Lagrangian simulation and tracking of the mesoscale eddies contaminated by Fukushima-derived radionuclides

Prants, S. V.; Budyansky, M. V.; Uleysky, M. Yu.

doi:10.5194/os-13-453-2017

Cited by 21 publications

(6 citation statements)

References 33 publications

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“…Prants et al [13] developed a Lagrangian methodology to simulate and track the origin and evolution of water masses within mesoscale ocean eddies using trajectories of synthetic particles advected by altimetry velocities. They applied this technique to identify the Tohoku and Hokkaido eddies contaminated by Fukushima-derived radionuclides after the 2011 disaster, and compared the modeled eddy distributions against in situ measurements.…”

Section: Traditional Methodsmentioning

confidence: 99%

A Metadata-Enhanced Deep Learning Method for Sea Surface Height and Mesoscale Eddy Prediction

Zhu,

Song,

Qiu

et al. 2024

Remote Sensing

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Predicting the mesoscale eddies in the ocean is crucial for advancing our understanding of the ocean and climate systems. Establishing spatio-temporal correlation among input data is a significant challenge in mesoscale eddy prediction tasks, especially for deep learning techniques. In this paper, we first present a deep learning solution based on a video prediction model to capture the spatio-temporal correlation and predict future sea surface height data accurately. To enhance the performance of the model, we introduced a novel metadata embedding module that utilizes neural networks to fuse remote sensing metadata with input data, resulting in increased accuracy. To the best of our knowledge, our model outperforms the state-of-the-art method for predicting sea level anomalies. Consequently, a mesoscale eddy detection algorithm will be applied to the predicted sea surface height data to generate mesoscale eddies in future. The proposed solution achieves competitive results, indicating that the prediction error for the eddy center position is 5.6 km for a 3-day prediction and 13.6 km for a 7-day prediction.

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Section: Traditional Methodsmentioning

confidence: 99%

A Metadata-Enhanced Deep Learning Method for Sea Surface Height and Mesoscale Eddy Prediction

Zhu,

Song,

Qiu

et al. 2024

Remote Sensing

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“…The presence of TBs of different kinds strongly affects the dispersion of radioactive tracers in the ocean. The Lagrangian particle tracking has been actively used to simulate the propagation of radionuclides in the ocean, especially after the tsunami on 11 March 2011, followed by heavy damage to the Fukushima Nuclear Power Plant (e.g., [92][93][94][95][96]). In particular, it has been shown that contaminated water has been trapped by mesoscale eddies during their formation processes near the Fukushima location after the accident [96].…”

Section: Radioactive Tracersmentioning

confidence: 99%

Transport Barriers in Geophysical Flows: A Review

Prants

2023

Symmetry

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In the Lagrangian approach, the transport processes in the ocean and atmosphere are studied by tracking water or air parcels, each of which may carry different tracers. In the ocean, they are salt, nutrients, heat, and particulate matter, such as plankters, oil, radionuclides, and microplastics. In the atmosphere, the tracers are water vapor, ozone, and various chemicals. The observation and simulation reveal highly complex patterns of advection of tracers in turbulent-like geophysical flows. Transport barriers are material surfaces across which the transport is minimal. They can be classified into elliptic, parabolic, and hyperbolic barriers. Different diagnostics in detecting transport barriers and the analysis of their role in the dynamics of oceanic and atmospheric flows are reviewed. We discuss the mathematical tools, borrowed from dynamical systems theory, for detecting transport barriers in simple kinematic and dynamic models of vortical and jet-like flows. We show how the ideas and methods, developed for simple model flows, can be successfully applied for studying the role of barriers in oceanic and atmospheric flows. Special attention is placed on the significance of transport barriers in important practical issues: anthropogenic and natural pollution, advection of plankton, cross-shelf exchange, and propagation of upwelling fronts in coastal zones.

