chorin et al (2013) with the equal-weights idea from Ades and Van Leeuwen (2013). By the implicit construction, no parameter tuning is required.…”

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confidence: 99%

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A revised implicit equal‐weights particle filter

Skauvold

Eidsvik

Leeuwen

et al. 2019

Quart J Royal Meteoro Soc

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Particle filters are fully nonlinear data assimilation methods and as such are highly relevant. While the standard particle filter degenerates for high‐dimensional systems, recent developments have opened the way for new particle filters that can be used in such systems. The implicit equal‐weights particle filter (IEWPF) is an efficient approach that avoids filter degeneracy because it gives equal particle weights by construction. The method uses implicit sampling, whereby auxiliary vectors drawn from a proposal distribution undergo a transformation before they are added to each particle. In the original formulation of the IEWPF, the proposal distribution has a gap, causing all but one particle to have an inaccessible region in state space. We show that this leads to a systematic bias in the predictions and we modify the proposal distribution to eliminate the gap. We achieved this by using a two‐stage proposal method, where a single variance parameter is tuned to obtain adequate statistical coverage properties of the predictive distribution. We discuss the properties of the implicit mapping from an auxiliary random vector to the state vector, keeping in mind the aim of avoiding particle resampling. The revised filter is tested on linear and weakly nonlinear dynamical models in low‐dimensional and moderately high‐dimensional settings, demonstrating the success of the new methodology in removing the bias.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

A revised implicit equal‐weights particle filter

Skauvold

Eidsvik

Leeuwen

et al. 2019

Quart J Royal Meteoro Soc

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“…Fully nonlinear ensemble data assimilation methods, such as particle filters (van Leeuwen, 2009) can also be implemented. Examples of such methods would include the SIR filter (Gordon et al, 1993), the auxiliary particle filter (Pitt and Shephard, 1999), the implicit particle filter (Chorin et al, 2010) or the equivalent weights particle filter (van Leeuwen, 2010). Parameter estimation and sensitivity analyses could be implemented using these methods by including the parameters as prognostic variables of the model in an augmented state approach.…”

Section: Pros and Cons Of This Strategymentioning

confidence: 99%

A simple method for integrating a complex model into an ensemble data assimilation system using MPI

Browne

Wilson

2015

Environmental Modelling & Software

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a b s t r a c tThis paper details a strategy for modifying the source code of a complex model so that the model may be used in a data assimilation context, and gives the standards for implementing a data assimilation code to use such a model. The strategy relies on keeping the model separate from any data assimilation code, and coupling the two through the use of Message Passing Interface (MPI) functionality. This strategy limits the changes necessary to the model and as such is rapid to program, at the expense of ultimate performance. The implementation technique is applied in different models with state dimension up to .2.7 Â 10 8 The overheads added by using this implementation strategy in a coupled ocean-atmosphere climate model are shown to be an order of magnitude smaller than the addition of correlated stochastic random errors necessary for some nonlinear data assimilation techniques.

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“…The size of the observation vector becomes N y = 2N D . One challenge with the above observation is the unobserved value of the sea-surface level η, as it in general is not negligible compared to H eq , and therefore introduces a bias in (29). To compensate for this, we use the best available estimate for η, namely the simulated η for each individual particle, and define the innovation related to drifter d for particle i as…”

Section: Synthetic Truth and Observationsmentioning

confidence: 99%

“…), but without adding the stochastic model error. The observation also contains the location of each drifter, which is used to look up the relevant parts of the particle state vectors according to (29) and (30).…”

Section: Observations and Innovationsmentioning

confidence: 99%

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

Holm

Sætra

Leeuwen

2020

Journal of Computational Physics: X

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Forecasting 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 a recent state-of-the-art 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 analyse 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. * Corresponding author: havard.heitlo.holm@sintef.no 1 arXiv:1910.01031v1 [stat.CO] 2 Oct 2019 which is an offline trajectory model. It reads the ocean current forecasts produced by the ocean circulation models, and uses these to predict drift trajectories. Although OpenDrift is computationally efficient, the ocean circulation models still require access to supercomputers. This paper explores the option of using a state-of-the-art particle filter method applied to a simplified ocean model for efficient drift trajectory forecasting. The aim is to build a data-assimilation system that can run efficiently on commodity-level desktop computers, and also be extendable to supercomputers. We achieve this by using a simplified ocean model and a data-assimilation method that both are able to take advantage of massively parallel accelerator hardware, such as the graphical processing unit (GPU). This work is not intended as a substitute of current operational systems, but as a complementary approach, in which the predicted currents may even be updated with in-situ observations, e.g., during ongoing search and rescue operations. Furthermore, by enabling research models to run on individual desktop and laptop computers, researchers are able to do more rapid prototyping. At the same time, this work will contribute to more efficient simulations also on supercomputers, since all algorithms may be extended to run on multiple GPUs and compute nodes.The paper is organized as follows: We start by reviewing related work relevant for Lagrangian data assimilation with accelerated pa...

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A Survey of Implicit Particle Filters for Data Assimilation

Cited by 15 publications

References 53 publications

A revised implicit equal‐weights particle filter

A revised implicit equal‐weights particle filter

A simple method for integrating a complex model into an ensemble data assimilation system using MPI

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

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