OpenDrift v1.0: a generic framework for trajectory modelling

Dagestad, Knut‐Frode; Röhrs, Johannes; Breivik, Øyvind; Ådlandsvik, Bjørn

doi:10.5194/gmd-11-1405-2018

Cited by 192 publications

(144 citation statements)

References 41 publications

(37 reference statements)

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“…A number of tools are available to track virtual particles, with diverse characteristics, strengths and limitations, including Ariane (Blanke and Raynaud, 1997), TRACMASS (Döös et al, 2017), CMS (Paris et al, 2013) and OpenDrift (Dagestad et al, 2018). An extensive list and description of La-P. Delandmeter and E. van Sebille: The Parcels v2.0 Lagrangian framework grangian analysis tools is provided in van Sebille et al (2018).…”

Section: Introductionmentioning

confidence: 99%

The Parcels v2.0 Lagrangian framework: new field interpolation schemes

2019

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Abstract. With the increasing number of data produced by numerical ocean models, so increases the need for efficient tools to analyse these data. One of these tools is Lagrangian ocean analysis, where a set of virtual particles is released and their dynamics are integrated in time based on fields defining the ocean state, including the hydrodynamics and biogeochemistry if available. This popular methodology needs to adapt to the large variety of models producing these fields at different formats. This is precisely the aim of Parcels, a Lagrangian ocean analysis framework designed to combine (1) a wide flexibility to model particles of different natures and (2) an efficient implementation in accordance with modern computing infrastructure. In the new Parcels v2.0, we implement a set of interpolation schemes to read various types of discretized fields, from rectilinear to curvilinear grids in the horizontal direction, from z to s levels in the vertical direction and using grid staggering with the Arakawa A, B and C grids. In particular, we develop a new interpolation scheme for a three-dimensional curvilinear C grid and analyse its properties. Parcels v2.0 capabilities, including a suite of meta-field objects, are then illustrated in a brief study of the distribution of floating microplastic in the northwest European continental shelf and its sensitivity to various physical processes.

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Section: Introductionmentioning

confidence: 99%

The Parcels v2.0 Lagrangian framework: new field interpolation schemes

2019

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“…The individual‐based model (IBM) simulates development and transport of cod eggs and larvae based on earlier studies of larval cod (Kristiansen, Lough, Werner, Broughton, & Buckley, 2009;Kristiansen, Stock, Drinkwater, & Curchitser, 2014;Kristiansen, Vikebø, Sundby, Huse, & Fiksen, 2009). The IBM is integrated as a module in the open source Lagrangian particle tracking framework OpenDrift (github.com/opendrift; Dagestad, Röhrs, Breivik, & Ådlandsvik, 2018;Kvile et al., 2018), and the code for the cod eggs and larvae module is available on http://github.com/trondkr/KINO-ROMS/tree/master/Romagnoni-2019-OpenDrift. To simulate transport with ocean currents and temperature‐dependent development, the IBM was coupled offline to a reanalysis of the regional ocean circulation model ROMS (Shchepetkin & McWilliams, 2005) configured for ocean regions covering the Nordic Seas (including the North Sea) and parts of the Arctic Ocean, with 4 km horizontal resolution, 32 vertical layers and output stored daily (Lien, Gusdal, Albretsen, & Melsom, 2013).…”

Section: Methodsmentioning

confidence: 99%

Influence of larval transport and temperature on recruitment dynamics of North Sea cod (Gadus morhua) across spatial scales of observation

Romagnoni

Kvile

Dagestad

et al. 2020

Fisheries Oceanography

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The survival of fish eggs and larvae, and therefore recruitment success, can be critically affected by transport in ocean currents. Combining a model of early‐life stage dispersal with statistical stock–recruitment models, we investigated the role of larval transport for recruitment variability across spatial scales for the population complex of North Sea cod (Gadus morhua). By using a coupled physical–biological model, we estimated the egg and larval transport over a 44‐year period. The oceanographic component of the model, capable of capturing the interannual variability of temperature and ocean current patterns, was coupled to the biological component, an individual‐based model (IBM) that simulated the cod eggs and larvae development and mortality. This study proposes a novel method to account for larval transport and success in stock–recruitment models: weighting the spawning stock biomass by retention rate and, in the case of multiple populations, their connectivity. Our method provides an estimate of the stock biomass contributing to recruitment and the effect of larval transport on recruitment variability. Our results indicate an effect, albeit small, in some populations at the local level. Including transport anomaly as an environmental covariate in traditional stock–recruitment models in turn captures recruitment variability at larger scales. Our study aims to quantify the role of larval transport for recruitment across spatial scales, and disentangle the roles of temperature and larval transport on effective connectivity between populations, thus informing about the potential impacts of climate change on the cod population structure in the North Sea.

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“…Typically, all N e particles are initialized with weight w 0 i = 1/N e , as they are sampled independently from the pdf of the initial conditions, p(ψ 0 ). Each particle is then simulated independently according to (1) until observation time t n . By applying (4) directly with (2) as the prior density, and by considering the marginal probability as a normalization constant, the posterior distribution is expressed as…”

Section: Standard Particle Filtermentioning

confidence: 99%

“…One technique used for overcoming the curse of dimensionality is to sample the states ψ n i from a proposal density, q, with an appropriate compensation in the weights. First, (1) shows that the pdf of the state at time t n is related to that of the previous time by the Markovian property p(ψ n ) = ∫ p(ψ n |ψ n−1 )p(ψ n−1 ) dψ n−1 ≈ 1 N e N e i=1 p(ψ n |ψ n−1 i ),…”

Section: The Implicit Equal-weights Particle Filtermentioning

confidence: 99%

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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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OpenDrift v1.0: a generic framework for trajectory modelling

Cited by 192 publications

References 41 publications

The Parcels v2.0 Lagrangian framework: new field interpolation schemes

The Parcels v2.0 Lagrangian framework: new field interpolation schemes

Influence of larval transport and temperature on recruitment dynamics of North Sea cod (Gadus morhua) across spatial scales of observation

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

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