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“…There are probably still limitations such as the excess eddy merging and prematurely terminating trajectories that need to be considered (Faghmous et al, 2015). The Lagrangian based eddy detection algorithms have also been suggested its effectivity, specially for mesoscale eddy evolution and its properties (Vortmeyer-Kley et al, 2016;Prants et al, 2017). Prants et al (2017) proposed the Lagrangian methodology to distinguish whether the origin and history of water masses in mesoscale eddies came from the main currents in Kuroshio-Oyashio confluence zone, showing a good qualitative correspondence, compared with in situ measurements.…”

Section: Proposal For More Diagnosticsmentioning

confidence: 99%

“…The Lagrangian based eddy detection algorithms have also been suggested its effectivity, specially for mesoscale eddy evolution and its properties (Vortmeyer-Kley et al, 2016;Prants et al, 2017). Prants et al (2017) proposed the Lagrangian methodology to distinguish whether the origin and history of water masses in mesoscale eddies came from the main currents in Kuroshio-Oyashio confluence zone, showing a good qualitative correspondence, compared with in situ measurements. Vortmeyer-Kley et al (2016) proposed a vorticity based heuristic Euler-Lagrangian descriptor utilizing the idea of Lagrangian coherent structures, showing closer lifetime estimation to analytical results and its robustness with respect to certain types of noise in eddy detection from observation.…”

Section: Proposal For More Diagnosticsmentioning

confidence: 99%

The Identification and Prediction of Mesoscale Eddy Variation via Memory in Memory With Scheduled Sampling for Sea Level Anomaly

Nian

Cai

et al. 2021

Front. Mar. Sci.

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Ocean mesoscale eddies are ubiquitous in world ocean and account for 90% oceanic kinetic energy, which dominate the upper ocean flow field. Accurately predicting the variation of ocean mesoscale eddies is the key to understand the oceanic flow field and circulation system. In this article, we propose to make an initial attempt to explore spatio-temporal predictability of mesoscale eddies, employing deep learning architecture, which primarily establishes Memory In Memory (MIM) for sea level anomaly (SLA) prediction, combined with the existing mesoscale eddy detection. Oriented to the western Pacific ocean (125°−137.5°E and 15°−27.5°N), we quantitatively investigate the historic daily SLA variability at a 0.25° spatial resolution from 2000 to 2018, derived by satellite altimetry. We develop the enhanced MIM prediction strategies, equipped with Gated Recurrent Unit (GRU) and spatial attention module, in a scheduled sampling manner, which overcomes the gradient vanishing and complements to strengthen spatio-temporal features for long-term dependencies. At the early stage, the real value SLA input guides the model training process for initialization, while the scheduled sampling intentionally feeds the newly predicted value, to resolve the distribution inconsistency of inference. It has been demonstrated in our experiment results that our proposed prediction scheme outperformed the state-of-art approaches for SLA time series, with MAPE, RMSE of the 14-day prediction duration, respectively, 5.1%, 0.023 m on average, even up to 4.6%, 0.018 m for the effective sub-regions, compared to 19.8%, 0.086 m in ConvLSTM and 8.3%, 0.040 m in original MIM, which greatly facilitated the mesoscale eddy prediction. This proposed scheme will be beneficial to understand of the underlying dynamical mechanism behind the predictability of mesoscale eddies in the future, and help the deployment of ARGO, glider, AUV and other observational platforms.

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Lagrangian simulation and tracking of the mesoscale eddies contaminated by Fukushima-derived radionuclides

Cited by 21 publications

References 33 publications

A Metadata-Enhanced Deep Learning Method for Sea Surface Height and Mesoscale Eddy Prediction

A Metadata-Enhanced Deep Learning Method for Sea Surface Height and Mesoscale Eddy Prediction

Transport Barriers in Geophysical Flows: A Review

The Identification and Prediction of Mesoscale Eddy Variation via Memory in Memory With Scheduled Sampling for Sea Level Anomaly

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