Global Oceanic Eddy Identification: A Deep Learning Method From Argo Profiles and Altimetry Data

Abstract

The inadequate spatial resolution of altimeter results in low identification efficiency of oceanic eddies, especially for small-scale eddies. It is well known that eddies can not only induce sea surface signal but more importantly have typical vertical structure characteristics. However, although the vertical structure characteristics are usually used for statistical analysis, they are seldom considered in the process of eddy recognition. This study is devoted to identifying eddies from the perspective of their vertical signal derived from the 18-year Argo data. Due to the irregular and noisy profile pattern, the direct identification of eddy core from Argo profile is deemed to be a challenge. With the popularity of artificial intelligence, a new hybrid method that combines the advantages of convolutional neural network (CNN) with extreme gradient boosting (XGBoost) is proposed to extract the representative vertical feature and identify eddy from a profile. First, CNN is employed as a feature extractor to automatically obtain vertical features from the input profile at the bottom of the network. Second, the obtained high-dimensional feature vectors are inputted into the XGBoost model, combined with other profile features for classifying profiles that are outside altimeter-identified eddies (Alt eddy). Finally, extensive experiments are implemented to demonstrate the efficiency of the proposed method. The results show that the classification accuracy of CNN-XGBoost model can reach 98%, and about 36% eddies are recaptured. These eddies, dubbed CNN-XGB eddies, are benchmarked against Alt eddies for the vertical structure and geographical distribution, demonstrating a similar or even stronger vertical signal and a prominent eddy belt in the tropical ocean. Within the proposed theory framework, there are various potentials to obtain a better outlook for eddy identification and in situ float observations.

An Efficient Oceanic Eddy Identification Method With XBT Data Using Transformer

“…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].…”

“…First, additional data filtering shall be conducted for the profiles with the first measurement less than 10m and the last measurement greater than 700m, and at least 30 effective data points within the depth range of 0m to 700m. The aforementioned high-quality profiles are processed by linear interpolation with an interval of 1m in the global ocean to obtain the final data [35].…”

A Deep Learning Model to Recognize and Quantitatively Analyze Cold Seep Substrates and the Dominant Associated Species

“…With the development of deep learning, an increasing number of target detection and recognition method have been applied to marine research. The CNN is an efficient deep learning model that can be used to extract profile feature information as it can reduce the network structure complexity as well as the number of parameters through local receptive fields, weight sharing, and pooling operations, while also actively extracting highdimensional features from big data (Chen et al, 2021). CNN image processing was customized by Zuazo et al (2020) to extract biological information of the bubblegum coral Paragorgia arborea from times series.…”

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

“…Ocean mesoscale eddies, the rotating vortices with typical horizontal scales of tens to hundreds km and timescales on the order of weeks to months, are ubiquitous in world ocean. They induce significant transport of water mass, heat, salt, dissolved CO 2 , and other important oceanic tracers, which may have profound climatological impact (Bryden and Brady, 1989;Martin and Richards, 2001;McGillicuddy et al, 2007;Chelton et al, 2011a;Chen et al, 2011Chen et al, , 2021Sarangi, 2012;Frenger, 2013;Dong et al, 2014;Zhang Y. et al, 2014;Zhang Z. G. et al, 2014Zhang Z. G. et al, , 2017Zhang et al, 2016;Ma et al, 2019;Yang et al, 2019;Tian et al, 2020;Zhang and Qiu, 2020;Martínez-Moreno et al, 2021;Thoppil et al, 2021). The mesoscale eddies account for 90% oceanic kinetic energy and dominate the upper ocean flow field (Pascual et al, 2006;Wunsch, 2007;Martínez-Moreno et al, 2021).…”

“…That could provide opportunities to potentially identify the underlying patterns and mechanism associated with mesoscale eddies, as well as the related physical and biogeochemical impact. It could also help to guide the deployment of Array for Real-time Geostrophic Oceanography (ARGO), ocean glider, or Autonomous Underwater Vehicles (AUV), and when the observational missions are involved mesoscale eddies (Chaigneau et al, 2011;Zhang et al, 2013;Dong et al, 2017;Gourdeau et al, 2017;Zhang Z. W. et al, 2018;Shu et al, 2019;Chen et al, 2021).…”