Vertical Structure-Based Classification of Oceanic Eddy Using 3-D Convolutional Neural Network

Huang, Baoxiang; Ge, Linyao; Chen, Xiaoyan; Chen, Ge

doi:10.1109/tgrs.2021.3103251

Cited by 16 publications

(14 citation statements)

References 42 publications

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“…The associative statistical neural network incorporating the observation and induction bias is designed to extract abstract features of the eddy vertical structure, thus realizing high-precision eddy identification from a 3-D perspective (Chen et al,FIGURE 8 Typical applications in physical and biological oceanography with a top-down way of "surface to interior. " Chen et al 10.3389/fmars.2022.1034188 2021c; Huang et al, 2022) as illustrated in Figure 10A Compared with the traditional mathematical statistics methods, the AI eddy identification algorithm based on Argo 3-D structure not only improves the computational efficiency by more than 10 times, but also achieves 98% accuracy of eddy identification.…”

Section: Independent Eddy Identification With Argo Profilesmentioning

confidence: 99%

Deep blue artificial intelligence for knowledge discovery of the intermediate ocean

et al. 2023

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Oceans at a depth ranging from ~100 to ~1000-m (defined as the intermediate water here), though poorly understood compared to the sea surface, is a critical layer of the Earth system where many important oceanographic processes take place. Advances in ocean observation and computer technology have allowed ocean science to enter the era of big data (to be precise, big data for the surface layer, small data for the bottom layer, and the intermediate layer sits in between) and greatly promoted our understanding of near-surface ocean phenomena. During the past few decades, however, the intermediate ocean is also undergoing profound changes because of global warming, the research and prediction of which are of intensive concern. Due to the lack of three-dimensional ocean theories and field observations, how to remotely sense the intermediate ocean from space becomes a very attractive but challenging scientific issue. With the rapid development of the next generation of information technology, artificial intelligence (AI) has built a new bridge from data science to marine science (called Deep Blue AI, DBAI), which acts as a powerful weapon to extend the paradigm of modern oceanography in the era of the metaverse. This review first introduces the basic prior knowledge of water movement in the ~100 m ocean and vertical stratification within the ~1000-m depths as well as the data resources provided by satellite remote sensing, field observation, and model reanalysis for DBAI. Then, three universal DBAI methodologies, namely, associative statistical, physically informed, and mathematically driven neural networks, are elucidated in the context of intermediate ocean remote sensing. Finally, the unique advantages and potentials of DBAI in data mining and knowledge discovery are demonstrated in a top-down way of “surface-to-interior” via several typical examples in physical and biological oceanography.

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Section: Independent Eddy Identification With Argo Profilesmentioning

confidence: 99%

Deep blue artificial intelligence for knowledge discovery of the intermediate ocean

et al. 2023

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“…The SLA data adopted in this paper is the delayed time products by Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) from a combination of T/P, Jason-1, Jason-2, Jason-3, and ENVISAT missions [35]. This study used a total of 17 years of SLA dataset from 2002 to 2018 and had a 0.25 • × 0.25 • spatial resolution and daily temporal resolution as original data [36]. However, the original data observed by the satellite altimeter cannot be directly used for eddy current identification, so it needs to be optimized through the four-step eddy current identification scheme proposed by Liu et al [37].…”

Section: A Sla Datamentioning

confidence: 99%

“…We also eliminated these eddies before classifying and identifying them to ensure the quality of the dataset. In addition, to prevent the influence of extreme weather such as rainstorms and typhoons and ships traveling on the sea surface, thereby affecting the vertical structure of the eddy profile, the near-surface depth is set to 20m [35], [36]. However, it has not yet been identified which profiles are NE.…”

