Evolution and Structure of the Kuroshio Extension Front in Spring 2019

Wang, Jiahao; Mao, Kefeng; Chen, Xi; Zhu, Kelan

doi:10.3390/jmse8070502

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…Regarding width prediction, accuracy (1 km) typically exceeded 60%, while accuracy (3 km) consistently went beyond 80%. Remarkably, the highest accuracy was observed when the sound source directed southward, aligning with the KEF's near parallelism to the latitude line and its northward inclination (Wang et al, 2020). This study strategically positioned the sound source cloth at the critical KEF location, significantly enhancing model interpretability.…”

Section: Comparison Of Three-dimensional Prediction Resultsmentioning

confidence: 82%

Machine learning–based feature prediction of convergence zones in ocean front environments

Xu,

Zhang,

Wang

2024

Front. Mar. Sci.

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The convergence zone holds significant importance in deep-sea underwater acoustic propagation, playing a pivotal role in remote underwater acoustic detection and communication. Despite the adaptability and predictive power of machine learning, its practical application in predicting the convergence zone remains largely unexplored. This study aimed to address this gap by developing a high-resolution ocean front-based model for convergence zone prediction. Out of 24 machine learning algorithms tested through K-fold cross-validation, the multilayer perceptron–random forest hybrid demonstrated the highest accuracy, showing its superiority in predicting the convergence zone within a complex ocean front environment. The research findings emphasized the substantial impact of ocean fronts on the convergence zone’s location concerning the sound source. Specifically, they highlighted that in relatively cold (or warm) water, the intensity of the ocean front significantly influences the proximity (or distance) of the convergence zone to the sound source. Furthermore, among the input features, the turning depth emerged as a crucial determinant, contributing more than 25% to the model’s effectiveness in predicting the convergence zone’s distance. The model achieved an accuracy of 82.43% in predicting the convergence zone’s distance with an error of less than 1 km. Additionally, it attained a 77.1% accuracy in predicting the convergence zone’s width within a similar error range. Notably, this prediction model exhibits strong performance and generalizability, capable of discerning evolving trends in new datasets when cross-validated using in situ observation data and information from diverse sea areas.

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Section: Comparison Of Three-dimensional Prediction Resultsmentioning

confidence: 82%

Machine learning–based feature prediction of convergence zones in ocean front environments

Xu,

Zhang,

Wang

2024

Front. Mar. Sci.

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“…The investigated relationship between eddies and SST gradients focuses only on the ocean surface due to the limitation of remotely sensed data (Ritchie et al, 2003). The vertical structure of eddies can reach a depth of 160 meters (Sun et al, 2019), and the depth of the front can reach more than 300 m (Wang et al, 2020). Thus, the entire structure of eddies and fronts is not fully incorporated in the current study.…”

Section: Discussionmentioning

confidence: 99%

“…Regarding the SST gradient, which is used as a proxy for fronts (Xi et al, 2022), few studies have investigated the influence of eddies on modulating the SST gradient. For instance, Wang et al (2020) revealed that eddies in the KE region can carry the front as they move westward and that the front gradually weakens. The eddies can expand the region with a large SST gradient in the preexisting frontal zone (Yuan and Castelao, 2017).…”

Section: Introductionmentioning

confidence: 99%

Mesoscale eddies induce variability in the sea surface temperature gradient in the Kuroshio Extension

Tang

et al. 2022

Front. Mar. Sci.

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The Kuroshio Extension (KE) region is one of the most energetic regions in the global ocean where prominent mesoscale dynamics persistently occur. The spatial distribution and temporal evolution of the sea surface temperature (SST) gradient and mesoscale eddies in the KE are investigated. The SST gradient can be applied for identifying the fronts, and the SST gradient within two times the radii of the eddies is composited to quantify the impact of eddies on frontal activities. Depressed SST gradients are identified for eddies with both polarities, but prominent spatial variance in the SST gradient reveals that a large SST gradient is located to the north of anticyclones and along the south periphery for cyclones. The eddies are further separated into two groups depending on their location relative to the main path of the KE, as the background fields to the north and south of the KE are largely different. The spatial pattern, e.g., monopole and dipole features, and temporal variation in the SST gradient are fully studied over the lifespans of eddies. The results show that most eddies can significantly weaken the internal SST gradient and induce the horizontal redistribution of the SST gradient in surrounding regions. Cyclonic eddies north of the KE elevate the fronts along the periphery of eddies. The temporal variability in the SST gradient is prominent and largely varies for each group of eddies. This study offers quantitative analyses of the spatial and temporal relationships between eddies and fronts that are important for understanding the mesoscale dynamics in the world’s oceans.

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Multisatellite observations of smaller mesoscale eddy generation in the Kuroshio Extension

Qian

Chen

2022

Acta Oceanol. Sin.

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Evolution and Structure of the Kuroshio Extension Front in Spring 2019

Cited by 6 publications

References 38 publications

Machine learning–based feature prediction of convergence zones in ocean front environments

Machine learning–based feature prediction of convergence zones in ocean front environments

Mesoscale eddies induce variability in the sea surface temperature gradient in the Kuroshio Extension

Multisatellite observations of smaller mesoscale eddy generation in the Kuroshio Extension

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