Characteristics of mesoscale eddies and their evolution in the north Indian ocean

Shankar Manche, Shiva; Nayak, Rabindra K.; Sikhakolli, Rajesh; Bothale, Rajashree V.; Chauhan, Prakash

doi:10.1016/j.pocean.2024.103213

Cited by 1 publication

(1 citation statement)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Features are extracted by using convolutional kernels to local regions of input data, and the spatial dimensions and quantity of feature maps are gradually refined through stacked layers of convolutional and pooling operations. The residual network is introduced to maintain smooth gradient flow and enhance generalization capabilities by incorporating skip connections between network layers (Shankar Manche et al, 2024). This structure effectively mitigates gradient dispersion and explosion during backpropagation optimization.…”

Section: The Residual Modulementioning

confidence: 99%

Subsurface temperature estimation of mesoscale eddies in the Northwest Pacific Ocean from satellite observations using a residual muti-channel attention convolution network

Liu,

Zhang,

Zhang

et al. 2024

Front. Mar. Sci.

View full text Add to dashboard Cite

The mesoscale eddies are prevalent oceanic circulation phenomena, exerting significant influence on various aspects of the marine environment including energy transfer, material transport and ecosystem dynamics in the Northwest Pacific Ocean. However, due to sparse vertical observational data, the understanding of the three-dimensional temperature structure of individual cases of mesoscale eddies remains limited. In recent years, utilizing surface remote sensing observations to estimate subsurface temperature anomaly has been crucial for comprehending the intricate multi-dimensional dynamic processes in the ocean. Consequently, this paper proposes an eddy residual multi-channel attention convolution network (ERCACN) with the adaptive threshold and designs the combination of various surface features to estimate the eddy subsurface temperature anomaly (ESTA). By integrating results with climatic temperature, thermal structures containing 46 levels at depths up to 1000 m could be obtained, achieving excellent daily temporal resolution and 0.25° spatial resolution. Validation using independent Argo profiles from 2016 to 2017 reveals that the combination of multiple surface variables outperforms univariate methods, and the ERCACN model demonstrates superior performance compared to other approaches. Overall, with an 8% error deemed acceptable, the ERCACN model achieves a precision of 88.08% in estimating ESTA. This method provides a novel perspective for other essential oceanic variables, contributing to a better perception of the global climate system.

show abstract

Section: The Residual Modulementioning

confidence: 99%