Section: A Eddy Dataset Preprocessingmentioning

confidence: 99%

An Efficient Oceanic Eddy Identification Method With XBT Data Using Transformer

Zhang

Huang

Chen

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Oceanic mesoscale eddies are relatively small, shortlived circulation patterns that are approximately in geostrophic balance. Meanwhile, eddies are omnipresent and can be characterized by dynamic sea level anomalies and temperature anomalies. This makes the eddy identification mainstream with Sea Level Anomaly (SLA). Unfortunately, nearly 90% of sea level dynamic anomalies caused by oceanic eddies cannot be observed due to insufficient resolution of satellite altimeters. Combining in-situ Expendable Bathythermograph (XBT) profiles data, and sea surface temperature data calibrated by the altimeter, this paper proposes a deep neural network to identify subsurface oceanic eddies and inverse the corresponding sea surface eddy properties. First, the eddies identified by SLA are purified to match the corresponding vertical profile dataset. Then, a neural network with a self-attention mechanism is constructed by combining the eddy vertical profile structure with temporal and spatial characteristics and external features to effectively identify the eddy. Furthermore, the eddy properties including radius, amplitude, and energy are inversion with XBT profile and SST features. Finally, the experimental results show that the accuracy of eddy classification can reach 98.22%, which demonstrates that vertical profiles can be used to classify eddies effectively. Subsequent reclassification of the outside altimeter-identified eddies recaptured about 36% of the eddies. The authenticity of the newly identified eddies can be demonstrated by statistical model validation as well as validation of sea surface temperature anomaly (SSTA). These results indicate that the subsurface eddies identification can be implemented by vertical profiles with deep learning.

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“…Among various recent deep learning schemes, the CNN has shown significant success in the oceanic (de Silva et al, 2021;Huang et al, 2022;Jahanbakht et al, 2022;Patil and Iiyama, 2022) and climate applications (Reichstein et al, 2019;Baño-Medina et al, 2021;Liu et al, 2021;Cheng et al, 2022), including the ENSO forecasting (Ham et al, 2019(Ham et al, , 2021bGeng and Wang, 2021;Hu et al, 2021;Mu et al, 2021), due to its fine ability to correlate the intricate patterns within the spatio-temporal data with the target (Krizhevsky et al, 2012). Therefore, in the current study, we propose a forecasting scheme for ENSO at very long lead times (up to 3 years) based on CNN by additionally taking into account the varying parameters of CNN models for each season and training them with a customized loss function which considers extreme ENSO events separately.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for skillful long-lead ENSO forecasts

Patil

Doi²,

Jayanthi³

et al. 2023

Front. Clim.

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El Niño-Southern Oscillation (ENSO) is one of the fundamental drivers of the Earth's climate variability. Thus, its skillful prediction at least a few months to years ahead is of utmost importance to society. Using both dynamical and statistical methods, several studies reported skillful ENSO predictions at various lead times. Predictions with long lead times, on the other hand, remain difficult. In this study, we propose a convolutional neural network (CNN)-based statistical ENSO prediction system with heterogeneous CNN parameters for each season with a modified loss function to predict ENSO at least 18–24 months ahead. The developed prediction system indicates that the CNN model is highly skillful in predicting ENSO at long lead times of 18–24 months with high skills in predicting extreme ENSO events compared with the Scale Interaction Experiment-Frontier ver. 2 (SINTEX-F2) dynamical system and several other statistical prediction systems. The analysis indicates that the CNN model can overcome the spring barrier, a major hindrance to dynamical prediction systems, in predicting ENSO at long lead times. The improvement in the prediction skill can partly be attributed to the heterogeneous parameters of seasonal CNN models used in this study and also to the use of a modified loss function in the CNN model. In this study, we also attempted to identify various precursors to ENSO events using CNN heatmap analysis.

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Vertical Structure-Based Classification of Oceanic Eddy Using 3-D Convolutional Neural Network

Cited by 16 publications

References 42 publications

Deep blue artificial intelligence for knowledge discovery of the intermediate ocean

Deep blue artificial intelligence for knowledge discovery of the intermediate ocean

An Efficient Oceanic Eddy Identification Method With XBT Data Using Transformer

Deep learning for skillful long-lead ENSO forecasts

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